diff --git a/modules/ROOT/examples/Dropped text.txt b/modules/ROOT/examples/Dropped text.txt deleted file mode 100644 index 8b137891..00000000 --- a/modules/ROOT/examples/Dropped text.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/modules/ROOT/examples/browser-guide.txt b/modules/ROOT/examples/browser-guide.txt deleted file mode 100644 index e69de29b..00000000 diff --git a/modules/ROOT/examples/createConstraints.cypher b/modules/ROOT/examples/createConstraints.cypher deleted file mode 100644 index bb60c64e..00000000 --- a/modules/ROOT/examples/createConstraints.cypher +++ /dev/null @@ -1,6 +0,0 @@ -CREATE INDEX on :Ticket(id); -CREATE INDEX on :Client(id); -CREATE INDEX on :Product(description); -CREATE INDEX on :TicketItem(netAmount); -CREATE INDEX on :TicketItem(units); -CREATE INDEX on :TicketItem(product); diff --git a/modules/ROOT/examples/export_csv.sql b/modules/ROOT/examples/export_csv.sql deleted file mode 100644 index 37938310..00000000 --- a/modules/ROOT/examples/export_csv.sql +++ /dev/null @@ -1,8 +0,0 @@ -COPY (SELECT * FROM customers) TO '/tmp/customers.csv' WITH CSV header; -COPY (SELECT * FROM suppliers) TO '/tmp/suppliers.csv' WITH CSV header; -COPY (SELECT * FROM products) TO '/tmp/products.csv' WITH CSV header; -COPY (SELECT * FROM employees) TO '/tmp/employees.csv' WITH CSV header; -COPY (SELECT * FROM categories) TO '/tmp/categories.csv' WITH CSV header; - -COPY (SELECT * FROM orders - LEFT OUTER JOIN order_details ON order_details.OrderID = orders.OrderID) TO '/tmp/orders.csv' WITH CSV header; diff --git a/modules/ROOT/examples/fraud-page.rb b/modules/ROOT/examples/fraud-page.rb deleted file mode 100644 index bb1c6f3d..00000000 --- a/modules/ROOT/examples/fraud-page.rb +++ /dev/null @@ -1,74 +0,0 @@ - -# gem install neo4j-core -require 'neo4j-core' -session = Neo4j::Session.open(:server_db, 'http://localhost:7474', basic_auth: {username: 'neo4j', password: ''}) - -# Insert data -insert_query = """ -CREATE (hank:Person {name:'Hank'}), -(abby:Person {name:'Abby'}), -(max:Person {name:'Max'}), -(sophie:Person {name: 'Sophie'}), -(jane:Person {name: 'Jane'}), -(bill:Person {name: 'Bill'}), -(ssn993632634:SSN {number: 993632634, name: 'SSN 993632634'}), -(ssn123456789:SSN {number: 123456789, name: 'SSN 123456789'}), -(ssn523252364:SSN {number: 523252364, name: 'SSN 523252364'}), -(chase:Account {bank: 'Chase', number: 1523, name: 'Chase 1523'}), -(bofa:Account {bank: 'Bank of America', number: 4634, name: 'BofA 4634'}), -(cayman:Account {bank: 'Cayman', number: 863, name: 'Cayman 863'}), -(bill)-[:HAS_SSN]->(ssn523252364), -(bill)-[:HAS_ACCOUNT]->(bofa), -(jane)-[:HAS_SSN]->(ssn123456789), -(jane)-[:HAS_ACCOUNT]->(chase), -(hank)-[:HAS_ACCOUNT]->(cayman), -(abby)-[:HAS_ACCOUNT]->(cayman), -(abby)-[:HAS_SSN]->(ssn993632634), -(sophie)-[:HAS_SSN]->(ssn993632634), -(max)-[:HAS_SSN]->(ssn993632634) -""" -session.query(insert_query) - -# Transitive Closure - -transitive_query = """ -MATCH (n:Person)-[*]-(o) -WHERE n.name = $name -RETURN DISTINCT o.name AS other -""" - -response = session.query(transitive_query, name: 'Hank') -puts "Suspicious entities: " -response.each do |row| - puts row[:other] -end -puts "---------------------" -# Investigation Targeting - -targeting_query = """ -MATCH (n:Person)-[*]-(o) -WITH n, count(DISTINCT o) AS size -WHERE size > 2 -RETURN n.name AS target -""" - -response = session.query(targeting_query) -puts "Investigation targets: " -response.each do |row| - puts row[:target] -end -puts "----------------------" - -# Fast Insights - -connecting_paths_query = """ -MATCH (ssn:SSN)<-[:HAS_SSN]-(:Person)-[:HAS_ACCOUNT]->(acct:Account) -WHERE ssn.number = 993632634 -RETURN acct.bank + ' ' + str(acct.number) AS account -""" - -response = session.query(connecting_paths_query, name1: 'Joe', name2: 'Billy') -puts "Accounts: " -response.each do |row| - puts row[:account] -end \ No newline at end of file diff --git a/modules/ROOT/examples/fraud.frag0.txt b/modules/ROOT/examples/fraud.frag0.txt deleted file mode 100644 index 53bada63..00000000 --- a/modules/ROOT/examples/fraud.frag0.txt +++ /dev/null @@ -1,6 +0,0 @@ -MATCH - (n:Person)-[*]-(o) -WHERE - n.name = "Hank" -RETURN - o diff --git a/modules/ROOT/examples/fraud.frag1.txt b/modules/ROOT/examples/fraud.frag1.txt deleted file mode 100644 index 594c7b39..00000000 --- a/modules/ROOT/examples/fraud.frag1.txt +++ /dev/null @@ -1,9 +0,0 @@ -MATCH - (n:Person)-[*]-(o) -WITH - n, - count(DISTINCT o) AS size -WHERE - size > 2 -RETURN - n diff --git a/modules/ROOT/examples/fraud.frag2.txt b/modules/ROOT/examples/fraud.frag2.txt deleted file mode 100644 index 853cda4f..00000000 --- a/modules/ROOT/examples/fraud.frag2.txt +++ /dev/null @@ -1,7 +0,0 @@ -MATCH - (ssn:SSN)<-[:HAS_SSN]-(:Person)- - [:HAS_ACCOUNT]->(acct:Account) -WHERE - ssn.number = 993632634 -RETURN - acct diff --git a/modules/ROOT/examples/html_transformer.rb b/modules/ROOT/examples/html_transformer.rb deleted file mode 100644 index 2d162aa9..00000000 --- a/modules/ROOT/examples/html_transformer.rb +++ /dev/null @@ -1,16 +0,0 @@ -module HtmlTransformer - def self.transform(lines) - lines = lines.each_line.to_a if lines.is_a?(String) - - lines.map do |line| - line - .gsub(%r{/developer/+developer}, '/developer') - .gsub(%r{/developer/+(docs|graph-academy|sandbox|graphacademy|editions|download|use-cases|online-course|online-training|blog|books|hardware-sizing|support|learning-neo4j-book)}, '/\1') -# .gsub(%r{href="/developer/}, 'href="/developer/') -# .gsub(%r{href="(?:/developer/)? # base of the url -# (?:(?:\.\.|[a-zA-Z0-9_-]+)/)* # the classification that we want to bin -# ([^#:]+?)"}x,'href="/developer/\1"') - end.join("") - end -end - diff --git a/modules/ROOT/examples/import-movies.rb b/modules/ROOT/examples/import-movies.rb deleted file mode 100644 index c1b10a86..00000000 --- a/modules/ROOT/examples/import-movies.rb +++ /dev/null @@ -1,65 +0,0 @@ -require 'rubygems' -require 'rest-client' -require 'json' - -URL = "http://api.themoviedb.org/3" -# get the api key at https://www.themoviedb.org/faq/api and set it as environment variable -KEY = ENV['THE_MOVIE_DB_KEY'] - - -puts setup -puts "BEGIN" -[19995 , 194, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 13, 20526, 11, 1893, 1892, - 1894, 168, 193, 200, 157, 152, 201, 154, 12155, 58, 285, 118, 22, 392, 5255, 568, 9800, 497, 101, 120, 121, 122].each do |id| - puts create_movie(movie(id)) -end -puts "COMMIT" - -def get(type,id) - url = "#{URL}/#{type}/#{id}?api_key=#{KEY}&append_to_response=casts" - res = RestClient.get url - File.open("json/#{id}.json", 'w') {|f| f.write(res.to_str) } - JSON.parse(res.to_str) -end - -def person(id) - get("person",id) -end - -def clean(name) - name.to_s.gsub(/['"]/,"") -end -def movie(id) - parse_movie(get("movie",id),id) -end - -def parse_movie(res,id) -# puts res.inspect - movie = [:tagline,:released,:genres].reduce({:movie_id => id}) { |r, prop| r[prop] = res[prop.to_s] if res[prop.to_s] && res[prop.to_s].length>0; r } - movie[:title] = res["title"] - movie[:genres] = movie[:genres].collect { |g| g["name"] } - movie[:actors] = res["casts"]["cast"].collect { |g| { :id => g["id"], :name => clean(g["name"]) , :role => clean(g["character"]) }} - movie[:directors] = res["casts"]["crew"].find_all {|a| a["job"]=="Director" } .collect { |g| { :id => g["id"], :name => clean(g["name"]) }} - movie -end - -def setup - ["CREATE INDEX ON :Movie(title)", - "CREATE INDEX ON :Movie(movie_id)", - "CREATE INDEX ON :Person(id)", - "CREATE INDEX ON :Person(name)", - "CREATE INDEX ON :Genre(genre)",""].join(";\n") -end - -# node auto-index for movie_id, id, name, title, genre, type -def create_movie(movie) - props=[:movie_id, :title,:tagline,:released].find_all{ |p| movie[p] }.collect { |p| "#{p}:'#{clean(movie[p])}'" }.join(', ') - actors = movie[:actors].collect { |a| "CREATE UNIQUE (movie)<-[:ACTS_IN {role:'#{clean(a[:role])}'}]-(:Person:Actor {id:'#{a[:id]}', name: '#{a[:name]}'})-[:PERSON]->(people) "}.join(" \n") + "\n" - directors = movie[:directors].collect { |a| "CREATE UNIQUE (movie)<-[:DIRECTED]-(:Person:Director {id:'#{a[:id]}', name:'#{a[:name]}'})-[:PERSON]->(people) "}.join(" \n") + "\n" - genres = movie[:genres].collect { |g| "CREATE UNIQUE (movie)-[:GENRE]->(:Genre {genre:'#{g}'})-[:GENRE]->(genres) "}.join(" \n") + "\n" - " MERGE (genres:Genres) - MERGE (movies:Movies) - MERGE (people:People) - CREATE (movie:Movie {#{props}}) - " + genres + actors + directors + ";" -end diff --git a/modules/ROOT/examples/importTickets.cypher b/modules/ROOT/examples/importTickets.cypher deleted file mode 100644 index a35d4b45..00000000 --- a/modules/ROOT/examples/importTickets.cypher +++ /dev/null @@ -1,30 +0,0 @@ -// tag::params[] -:param params => ({ url: {url}, mall: {mall}}); -// end::params[] - -// tag::query[] - -CALL apoc.periodic.iterate( - "CALL apoc.load.json($url) - YIELD value - WHERE value.mall = $mall - RETURN value - LIMIT 20000", - "CREATE (t:Ticket {id: value._id, datetime: datetime(value.date)}) - MERGE (c:Client {id: value.client}) - CREATE (c)-[:PURCHASED]->(t) - WITH value, t - UNWIND value.items as item - CREATE (t)-[:HAS_TICKETITEM]->(ti:TicketItem { - product: item.desc, - netAmount: item.net_am, - units: item.n_unit - }) - MERGE (p:Product {description: item.desc}) - CREATE (ti)-[:FOR_PRODUCT]->(p)", - { batchSize: 10000, - iterateList: true, - parallel: false, - params: $params } -); -// end::query[] diff --git a/modules/ROOT/examples/import_csv.cypher b/modules/ROOT/examples/import_csv.cypher deleted file mode 100644 index 0340bd0e..00000000 --- a/modules/ROOT/examples/import_csv.cypher +++ /dev/null @@ -1,77 +0,0 @@ -// tag::nodes[] -// Create orders -LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row -MERGE (order:Order {orderID: row.OrderID}) - ON CREATE SET order.shipName = row.ShipName; - -// Create products -LOAD CSV WITH HEADERS FROM 'file:///products.csv' AS row -MERGE (product:Product {productID: row.ProductID}) - ON CREATE SET product.productName = row.ProductName, product.unitPrice = toFloat(row.UnitPrice); - -// Create suppliers -LOAD CSV WITH HEADERS FROM 'file:///suppliers.csv' AS row -MERGE (supplier:Supplier {supplierID: row.SupplierID}) - ON CREATE SET supplier.companyName = row.CompanyName; - -// Create employees -LOAD CSV WITH HEADERS FROM 'file:///employees.csv' AS row -MERGE (e:Employee {employeeID:row.EmployeeID}) - ON CREATE SET e.firstName = row.FirstName, e.lastName = row.LastName, e.title = row.Title; - -// Create categories -LOAD CSV WITH HEADERS FROM 'file:///categories.csv' AS row -MERGE (c:Category {categoryID: row.CategoryID}) - ON CREATE SET c.categoryName = row.CategoryName, c.description = row.Description; -// end::nodes[] - -// tag::indexes[] -CREATE INDEX product_id FOR (p:Product) ON (p.productID); -CREATE INDEX product_name FOR (p:Product) ON (p.productName); -CREATE INDEX supplier_id FOR (s:Supplier) ON (s.supplierID); -CREATE INDEX employee_id FOR (e:Employee) ON (e.employeeID); -CREATE INDEX category_id FOR (c:Category) ON (c.categoryID); -// end::indexes[] - -// tag::constraints[] -CREATE CONSTRAINT order_id ON (o:Order) ASSERT o.orderID IS UNIQUE; -// end::constraints[] - -schema await - -// tag::rels_orders[] -// Create relationships between orders and products -LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row -MATCH (order:Order {orderID: row.OrderID}) -MATCH (product:Product {productID: row.ProductID}) -MERGE (order)-[op:CONTAINS]->(product) - ON CREATE SET op.unitPrice = toFloat(row.UnitPrice), op.quantity = toFloat(row.Quantity); - -// Create relationships between orders and employees -LOAD CSV WITH HEADERS FROM "file:///orders.csv" AS row -MATCH (order:Order {orderID: row.OrderID}) -MATCH (employee:Employee {employeeID: row.EmployeeID}) -MERGE (employee)-[:SOLD]->(order); -// end::rels_orders[] - -// tag::rels_products[] -// Create relationships between products and suppliers -LOAD CSV WITH HEADERS FROM "file:///products.csv" AS row -MATCH (product:Product {productID: row.ProductID}) -MATCH (supplier:Supplier {supplierID: row.SupplierID}) -MERGE (supplier)-[:SUPPLIES]->(product); - -// Create relationships between products and categories -LOAD CSV WITH HEADERS FROM "file:///products.csv" AS row -MATCH (product:Product {productID: row.ProductID}) -MATCH (category:Category {categoryID: row.CategoryID}) -MERGE (product)-[:PART_OF]->(category); -// end::rels_products[] - -// tag::rels_employees[] -// Create relationships between employees (reporting hierarchy) -LOAD CSV WITH HEADERS FROM "file:///employees.csv" AS row -MATCH (employee:Employee {employeeID: row.EmployeeID}) -MATCH (manager:Employee {employeeID: row.ReportsTo}) -MERGE (employee)-[:REPORTS_TO]->(manager); -// end::rels_employees[] \ No newline at end of file diff --git a/modules/ROOT/examples/movie-database-queries.txt b/modules/ROOT/examples/movie-database-queries.txt deleted file mode 100644 index f40746e3..00000000 --- a/modules/ROOT/examples/movie-database-queries.txt +++ /dev/null @@ -1,120 +0,0 @@ -.Actors who played in some movie -[source,cypher] ----- -MATCH (m:Movie {title: 'Forrest Gump'})<-[:ACTS_IN]-(a:Actor) -RETURN a.name, a.birthplace ----- -//table - -.Find the most prolific actors -[source,cypher] ----- -MATCH (a:Actor)-[:ACTS_IN]->(m:Movie) -RETURN a, count(*) -ORDER BY count(*) DESC LIMIT 10; ----- -//table - -.Find actors who have been in less than 3 movies -[source,cypher] ----- -MATCH (a:Actor)-[:ACTS_IN]->(m:Movie) -WITH a, count(m) AS movie_count -WHERE movie_count < 3 -RETURN a, movie_count -ORDER BY movie_count DESC LIMIT 5; ----- -//table - -.Find the actors with 20+ movies, and the movies in which they acted -[source,cypher] ----- -MATCH (a:Actor)-[:ACTS_IN]->(m:Movie) -WITH a, collect(m.title) AS movies -WHERE size(movies) >= 20 -RETURN a, movies -ORDER BY size(movies) DESC LIMIT 10; ----- -//table - -.Find prolific actors (10+) who have directed at least two films, count films acted in and list films directed -[source,cypher] ----- -MATCH (a:Actor)-[:ACTS_IN]->(m:Movie) -WITH a, count(m) AS acted -WHERE acted >= 10 -WITH a, acted -MATCH (a:Director)-[:DIRECTED]->(m:Movie) -WITH a, acted, collect(m.title) AS directed -WHERE size(directed) >= 2 -RETURN a.name, acted, directed -ORDER BY size(directed) DESC, acted DESC; ----- -//table - -.Rewritten to filter both :Actor and :Director labels up front -[source,cypher] ----- -MATCH (a:Actor:Director)-[:ACTS_IN]->(m:Movie) -WITH a, count(1) AS acted -WHERE acted >= 10 -WITH a, acted -MATCH (a:Actor:Director)-[:DIRECTED]->(m:Movie) -WITH a, acted, collect(m.title) AS directed -WHERE size(directed) >= 2 -RETURN a.name, acted, directed -ORDER BY size(directed) DESC, acted DESC; ----- -//table - -.Using the lowest cardinality label, :Director -[source,cypher] ----- -MATCH (a:Director)-[:ACTS_IN]->(m) -WITH a, count(m) AS acted -WHERE acted >= 10 -WITH a, acted -MATCH (a)-[:DIRECTED]->(m) -WITH a, acted, collect(m.title) AS directed -WHERE size(directed) >= 2 -RETURN a.name, acted, directed -ORDER BY size(directed) DESC, acted DESC; ----- - -.User-Ratings -[source,cypher] ----- -MATCH (u:User {login: 'Michael'})-[r:RATED]->(m:Movie) -WHERE r.stars > 3 -RETURN m.title, r.stars, r.comment ----- -//table - -.Recommendations including counts, grouping and sorting -[source,cypher] ----- -MATCH (u:User {login: 'Michael'})-[:FRIEND]-()-[r:RATED]->(m:Movie) -RETURN m.title, avg(r.stars), count(*) -ORDER BY AVG(r.stars) DESC, count(*) DESC ----- -//table - -.Ratings by like-minded people -[source,cypher] ----- -MATCH (u:User)-[r:RATED]->(m:Movie)<-[r2:RATED]-(likeminded), - (u)-[:FRIEND]-(friend) -WHERE r.stars > 3 AND r2.stars >= 3 -RETURN likeminded, count(*) -ORDER BY count(*) desc LIMIT 10; ----- -//table - -.Mutual Friend recommendations -[source,cypher] ----- -MATCH (u:User {login: 'Michael'})-[:FRIEND]-(f:Person)-[r:RATED]->(m:Movie) -WHERE r.stars > 3 -RETURN f.name, m.title, r.stars, r.comment ----- -//table diff --git a/modules/ROOT/examples/network-page.rb b/modules/ROOT/examples/network-page.rb deleted file mode 100644 index 66179a06..00000000 --- a/modules/ROOT/examples/network-page.rb +++ /dev/null @@ -1,64 +0,0 @@ - -# gem install neo4j-core -require 'neo4j-core' -session = Neo4j::Session.open(:server_db, 'http://localhost:7474', basic_auth: {username: 'neo4j', password: ''}) - -# Insert data - -insert_query = """ -UNWIND {pairs} AS pair -MERGE (s1:Service {name: pair[0]}) -MERGE (s2:Service {name: pair[1]}) -MERGE (s1)-[:DEPENDS_ON]->(s2); -""" - -data = [["CRM", "Database VM"], ["Database VM", "Server 2"], - ["Server 2", "SAN"], ["Server 1", "SAN"], ["Webserver VM", "Server 1"], - ["Public Website", "Webserver VM"], ["Public Website", "Webserver VM"]] - -session.query(insert_query, pairs: data) - -# Impact Analysis - -impact_query = """ -MATCH (n:Service)<-[:DEPENDS_ON*]-(dependent:Service) -WHERE n.name = {service_name} -RETURN dependent.name AS dependent_service -""" - -response = session.query(impact_query, service_name: 'Server 1') -puts "Dependent services: " -response.each do |row| - puts row[:dependent_service] -end -puts "--------------------" - -# Dependency Analysis - -dependency_query = """ -MATCH (n:Service)-[:DEPENDS_ON*]->(downstream:Service) -WHERE n.name = {service_name} -RETURN downstream.name AS downstream_service -""" - -response = session.query(dependency_query, service_name: 'Public Website') -puts "Downstream services: " -response.each do |row| - puts row[:downstream_service] -end -puts "---------------------" - -# Statistics - -stats_query = """ -MATCH (n:Service)<-[:DEPENDS_ON*]-(dependent:Service) -RETURN n.name AS service, count(DISTINCT dependent) AS dependents -ORDER BY dependents DESC -LIMIT 1 -""" - -puts "Service with most dependent services: " -response = session.query(stats_query) -response.each do |row| - puts row[:service] + " with " + row[:dependents].to_s + " dependencies" -end \ No newline at end of file diff --git a/modules/ROOT/examples/network.frag0.txt b/modules/ROOT/examples/network.frag0.txt deleted file mode 100644 index 45f0794a..00000000 --- a/modules/ROOT/examples/network.frag0.txt +++ /dev/null @@ -1,6 +0,0 @@ -MATCH - (n:Service)<-[:DEPENDS_ON*]-(dependent:Service) -WHERE - n.name = "Server 1" -RETURN - dependent diff --git a/modules/ROOT/examples/network.frag1.txt b/modules/ROOT/examples/network.frag1.txt deleted file mode 100644 index 99d9b654..00000000 --- a/modules/ROOT/examples/network.frag1.txt +++ /dev/null @@ -1,6 +0,0 @@ -MATCH - (n:Service)-[:DEPENDS_ON*]->(downstream:Service) -WHERE - n.name = "Public Website" -RETURN - downstream diff --git a/modules/ROOT/examples/network.frag2.txt b/modules/ROOT/examples/network.frag2.txt deleted file mode 100644 index 4a9f08ad..00000000 --- a/modules/ROOT/examples/network.frag2.txt +++ /dev/null @@ -1,8 +0,0 @@ -MATCH - (svc:Service)<-[:DEPENDS_ON*]-(dependent:Service) -RETURN - svc, - count(DISTINCT dependent) AS dependents -ORDER BY - dependents DESC -LIMIT 1 diff --git a/modules/ROOT/examples/northwind.postgre.sql b/modules/ROOT/examples/northwind.postgre.sql deleted file mode 100644 index fcd6bc2c..00000000 --- a/modules/ROOT/examples/northwind.postgre.sql +++ /dev/null @@ -1,3861 +0,0 @@ --- --- PostgreSQL Northwind Database v1.0 from Ramiro Estigarribia Canese --- you may contact him at email ramiro.estigarribia@rieder.com.py --- - - -SET statement_timeout = 0; -SET client_encoding = 'UTF8'; -SET standard_conforming_strings = on; -SET check_function_bodies = false; -SET client_min_messages = warning; - --- --- Name: plpgsql; Type: EXTENSION; Schema: -; Owner: --- - -CREATE EXTENSION IF NOT EXISTS plpgsql WITH SCHEMA pg_catalog; - - --- --- Name: EXTENSION plpgsql; Type: COMMENT; Schema: -; Owner: --- - -COMMENT ON EXTENSION plpgsql IS 'PL/pgSQL procedural language'; - - -SET search_path = public, pg_catalog; - -SET default_tablespace = ''; - -SET default_with_oids = false; - --- --- Name: categories; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE categories ( - CategoryID smallint NOT NULL, - CategoryName character varying(15) NOT NULL, - Description text, - Picture bytea -); - - -ALTER TABLE public.categories OWNER TO postgres; - --- --- Name: customercustomerdemo; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE customercustomerdemo ( - CustomerID bpchar NOT NULL, - CustomerTypeID bpchar NOT NULL -); - - -ALTER TABLE public.customercustomerdemo OWNER TO postgres; - --- --- Name: customerdemographics; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE customerdemographics ( - CustomerTypeID bpchar NOT NULL, - CustomerDesc text -); - - -ALTER TABLE public.customerdemographics OWNER TO postgres; - --- --- Name: customers; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE customers ( - CustomerID bpchar NOT NULL, - CompanyName character varying(40) NOT NULL, - ContactName character varying(30), - ContactTitle character varying(30), - Address character varying(60), - City character varying(15), - Region character varying(15), - PostalCode character varying(10), - Country character varying(15), - Phone character varying(24), - Fax character varying(24) -); - - -ALTER TABLE public.customers OWNER TO postgres; - --- --- Name: employees; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE employees ( - EmployeeID smallint NOT NULL, - LastName character varying(20) NOT NULL, - FirstName character varying(10) NOT NULL, - Title character varying(30), - TitleOfCourtesy character varying(25), - BirthDate date, - HireDate date, - Address character varying(60), - City character varying(15), - Region character varying(15), - PostalCode character varying(10), - Country character varying(15), - HomePhone character varying(24), - Extension character varying(4), - Photo bytea, - Notes text, - ReportsTo smallint, - PhotoPath character varying(255) -); - - -ALTER TABLE public.employees OWNER TO postgres; - --- --- Name: employeeterritories; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE employeeterritories ( - EmployeeID smallint NOT NULL, - TerritoryID character varying(20) NOT NULL -); - - -ALTER TABLE public.employeeterritories OWNER TO postgres; - --- --- Name: order_details; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE order_details ( - OrderID smallint NOT NULL, - ProductID smallint NOT NULL, - UnitPrice real NOT NULL, - Quantity smallint NOT NULL, - Discount real NOT NULL -); - - -ALTER TABLE public.order_details OWNER TO postgres; - --- --- Name: orders; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE orders ( - OrderID smallint NOT NULL, - CustomerID bpchar, - EmployeeID smallint, - OrderDate date, - RequiredDate date, - ShippedDate date, - ShipVia smallint, - Freight real, - ShipName character varying(40), - ShipAddress character varying(60), - ShipCity character varying(15), - ShipRegion character varying(15), - ShipPostalCode character varying(10), - ShipCountry character varying(15) -); - - -ALTER TABLE public.orders OWNER TO postgres; - --- --- Name: products; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE products ( - ProductID smallint NOT NULL, - ProductName character varying(40) NOT NULL, - SupplierID smallint, - CategoryID smallint, - QuantityPerUnit character varying(20), - UnitPrice real, - UnitsInStock smallint, - UnitsOnOrder smallint, - ReorderLevel smallint, - Discontinued integer NOT NULL -); - - -ALTER TABLE public.products OWNER TO postgres; - --- --- Name: region; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE region ( - RegionID smallint NOT NULL, - RegionDescription bpchar NOT NULL -); - - -ALTER TABLE public.region OWNER TO postgres; - --- --- Name: shippers; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE shippers ( - ShipperID smallint NOT NULL, - CompanyName character varying(40) NOT NULL, - Phone character varying(24) -); - - -ALTER TABLE public.shippers OWNER TO postgres; - --- --- Name: shippers_tmp; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE shippers_tmp ( - ShipperID smallint NOT NULL, - CompanyName character varying(40) NOT NULL, - Phone character varying(24) -); - - -ALTER TABLE public.shippers_tmp OWNER TO postgres; - --- --- Name: suppliers; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE suppliers ( - SupplierID smallint NOT NULL, - CompanyName character varying(40) NOT NULL, - ContactName character varying(30), - ContactTitle character varying(30), - Address character varying(60), - City character varying(15), - Region character varying(15), - PostalCode character varying(10), - Country character varying(15), - Phone character varying(24), - Fax character varying(24), - HomePage text -); - - -ALTER TABLE public.suppliers OWNER TO postgres; - --- --- Name: territories; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE territories ( - TerritoryID character varying(20) NOT NULL, - TerritoryDescription bpchar NOT NULL, - RegionID smallint NOT NULL -); - - -ALTER TABLE public.territories OWNER TO postgres; - --- --- Name: usstates; Type: TABLE; Schema: public; Owner: postgres; Tablespace: --- - -CREATE TABLE usstates ( - StateID smallint NOT NULL, - StateName character varying(100), - StateAbbr character varying(2), - StateRegion character varying(50) -); - - -ALTER TABLE public.usstates OWNER TO postgres; - --- --- Data for Name: categories; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO categories VALUES (1, 'Beverages', 'Soft drinks, coffees, teas, beers, and ales', '\x'); -INSERT INTO categories VALUES (2, 'Condiments', 'Sweet and savory sauces, relishes, spreads, and seasonings', '\x'); -INSERT INTO categories VALUES (3, 'Confections', 'Desserts, candies, and sweet breads', '\x'); -INSERT INTO categories VALUES (4, 'Dairy Products', 'Cheeses', '\x'); -INSERT INTO categories VALUES (5, 'Grains/Cereals', 'Breads, crackers, pasta, and cereal', '\x'); -INSERT INTO categories VALUES (6, 'Meat/Poultry', 'Prepared meats', '\x'); -INSERT INTO categories VALUES (7, 'Produce', 'Dried fruit and bean curd', '\x'); -INSERT INTO categories VALUES (8, 'Seafood', 'Seaweed and fish', '\x'); - - --- --- Data for Name: customercustomerdemo; Type: TABLE DATA; Schema: public; Owner: postgres --- - - - --- --- Data for Name: customerdemographics; Type: TABLE DATA; Schema: public; Owner: postgres --- - - - --- --- Data for Name: customers; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO customers VALUES ('ALFKI', 'Alfreds Futterkiste', 'Maria Anders', 'Sales Representative', 'Obere Str. 57', 'Berlin', NULL, '12209', 'Germany', '030-0074321', '030-0076545'); -INSERT INTO customers VALUES ('ANATR', 'Ana Trujillo Emparedados y helados', 'Ana Trujillo', 'Owner', 'Avda. de la Constitución 2222', 'México D.F.', NULL, '05021', 'Mexico', '(5) 555-4729', '(5) 555-3745'); -INSERT INTO customers VALUES ('ANTON', 'Antonio Moreno Taquería', 'Antonio Moreno', 'Owner', 'Mataderos 2312', 'México D.F.', NULL, '05023', 'Mexico', '(5) 555-3932', NULL); -INSERT INTO customers VALUES ('AROUT', 'Around the Horn', 'Thomas Hardy', 'Sales Representative', '120 Hanover Sq.', 'London', NULL, 'WA1 1DP', 'UK', '(171) 555-7788', '(171) 555-6750'); -INSERT INTO customers VALUES ('BERGS', 'Berglunds snabbköp', 'Christina Berglund', 'Order Administrator', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden', '0921-12 34 65', '0921-12 34 67'); -INSERT INTO customers VALUES ('BLAUS', 'Blauer See Delikatessen', 'Hanna Moos', 'Sales Representative', 'Forsterstr. 57', 'Mannheim', NULL, '68306', 'Germany', '0621-08460', '0621-08924'); -INSERT INTO customers VALUES ('BLONP', 'Blondesddsl père et fils', 'Frédérique Citeaux', 'Marketing Manager', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France', '88.60.15.31', '88.60.15.32'); -INSERT INTO customers VALUES ('BOLID', 'Bólido Comidas preparadas', 'Martín Sommer', 'Owner', 'C/ Araquil, 67', 'Madrid', NULL, '28023', 'Spain', '(91) 555 22 82', '(91) 555 91 99'); -INSERT INTO customers VALUES ('BONAP', 'Bon app''', 'Laurence Lebihan', 'Owner', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France', '91.24.45.40', '91.24.45.41'); -INSERT INTO customers VALUES ('BOTTM', 'Bottom-Dollar Markets', 'Elizabeth Lincoln', 'Accounting Manager', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada', '(604) 555-4729', '(604) 555-3745'); -INSERT INTO customers VALUES ('BSBEV', 'B''s Beverages', 'Victoria Ashworth', 'Sales Representative', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK', '(171) 555-1212', NULL); -INSERT INTO customers VALUES ('CACTU', 'Cactus Comidas para llevar', 'Patricio Simpson', 'Sales Agent', 'Cerrito 333', 'Buenos Aires', NULL, '1010', 'Argentina', '(1) 135-5555', '(1) 135-4892'); -INSERT INTO customers VALUES ('CENTC', 'Centro comercial Moctezuma', 'Francisco Chang', 'Marketing Manager', 'Sierras de Granada 9993', 'México D.F.', NULL, '05022', 'Mexico', '(5) 555-3392', '(5) 555-7293'); -INSERT INTO customers VALUES ('CHOPS', 'Chop-suey Chinese', 'Yang Wang', 'Owner', 'Hauptstr. 29', 'Bern', NULL, '3012', 'Switzerland', '0452-076545', NULL); -INSERT INTO customers VALUES ('COMMI', 'Comércio Mineiro', 'Pedro Afonso', 'Sales Associate', 'Av. dos Lusíadas, 23', 'Sao Paulo', 'SP', '05432-043', 'Brazil', '(11) 555-7647', NULL); -INSERT INTO customers VALUES ('CONSH', 'Consolidated Holdings', 'Elizabeth Brown', 'Sales Representative', 'Berkeley Gardens 12 Brewery', 'London', NULL, 'WX1 6LT', 'UK', '(171) 555-2282', '(171) 555-9199'); -INSERT INTO customers VALUES ('DRACD', 'Drachenblut Delikatessen', 'Sven Ottlieb', 'Order Administrator', 'Walserweg 21', 'Aachen', NULL, '52066', 'Germany', '0241-039123', '0241-059428'); -INSERT INTO customers VALUES ('DUMON', 'Du monde entier', 'Janine Labrune', 'Owner', '67, rue des Cinquante Otages', 'Nantes', NULL, '44000', 'France', '40.67.88.88', '40.67.89.89'); -INSERT INTO customers VALUES ('EASTC', 'Eastern Connection', 'Ann Devon', 'Sales Agent', '35 King George', 'London', NULL, 'WX3 6FW', 'UK', '(171) 555-0297', '(171) 555-3373'); -INSERT INTO customers VALUES ('ERNSH', 'Ernst Handel', 'Roland Mendel', 'Sales Manager', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria', '7675-3425', '7675-3426'); -INSERT INTO customers VALUES ('FAMIA', 'Familia Arquibaldo', 'Aria Cruz', 'Marketing Assistant', 'Rua Orós, 92', 'Sao Paulo', 'SP', '05442-030', 'Brazil', '(11) 555-9857', NULL); -INSERT INTO customers VALUES ('FISSA', 'FISSA Fabrica Inter. Salchichas S.A.', 'Diego Roel', 'Accounting Manager', 'C/ Moralzarzal, 86', 'Madrid', NULL, '28034', 'Spain', '(91) 555 94 44', '(91) 555 55 93'); -INSERT INTO customers VALUES ('FOLIG', 'Folies gourmandes', 'Martine Rancé', 'Assistant Sales Agent', '184, chaussée de Tournai', 'Lille', NULL, '59000', 'France', '20.16.10.16', '20.16.10.17'); -INSERT INTO customers VALUES ('FOLKO', 'Folk och fä HB', 'Maria Larsson', 'Owner', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden', '0695-34 67 21', NULL); -INSERT INTO customers VALUES ('FRANK', 'Frankenversand', 'Peter Franken', 'Marketing Manager', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany', '089-0877310', '089-0877451'); -INSERT INTO customers VALUES ('FRANR', 'France restauration', 'Carine Schmitt', 'Marketing Manager', '54, rue Royale', 'Nantes', NULL, '44000', 'France', '40.32.21.21', '40.32.21.20'); -INSERT INTO customers VALUES ('FRANS', 'Franchi S.p.A.', 'Paolo Accorti', 'Sales Representative', 'Via Monte Bianco 34', 'Torino', NULL, '10100', 'Italy', '011-4988260', '011-4988261'); -INSERT INTO customers VALUES ('FURIB', 'Furia Bacalhau e Frutos do Mar', 'Lino Rodriguez', 'Sales Manager', 'Jardim das rosas n. 32', 'Lisboa', NULL, '1675', 'Portugal', '(1) 354-2534', '(1) 354-2535'); -INSERT INTO customers VALUES ('GALED', 'Galería del gastrónomo', 'Eduardo Saavedra', 'Marketing Manager', 'Rambla de Cataluña, 23', 'Barcelona', NULL, '08022', 'Spain', '(93) 203 4560', '(93) 203 4561'); -INSERT INTO customers VALUES ('GODOS', 'Godos Cocina Típica', 'José Pedro Freyre', 'Sales Manager', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain', '(95) 555 82 82', NULL); -INSERT INTO customers VALUES ('GOURL', 'Gourmet Lanchonetes', 'André Fonseca', 'Sales Associate', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil', '(11) 555-9482', NULL); -INSERT INTO customers VALUES ('GREAL', 'Great Lakes Food Market', 'Howard Snyder', 'Marketing Manager', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA', '(503) 555-7555', NULL); -INSERT INTO customers VALUES ('GROSR', 'GROSELLA-Restaurante', 'Manuel Pereira', 'Owner', '5ª Ave. Los Palos Grandes', 'Caracas', 'DF', '1081', 'Venezuela', '(2) 283-2951', '(2) 283-3397'); -INSERT INTO customers VALUES ('HANAR', 'Hanari Carnes', 'Mario Pontes', 'Accounting Manager', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil', '(21) 555-0091', '(21) 555-8765'); -INSERT INTO customers VALUES ('HILAA', 'HILARION-Abastos', 'Carlos Hernández', 'Sales Representative', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela', '(5) 555-1340', '(5) 555-1948'); -INSERT INTO customers VALUES ('HUNGC', 'Hungry Coyote Import Store', 'Yoshi Latimer', 'Sales Representative', 'City Center Plaza 516 Main St.', 'Elgin', 'OR', '97827', 'USA', '(503) 555-6874', '(503) 555-2376'); -INSERT INTO customers VALUES ('HUNGO', 'Hungry Owl All-Night Grocers', 'Patricia McKenna', 'Sales Associate', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland', '2967 542', '2967 3333'); -INSERT INTO customers VALUES ('ISLAT', 'Island Trading', 'Helen Bennett', 'Marketing Manager', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK', '(198) 555-8888', NULL); -INSERT INTO customers VALUES ('KOENE', 'Königlich Essen', 'Philip Cramer', 'Sales Associate', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany', '0555-09876', NULL); -INSERT INTO customers VALUES ('LACOR', 'La corne d''abondance', 'Daniel Tonini', 'Sales Representative', '67, avenue de l''Europe', 'Versailles', NULL, '78000', 'France', '30.59.84.10', '30.59.85.11'); -INSERT INTO customers VALUES ('LAMAI', 'La maison d''Asie', 'Annette Roulet', 'Sales Manager', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France', '61.77.61.10', '61.77.61.11'); -INSERT INTO customers VALUES ('LAUGB', 'Laughing Bacchus Wine Cellars', 'Yoshi Tannamuri', 'Marketing Assistant', '1900 Oak St.', 'Vancouver', 'BC', 'V3F 2K1', 'Canada', '(604) 555-3392', '(604) 555-7293'); -INSERT INTO customers VALUES ('LAZYK', 'Lazy K Kountry Store', 'John Steel', 'Marketing Manager', '12 Orchestra Terrace', 'Walla Walla', 'WA', '99362', 'USA', '(509) 555-7969', '(509) 555-6221'); -INSERT INTO customers VALUES ('LEHMS', 'Lehmanns Marktstand', 'Renate Messner', 'Sales Representative', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany', '069-0245984', '069-0245874'); -INSERT INTO customers VALUES ('LETSS', 'Let''s Stop N Shop', 'Jaime Yorres', 'Owner', '87 Polk St. Suite 5', 'San Francisco', 'CA', '94117', 'USA', '(415) 555-5938', NULL); -INSERT INTO customers VALUES ('LILAS', 'LILA-Supermercado', 'Carlos González', 'Accounting Manager', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela', '(9) 331-6954', '(9) 331-7256'); -INSERT INTO customers VALUES ('LINOD', 'LINO-Delicateses', 'Felipe Izquierdo', 'Owner', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela', '(8) 34-56-12', '(8) 34-93-93'); -INSERT INTO customers VALUES ('LONEP', 'Lonesome Pine Restaurant', 'Fran Wilson', 'Sales Manager', '89 Chiaroscuro Rd.', 'Portland', 'OR', '97219', 'USA', '(503) 555-9573', '(503) 555-9646'); -INSERT INTO customers VALUES ('MAGAA', 'Magazzini Alimentari Riuniti', 'Giovanni Rovelli', 'Marketing Manager', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy', '035-640230', '035-640231'); -INSERT INTO customers VALUES ('MAISD', 'Maison Dewey', 'Catherine Dewey', 'Sales Agent', 'Rue Joseph-Bens 532', 'Bruxelles', NULL, 'B-1180', 'Belgium', '(02) 201 24 67', '(02) 201 24 68'); -INSERT INTO customers VALUES ('MEREP', 'Mère Paillarde', 'Jean Fresnière', 'Marketing Assistant', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada', '(514) 555-8054', '(514) 555-8055'); -INSERT INTO customers VALUES ('MORGK', 'Morgenstern Gesundkost', 'Alexander Feuer', 'Marketing Assistant', 'Heerstr. 22', 'Leipzig', NULL, '04179', 'Germany', '0342-023176', NULL); -INSERT INTO customers VALUES ('NORTS', 'North/South', 'Simon Crowther', 'Sales Associate', 'South House 300 Queensbridge', 'London', NULL, 'SW7 1RZ', 'UK', '(171) 555-7733', '(171) 555-2530'); -INSERT INTO customers VALUES ('OCEAN', 'Océano Atlántico Ltda.', 'Yvonne Moncada', 'Sales Agent', 'Ing. Gustavo Moncada 8585 Piso 20-A', 'Buenos Aires', NULL, '1010', 'Argentina', '(1) 135-5333', '(1) 135-5535'); -INSERT INTO customers VALUES ('OLDWO', 'Old World Delicatessen', 'Rene Phillips', 'Sales Representative', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA', '(907) 555-7584', '(907) 555-2880'); -INSERT INTO customers VALUES ('OTTIK', 'Ottilies Käseladen', 'Henriette Pfalzheim', 'Owner', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany', '0221-0644327', '0221-0765721'); -INSERT INTO customers VALUES ('PARIS', 'Paris spécialités', 'Marie Bertrand', 'Owner', '265, boulevard Charonne', 'Paris', NULL, '75012', 'France', '(1) 42.34.22.66', '(1) 42.34.22.77'); -INSERT INTO customers VALUES ('PERIC', 'Pericles Comidas clásicas', 'Guillermo Fernández', 'Sales Representative', 'Calle Dr. Jorge Cash 321', 'México D.F.', NULL, '05033', 'Mexico', '(5) 552-3745', '(5) 545-3745'); -INSERT INTO customers VALUES ('PICCO', 'Piccolo und mehr', 'Georg Pipps', 'Sales Manager', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria', '6562-9722', '6562-9723'); -INSERT INTO customers VALUES ('PRINI', 'Princesa Isabel Vinhos', 'Isabel de Castro', 'Sales Representative', 'Estrada da saúde n. 58', 'Lisboa', NULL, '1756', 'Portugal', '(1) 356-5634', NULL); -INSERT INTO customers VALUES ('QUEDE', 'Que Delícia', 'Bernardo Batista', 'Accounting Manager', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil', '(21) 555-4252', '(21) 555-4545'); -INSERT INTO customers VALUES ('QUEEN', 'Queen Cozinha', 'Lúcia Carvalho', 'Marketing Assistant', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil', '(11) 555-1189', NULL); -INSERT INTO customers VALUES ('QUICK', 'QUICK-Stop', 'Horst Kloss', 'Accounting Manager', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany', '0372-035188', NULL); -INSERT INTO customers VALUES ('RANCH', 'Rancho grande', 'Sergio Gutiérrez', 'Sales Representative', 'Av. del Libertador 900', 'Buenos Aires', NULL, '1010', 'Argentina', '(1) 123-5555', '(1) 123-5556'); -INSERT INTO customers VALUES ('RATTC', 'Rattlesnake Canyon Grocery', 'Paula Wilson', 'Assistant Sales Representative', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA', '(505) 555-5939', '(505) 555-3620'); -INSERT INTO customers VALUES ('REGGC', 'Reggiani Caseifici', 'Maurizio Moroni', 'Sales Associate', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy', '0522-556721', '0522-556722'); -INSERT INTO customers VALUES ('RICAR', 'Ricardo Adocicados', 'Janete Limeira', 'Assistant Sales Agent', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil', '(21) 555-3412', NULL); -INSERT INTO customers VALUES ('RICSU', 'Richter Supermarkt', 'Michael Holz', 'Sales Manager', 'Grenzacherweg 237', 'Genève', NULL, '1203', 'Switzerland', '0897-034214', NULL); -INSERT INTO customers VALUES ('ROMEY', 'Romero y tomillo', 'Alejandra Camino', 'Accounting Manager', 'Gran Vía, 1', 'Madrid', NULL, '28001', 'Spain', '(91) 745 6200', '(91) 745 6210'); -INSERT INTO customers VALUES ('SANTG', 'Santé Gourmet', 'Jonas Bergulfsen', 'Owner', 'Erling Skakkes gate 78', 'Stavern', NULL, '4110', 'Norway', '07-98 92 35', '07-98 92 47'); -INSERT INTO customers VALUES ('SAVEA', 'Save-a-lot Markets', 'Jose Pavarotti', 'Sales Representative', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA', '(208) 555-8097', NULL); -INSERT INTO customers VALUES ('SEVES', 'Seven Seas Imports', 'Hari Kumar', 'Sales Manager', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK', '(171) 555-1717', '(171) 555-5646'); -INSERT INTO customers VALUES ('SIMOB', 'Simons bistro', 'Jytte Petersen', 'Owner', 'Vinbæltet 34', 'Kobenhavn', NULL, '1734', 'Denmark', '31 12 34 56', '31 13 35 57'); -INSERT INTO customers VALUES ('SPECD', 'Spécialités du monde', 'Dominique Perrier', 'Marketing Manager', '25, rue Lauriston', 'Paris', NULL, '75016', 'France', '(1) 47.55.60.10', '(1) 47.55.60.20'); -INSERT INTO customers VALUES ('SPLIR', 'Split Rail Beer & Ale', 'Art Braunschweiger', 'Sales Manager', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA', '(307) 555-4680', '(307) 555-6525'); -INSERT INTO customers VALUES ('SUPRD', 'Suprêmes délices', 'Pascale Cartrain', 'Accounting Manager', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium', '(071) 23 67 22 20', '(071) 23 67 22 21'); -INSERT INTO customers VALUES ('THEBI', 'The Big Cheese', 'Liz Nixon', 'Marketing Manager', '89 Jefferson Way Suite 2', 'Portland', 'OR', '97201', 'USA', '(503) 555-3612', NULL); -INSERT INTO customers VALUES ('THECR', 'The Cracker Box', 'Liu Wong', 'Marketing Assistant', '55 Grizzly Peak Rd.', 'Butte', 'MT', '59801', 'USA', '(406) 555-5834', '(406) 555-8083'); -INSERT INTO customers VALUES ('TOMSP', 'Toms Spezialitäten', 'Karin Josephs', 'Marketing Manager', 'Luisenstr. 48', 'Münster', NULL, '44087', 'Germany', '0251-031259', '0251-035695'); -INSERT INTO customers VALUES ('TORTU', 'Tortuga Restaurante', 'Miguel Angel Paolino', 'Owner', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico', '(5) 555-2933', NULL); -INSERT INTO customers VALUES ('TRADH', 'Tradição Hipermercados', 'Anabela Domingues', 'Sales Representative', 'Av. Inês de Castro, 414', 'Sao Paulo', 'SP', '05634-030', 'Brazil', '(11) 555-2167', '(11) 555-2168'); -INSERT INTO customers VALUES ('TRAIH', 'Trail''s Head Gourmet Provisioners', 'Helvetius Nagy', 'Sales Associate', '722 DaVinci Blvd.', 'Kirkland', 'WA', '98034', 'USA', '(206) 555-8257', '(206) 555-2174'); -INSERT INTO customers VALUES ('VAFFE', 'Vaffeljernet', 'Palle Ibsen', 'Sales Manager', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark', '86 21 32 43', '86 22 33 44'); -INSERT INTO customers VALUES ('VICTE', 'Victuailles en stock', 'Mary Saveley', 'Sales Agent', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France', '78.32.54.86', '78.32.54.87'); -INSERT INTO customers VALUES ('VINET', 'Vins et alcools Chevalier', 'Paul Henriot', 'Accounting Manager', '59 rue de l''Abbaye', 'Reims', NULL, '51100', 'France', '26.47.15.10', '26.47.15.11'); -INSERT INTO customers VALUES ('WANDK', 'Die Wandernde Kuh', 'Rita Müller', 'Sales Representative', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany', '0711-020361', '0711-035428'); -INSERT INTO customers VALUES ('WARTH', 'Wartian Herkku', 'Pirkko Koskitalo', 'Accounting Manager', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland', '981-443655', '981-443655'); -INSERT INTO customers VALUES ('WELLI', 'Wellington Importadora', 'Paula Parente', 'Sales Manager', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil', '(14) 555-8122', NULL); -INSERT INTO customers VALUES ('WHITC', 'White Clover Markets', 'Karl Jablonski', 'Owner', '305 - 14th Ave. S. Suite 3B', 'Seattle', 'WA', '98128', 'USA', '(206) 555-4112', '(206) 555-4115'); -INSERT INTO customers VALUES ('WILMK', 'Wilman Kala', 'Matti Karttunen', 'Owner/Marketing Assistant', 'Keskuskatu 45', 'Helsinki', NULL, '21240', 'Finland', '90-224 8858', '90-224 8858'); -INSERT INTO customers VALUES ('WOLZA', 'Wolski Zajazd', 'Zbyszek Piestrzeniewicz', 'Owner', 'ul. Filtrowa 68', 'Warszawa', NULL, '01-012', 'Poland', '(26) 642-7012', '(26) 642-7012'); - - --- --- Data for Name: employees; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO employees VALUES (1, 'Davolio', 'Nancy', 'Sales Representative', 'Ms.', '1948-12-08', '1992-05-01', '507 - 20th Ave. E.\nApt. 2A', 'Seattle', 'WA', '98122', 'USA', '(206) 555-9857', '5467', '\x', 'Education includes a BA in psychology from Colorado State University in 1970. She also completed The Art of the Cold Call. Nancy is a member of Toastmasters International.', 2, 'http://accweb/emmployees/davolio.bmp'); -INSERT INTO employees VALUES (2, 'Fuller', 'Andrew', 'Vice President, Sales', 'Dr.', '1952-02-19', '1992-08-14', '908 W. Capital Way', 'Tacoma', 'WA', '98401', 'USA', '(206) 555-9482', '3457', '\x', 'Andrew received his BTS commercial in 1974 and a Ph.D. in international marketing from the University of Dallas in 1981. He is fluent in French and Italian and reads German. He joined the company as a sales representative, was promoted to sales manager in January 1992 and to vice president of sales in March 1993. Andrew is a member of the Sales Management Roundtable, the Seattle Chamber of Commerce, and the Pacific Rim Importers Association.', NULL, 'http://accweb/emmployees/fuller.bmp'); -INSERT INTO employees VALUES (3, 'Leverling', 'Janet', 'Sales Representative', 'Ms.', '1963-08-30', '1992-04-01', '722 Moss Bay Blvd.', 'Kirkland', 'WA', '98033', 'USA', '(206) 555-3412', '3355', '\x', 'Janet has a BS degree in chemistry from Boston College (1984). She has also completed a certificate program in food retailing management. Janet was hired as a sales associate in 1991 and promoted to sales representative in February 1992.', 2, 'http://accweb/emmployees/leverling.bmp'); -INSERT INTO employees VALUES (4, 'Peacock', 'Margaret', 'Sales Representative', 'Mrs.', '1937-09-19', '1993-05-03', '4110 Old Redmond Rd.', 'Redmond', 'WA', '98052', 'USA', '(206) 555-8122', '5176', '\x', 'Margaret holds a BA in English literature from Concordia College (1958) and an MA from the American Institute of Culinary Arts (1966). She was assigned to the London office temporarily from July through November 1992.', 2, 'http://accweb/emmployees/peacock.bmp'); -INSERT INTO employees VALUES (5, 'Buchanan', 'Steven', 'Sales Manager', 'Mr.', '1955-03-04', '1993-10-17', '14 Garrett Hill', 'London', NULL, 'SW1 8JR', 'UK', '(71) 555-4848', '3453', '\x', 'Steven Buchanan graduated from St. Andrews University, Scotland, with a BSC degree in 1976. Upon joining the company as a sales representative in 1992, he spent 6 months in an orientation program at the Seattle office and then returned to his permanent post in London. He was promoted to sales manager in March 1993. Mr. Buchanan has completed the courses Successful Telemarketing and International Sales Management. He is fluent in French.', 2, 'http://accweb/emmployees/buchanan.bmp'); -INSERT INTO employees VALUES (6, 'Suyama', 'Michael', 'Sales Representative', 'Mr.', '1963-07-02', '1993-10-17', 'Coventry House\nMiner Rd.', 'London', NULL, 'EC2 7JR', 'UK', '(71) 555-7773', '428', '\x', 'Michael is a graduate of Sussex University (MA, economics, 1983) and the University of California at Los Angeles (MBA, marketing, 1986). He has also taken the courses Multi-Cultural Selling and Time Management for the Sales Professional. He is fluent in Japanese and can read and write French, Portuguese, and Spanish.', 5, 'http://accweb/emmployees/davolio.bmp'); -INSERT INTO employees VALUES (7, 'King', 'Robert', 'Sales Representative', 'Mr.', '1960-05-29', '1994-01-02', 'Edgeham Hollow\nWinchester Way', 'London', NULL, 'RG1 9SP', 'UK', '(71) 555-5598', '465', '\x', 'Robert King served in the Peace Corps and traveled extensively before completing his degree in English at the University of Michigan in 1992, the year he joined the company. After completing a course entitled Selling in Europe, he was transferred to the London office in March 1993.', 5, 'http://accweb/emmployees/davolio.bmp'); -INSERT INTO employees VALUES (8, 'Callahan', 'Laura', 'Inside Sales Coordinator', 'Ms.', '1958-01-09', '1994-03-05', '4726 - 11th Ave. N.E.', 'Seattle', 'WA', '98105', 'USA', '(206) 555-1189', '2344', '\x', 'Laura received a BA in psychology from the University of Washington. She has also completed a course in business French. 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-INSERT INTO orders VALUES (10249, 'TOMSP', 6, '1996-07-05', '1996-08-16', '1996-07-10', 1, 11.6099997, 'Toms Spezialitäten', 'Luisenstr. 48', 'Münster', NULL, '44087', 'Germany'); -INSERT INTO orders VALUES (10250, 'HANAR', 4, '1996-07-08', '1996-08-05', '1996-07-12', 2, 65.8300018, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10251, 'VICTE', 3, '1996-07-08', '1996-08-05', '1996-07-15', 1, 41.3400002, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10252, 'SUPRD', 4, '1996-07-09', '1996-08-06', '1996-07-11', 2, 51.2999992, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10253, 'HANAR', 3, '1996-07-10', '1996-07-24', '1996-07-16', 2, 58.1699982, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10254, 'CHOPS', 5, '1996-07-11', '1996-08-08', '1996-07-23', 2, 22.9799995, 'Chop-suey Chinese', 'Hauptstr. 31', 'Bern', NULL, '3012', 'Switzerland'); -INSERT INTO orders VALUES (10255, 'RICSU', 9, '1996-07-12', '1996-08-09', '1996-07-15', 3, 148.330002, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (10256, 'WELLI', 3, '1996-07-15', '1996-08-12', '1996-07-17', 2, 13.9700003, 'Wellington Importadora', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil'); -INSERT INTO orders VALUES (10257, 'HILAA', 4, '1996-07-16', '1996-08-13', '1996-07-22', 3, 81.9100037, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10258, 'ERNSH', 1, '1996-07-17', '1996-08-14', '1996-07-23', 1, 140.509995, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10259, 'CENTC', 4, '1996-07-18', '1996-08-15', '1996-07-25', 3, 3.25, 'Centro comercial Moctezuma', 'Sierras de Granada 9993', 'México D.F.', NULL, '05022', 'Mexico'); -INSERT INTO orders VALUES (10260, 'OTTIK', 4, '1996-07-19', '1996-08-16', '1996-07-29', 1, 55.0900002, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (10261, 'QUEDE', 4, '1996-07-19', '1996-08-16', '1996-07-30', 2, 3.04999995, 'Que Delícia', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil'); -INSERT INTO orders VALUES (10262, 'RATTC', 8, '1996-07-22', '1996-08-19', '1996-07-25', 3, 48.2900009, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10263, 'ERNSH', 9, '1996-07-23', '1996-08-20', '1996-07-31', 3, 146.059998, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10264, 'FOLKO', 6, '1996-07-24', '1996-08-21', '1996-08-23', 3, 3.67000008, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10265, 'BLONP', 2, '1996-07-25', '1996-08-22', '1996-08-12', 1, 55.2799988, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10266, 'WARTH', 3, '1996-07-26', '1996-09-06', '1996-07-31', 3, 25.7299995, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10267, 'FRANK', 4, '1996-07-29', '1996-08-26', '1996-08-06', 1, 208.580002, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10268, 'GROSR', 8, '1996-07-30', '1996-08-27', '1996-08-02', 3, 66.2900009, 'GROSELLA-Restaurante', '5ª Ave. Los Palos Grandes', 'Caracas', 'DF', '1081', 'Venezuela'); -INSERT INTO orders VALUES (10269, 'WHITC', 5, '1996-07-31', '1996-08-14', '1996-08-09', 1, 4.55999994, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10270, 'WARTH', 1, '1996-08-01', '1996-08-29', '1996-08-02', 1, 136.539993, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10271, 'SPLIR', 6, '1996-08-01', '1996-08-29', '1996-08-30', 2, 4.53999996, 'Split Rail Beer & Ale', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA'); -INSERT INTO orders VALUES (10272, 'RATTC', 6, '1996-08-02', '1996-08-30', '1996-08-06', 2, 98.0299988, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10273, 'QUICK', 3, '1996-08-05', '1996-09-02', '1996-08-12', 3, 76.0699997, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10274, 'VINET', 6, '1996-08-06', '1996-09-03', '1996-08-16', 1, 6.01000023, 'Vins et alcools Chevalier', '59 rue de l''Abbaye', 'Reims', NULL, '51100', 'France'); -INSERT INTO orders VALUES (10275, 'MAGAA', 1, '1996-08-07', '1996-09-04', '1996-08-09', 1, 26.9300003, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10276, 'TORTU', 8, '1996-08-08', '1996-08-22', '1996-08-14', 3, 13.8400002, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10277, 'MORGK', 2, '1996-08-09', '1996-09-06', '1996-08-13', 3, 125.769997, 'Morgenstern Gesundkost', 'Heerstr. 22', 'Leipzig', NULL, '04179', 'Germany'); -INSERT INTO orders VALUES (10278, 'BERGS', 8, '1996-08-12', '1996-09-09', '1996-08-16', 2, 92.6900024, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10279, 'LEHMS', 8, '1996-08-13', '1996-09-10', '1996-08-16', 2, 25.8299999, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10280, 'BERGS', 2, '1996-08-14', '1996-09-11', '1996-09-12', 1, 8.97999954, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10281, 'ROMEY', 4, '1996-08-14', '1996-08-28', '1996-08-21', 1, 2.94000006, 'Romero y tomillo', 'Gran Vía, 1', 'Madrid', NULL, '28001', 'Spain'); -INSERT INTO orders VALUES (10282, 'ROMEY', 4, '1996-08-15', '1996-09-12', '1996-08-21', 1, 12.6899996, 'Romero y tomillo', 'Gran Vía, 1', 'Madrid', NULL, '28001', 'Spain'); -INSERT INTO orders VALUES (10283, 'LILAS', 3, '1996-08-16', '1996-09-13', '1996-08-23', 3, 84.8099976, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10284, 'LEHMS', 4, '1996-08-19', '1996-09-16', '1996-08-27', 1, 76.5599976, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10285, 'QUICK', 1, '1996-08-20', '1996-09-17', '1996-08-26', 2, 76.8300018, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10286, 'QUICK', 8, '1996-08-21', '1996-09-18', '1996-08-30', 3, 229.240005, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10287, 'RICAR', 8, '1996-08-22', '1996-09-19', '1996-08-28', 3, 12.7600002, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10288, 'REGGC', 4, '1996-08-23', '1996-09-20', '1996-09-03', 1, 7.44999981, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10289, 'BSBEV', 7, '1996-08-26', '1996-09-23', '1996-08-28', 3, 22.7700005, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (10290, 'COMMI', 8, '1996-08-27', '1996-09-24', '1996-09-03', 1, 79.6999969, 'Comércio Mineiro', 'Av. dos Lusíadas, 23', 'Sao Paulo', 'SP', '05432-043', 'Brazil'); -INSERT INTO orders VALUES (10291, 'QUEDE', 6, '1996-08-27', '1996-09-24', '1996-09-04', 2, 6.4000001, 'Que Delícia', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil'); -INSERT INTO orders VALUES (10292, 'TRADH', 1, '1996-08-28', '1996-09-25', '1996-09-02', 2, 1.35000002, 'Tradiçao Hipermercados', 'Av. Inês de Castro, 414', 'Sao Paulo', 'SP', '05634-030', 'Brazil'); -INSERT INTO orders VALUES (10293, 'TORTU', 1, '1996-08-29', '1996-09-26', '1996-09-11', 3, 21.1800003, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10294, 'RATTC', 4, '1996-08-30', '1996-09-27', '1996-09-05', 2, 147.259995, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10295, 'VINET', 2, '1996-09-02', '1996-09-30', '1996-09-10', 2, 1.14999998, 'Vins et alcools Chevalier', '59 rue de l''Abbaye', 'Reims', NULL, '51100', 'France'); -INSERT INTO orders VALUES (10296, 'LILAS', 6, '1996-09-03', '1996-10-01', '1996-09-11', 1, 0.119999997, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10297, 'BLONP', 5, '1996-09-04', '1996-10-16', '1996-09-10', 2, 5.73999977, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10298, 'HUNGO', 6, '1996-09-05', '1996-10-03', '1996-09-11', 2, 168.220001, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10299, 'RICAR', 4, '1996-09-06', '1996-10-04', '1996-09-13', 2, 29.7600002, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10300, 'MAGAA', 2, '1996-09-09', '1996-10-07', '1996-09-18', 2, 17.6800003, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10301, 'WANDK', 8, '1996-09-09', '1996-10-07', '1996-09-17', 2, 45.0800018, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (10302, 'SUPRD', 4, '1996-09-10', '1996-10-08', '1996-10-09', 2, 6.26999998, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10303, 'GODOS', 7, '1996-09-11', '1996-10-09', '1996-09-18', 2, 107.830002, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (10304, 'TORTU', 1, '1996-09-12', '1996-10-10', '1996-09-17', 2, 63.7900009, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10305, 'OLDWO', 8, '1996-09-13', '1996-10-11', '1996-10-09', 3, 257.619995, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (10306, 'ROMEY', 1, '1996-09-16', '1996-10-14', '1996-09-23', 3, 7.55999994, 'Romero y tomillo', 'Gran Vía, 1', 'Madrid', NULL, '28001', 'Spain'); -INSERT INTO orders VALUES (10307, 'LONEP', 2, '1996-09-17', '1996-10-15', '1996-09-25', 2, 0.560000002, 'Lonesome Pine Restaurant', '89 Chiaroscuro Rd.', 'Portland', 'OR', '97219', 'USA'); -INSERT INTO orders VALUES (10308, 'ANATR', 7, '1996-09-18', '1996-10-16', '1996-09-24', 3, 1.61000001, 'Ana Trujillo Emparedados y helados', 'Avda. de la Constitución 2222', 'México D.F.', NULL, '05021', 'Mexico'); -INSERT INTO orders VALUES (10309, 'HUNGO', 3, '1996-09-19', '1996-10-17', '1996-10-23', 1, 47.2999992, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10310, 'THEBI', 8, '1996-09-20', '1996-10-18', '1996-09-27', 2, 17.5200005, 'The Big Cheese', '89 Jefferson Way Suite 2', 'Portland', 'OR', '97201', 'USA'); -INSERT INTO orders VALUES (10311, 'DUMON', 1, '1996-09-20', '1996-10-04', '1996-09-26', 3, 24.6900005, 'Du monde entier', '67, rue des Cinquante Otages', 'Nantes', NULL, '44000', 'France'); -INSERT INTO orders VALUES (10312, 'WANDK', 2, '1996-09-23', '1996-10-21', '1996-10-03', 2, 40.2599983, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (10313, 'QUICK', 2, '1996-09-24', '1996-10-22', '1996-10-04', 2, 1.96000004, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10314, 'RATTC', 1, '1996-09-25', '1996-10-23', '1996-10-04', 2, 74.1600037, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10315, 'ISLAT', 4, '1996-09-26', '1996-10-24', '1996-10-03', 2, 41.7599983, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10316, 'RATTC', 1, '1996-09-27', '1996-10-25', '1996-10-08', 3, 150.149994, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10317, 'LONEP', 6, '1996-09-30', '1996-10-28', '1996-10-10', 1, 12.6899996, 'Lonesome Pine Restaurant', '89 Chiaroscuro Rd.', 'Portland', 'OR', '97219', 'USA'); -INSERT INTO orders VALUES (10318, 'ISLAT', 8, '1996-10-01', '1996-10-29', '1996-10-04', 2, 4.73000002, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10319, 'TORTU', 7, '1996-10-02', '1996-10-30', '1996-10-11', 3, 64.5, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10320, 'WARTH', 5, '1996-10-03', '1996-10-17', '1996-10-18', 3, 34.5699997, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10321, 'ISLAT', 3, '1996-10-03', '1996-10-31', '1996-10-11', 2, 3.43000007, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10322, 'PERIC', 7, '1996-10-04', '1996-11-01', '1996-10-23', 3, 0.400000006, 'Pericles Comidas clásicas', 'Calle Dr. Jorge Cash 321', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10323, 'KOENE', 4, '1996-10-07', '1996-11-04', '1996-10-14', 1, 4.88000011, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10324, 'SAVEA', 9, '1996-10-08', '1996-11-05', '1996-10-10', 1, 214.270004, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10325, 'KOENE', 1, '1996-10-09', '1996-10-23', '1996-10-14', 3, 64.8600006, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10326, 'BOLID', 4, '1996-10-10', '1996-11-07', '1996-10-14', 2, 77.9199982, 'Bólido Comidas preparadas', 'C/ Araquil, 67', 'Madrid', NULL, '28023', 'Spain'); -INSERT INTO orders VALUES (10327, 'FOLKO', 2, '1996-10-11', '1996-11-08', '1996-10-14', 1, 63.3600006, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10328, 'FURIB', 4, '1996-10-14', '1996-11-11', '1996-10-17', 3, 87.0299988, 'Furia Bacalhau e Frutos do Mar', 'Jardim das rosas n. 32', 'Lisboa', NULL, '1675', 'Portugal'); -INSERT INTO orders VALUES (10329, 'SPLIR', 4, '1996-10-15', '1996-11-26', '1996-10-23', 2, 191.669998, 'Split Rail Beer & Ale', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA'); -INSERT INTO orders VALUES (10330, 'LILAS', 3, '1996-10-16', '1996-11-13', '1996-10-28', 1, 12.75, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10331, 'BONAP', 9, '1996-10-16', '1996-11-27', '1996-10-21', 1, 10.1899996, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10332, 'MEREP', 3, '1996-10-17', '1996-11-28', '1996-10-21', 2, 52.8400002, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10333, 'WARTH', 5, '1996-10-18', '1996-11-15', '1996-10-25', 3, 0.589999974, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10334, 'VICTE', 8, '1996-10-21', '1996-11-18', '1996-10-28', 2, 8.56000042, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10335, 'HUNGO', 7, '1996-10-22', '1996-11-19', '1996-10-24', 2, 42.1100006, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10336, 'PRINI', 7, '1996-10-23', '1996-11-20', '1996-10-25', 2, 15.5100002, 'Princesa Isabel Vinhos', 'Estrada da saúde n. 58', 'Lisboa', NULL, '1756', 'Portugal'); -INSERT INTO orders VALUES (10337, 'FRANK', 4, '1996-10-24', '1996-11-21', '1996-10-29', 3, 108.260002, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10338, 'OLDWO', 4, '1996-10-25', '1996-11-22', '1996-10-29', 3, 84.2099991, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (10339, 'MEREP', 2, '1996-10-28', '1996-11-25', '1996-11-04', 2, 15.6599998, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10340, 'BONAP', 1, '1996-10-29', '1996-11-26', '1996-11-08', 3, 166.309998, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10341, 'SIMOB', 7, '1996-10-29', '1996-11-26', '1996-11-05', 3, 26.7800007, 'Simons bistro', 'Vinbæltet 34', 'Kobenhavn', NULL, '1734', 'Denmark'); -INSERT INTO orders VALUES (10342, 'FRANK', 4, '1996-10-30', '1996-11-13', '1996-11-04', 2, 54.8300018, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10343, 'LEHMS', 4, '1996-10-31', '1996-11-28', '1996-11-06', 1, 110.370003, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10344, 'WHITC', 4, '1996-11-01', '1996-11-29', '1996-11-05', 2, 23.2900009, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10345, 'QUICK', 2, '1996-11-04', '1996-12-02', '1996-11-11', 2, 249.059998, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10346, 'RATTC', 3, '1996-11-05', '1996-12-17', '1996-11-08', 3, 142.080002, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10347, 'FAMIA', 4, '1996-11-06', '1996-12-04', '1996-11-08', 3, 3.0999999, 'Familia Arquibaldo', 'Rua Orós, 92', 'Sao Paulo', 'SP', '05442-030', 'Brazil'); -INSERT INTO orders VALUES (10348, 'WANDK', 4, '1996-11-07', '1996-12-05', '1996-11-15', 2, 0.779999971, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (10349, 'SPLIR', 7, '1996-11-08', '1996-12-06', '1996-11-15', 1, 8.63000011, 'Split Rail Beer & Ale', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA'); -INSERT INTO orders VALUES (10350, 'LAMAI', 6, '1996-11-11', '1996-12-09', '1996-12-03', 2, 64.1900024, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10351, 'ERNSH', 1, '1996-11-11', '1996-12-09', '1996-11-20', 1, 162.330002, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10352, 'FURIB', 3, '1996-11-12', '1996-11-26', '1996-11-18', 3, 1.29999995, 'Furia Bacalhau e Frutos do Mar', 'Jardim das rosas n. 32', 'Lisboa', NULL, '1675', 'Portugal'); -INSERT INTO orders VALUES (10353, 'PICCO', 7, '1996-11-13', '1996-12-11', '1996-11-25', 3, 360.630005, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (10354, 'PERIC', 8, '1996-11-14', '1996-12-12', '1996-11-20', 3, 53.7999992, 'Pericles Comidas clásicas', 'Calle Dr. Jorge Cash 321', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10355, 'AROUT', 6, '1996-11-15', '1996-12-13', '1996-11-20', 1, 41.9500008, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10356, 'WANDK', 6, '1996-11-18', '1996-12-16', '1996-11-27', 2, 36.7099991, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (10357, 'LILAS', 1, '1996-11-19', '1996-12-17', '1996-12-02', 3, 34.8800011, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10358, 'LAMAI', 5, '1996-11-20', '1996-12-18', '1996-11-27', 1, 19.6399994, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10359, 'SEVES', 5, '1996-11-21', '1996-12-19', '1996-11-26', 3, 288.429993, 'Seven Seas Imports', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK'); -INSERT INTO orders VALUES (10360, 'BLONP', 4, '1996-11-22', '1996-12-20', '1996-12-02', 3, 131.699997, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10361, 'QUICK', 1, '1996-11-22', '1996-12-20', '1996-12-03', 2, 183.169998, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10362, 'BONAP', 3, '1996-11-25', '1996-12-23', '1996-11-28', 1, 96.0400009, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10363, 'DRACD', 4, '1996-11-26', '1996-12-24', '1996-12-04', 3, 30.5400009, 'Drachenblut Delikatessen', 'Walserweg 21', 'Aachen', NULL, '52066', 'Germany'); -INSERT INTO orders VALUES (10364, 'EASTC', 1, '1996-11-26', '1997-01-07', '1996-12-04', 1, 71.9700012, 'Eastern Connection', '35 King George', 'London', NULL, 'WX3 6FW', 'UK'); -INSERT INTO orders VALUES (10365, 'ANTON', 3, '1996-11-27', '1996-12-25', '1996-12-02', 2, 22, 'Antonio Moreno Taquería', 'Mataderos 2312', 'México D.F.', NULL, '05023', 'Mexico'); -INSERT INTO orders VALUES (10366, 'GALED', 8, '1996-11-28', '1997-01-09', '1996-12-30', 2, 10.1400003, 'Galería del gastronómo', 'Rambla de Cataluña, 23', 'Barcelona', NULL, '8022', 'Spain'); -INSERT INTO orders VALUES (10367, 'VAFFE', 7, '1996-11-28', '1996-12-26', '1996-12-02', 3, 13.5500002, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10368, 'ERNSH', 2, '1996-11-29', '1996-12-27', '1996-12-02', 2, 101.949997, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10369, 'SPLIR', 8, '1996-12-02', '1996-12-30', '1996-12-09', 2, 195.679993, 'Split Rail Beer & Ale', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA'); -INSERT INTO orders VALUES (10370, 'CHOPS', 6, '1996-12-03', '1996-12-31', '1996-12-27', 2, 1.16999996, 'Chop-suey Chinese', 'Hauptstr. 31', 'Bern', NULL, '3012', 'Switzerland'); -INSERT INTO orders VALUES (10371, 'LAMAI', 1, '1996-12-03', '1996-12-31', '1996-12-24', 1, 0.449999988, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10372, 'QUEEN', 5, '1996-12-04', '1997-01-01', '1996-12-09', 2, 890.780029, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10373, 'HUNGO', 4, '1996-12-05', '1997-01-02', '1996-12-11', 3, 124.120003, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10374, 'WOLZA', 1, '1996-12-05', '1997-01-02', '1996-12-09', 3, 3.94000006, 'Wolski Zajazd', 'ul. Filtrowa 68', 'Warszawa', NULL, '01-012', 'Poland'); -INSERT INTO orders VALUES (10375, 'HUNGC', 3, '1996-12-06', '1997-01-03', '1996-12-09', 2, 20.1200008, 'Hungry Coyote Import Store', 'City Center Plaza 516 Main St.', 'Elgin', 'OR', '97827', 'USA'); -INSERT INTO orders VALUES (10376, 'MEREP', 1, '1996-12-09', '1997-01-06', '1996-12-13', 2, 20.3899994, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10377, 'SEVES', 1, '1996-12-09', '1997-01-06', '1996-12-13', 3, 22.2099991, 'Seven Seas Imports', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK'); -INSERT INTO orders VALUES (10378, 'FOLKO', 5, '1996-12-10', '1997-01-07', '1996-12-19', 3, 5.44000006, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10379, 'QUEDE', 2, '1996-12-11', '1997-01-08', '1996-12-13', 1, 45.0299988, 'Que Delícia', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil'); -INSERT INTO orders VALUES (10380, 'HUNGO', 8, '1996-12-12', '1997-01-09', '1997-01-16', 3, 35.0299988, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10381, 'LILAS', 3, '1996-12-12', '1997-01-09', '1996-12-13', 3, 7.98999977, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10382, 'ERNSH', 4, '1996-12-13', '1997-01-10', '1996-12-16', 1, 94.7699966, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10383, 'AROUT', 8, '1996-12-16', '1997-01-13', '1996-12-18', 3, 34.2400017, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10384, 'BERGS', 3, '1996-12-16', '1997-01-13', '1996-12-20', 3, 168.639999, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10385, 'SPLIR', 1, '1996-12-17', '1997-01-14', '1996-12-23', 2, 30.9599991, 'Split Rail Beer & Ale', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA'); -INSERT INTO orders VALUES (10386, 'FAMIA', 9, '1996-12-18', '1997-01-01', '1996-12-25', 3, 13.9899998, 'Familia Arquibaldo', 'Rua Orós, 92', 'Sao Paulo', 'SP', '05442-030', 'Brazil'); -INSERT INTO orders VALUES (10387, 'SANTG', 1, '1996-12-18', '1997-01-15', '1996-12-20', 2, 93.6299973, 'Santé Gourmet', 'Erling Skakkes gate 78', 'Stavern', NULL, '4110', 'Norway'); -INSERT INTO orders VALUES (10388, 'SEVES', 2, '1996-12-19', '1997-01-16', '1996-12-20', 1, 34.8600006, 'Seven Seas Imports', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK'); -INSERT INTO orders VALUES (10389, 'BOTTM', 4, '1996-12-20', '1997-01-17', '1996-12-24', 2, 47.4199982, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10390, 'ERNSH', 6, '1996-12-23', '1997-01-20', '1996-12-26', 1, 126.379997, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10391, 'DRACD', 3, '1996-12-23', '1997-01-20', '1996-12-31', 3, 5.44999981, 'Drachenblut Delikatessen', 'Walserweg 21', 'Aachen', NULL, '52066', 'Germany'); -INSERT INTO orders VALUES (10392, 'PICCO', 2, '1996-12-24', '1997-01-21', '1997-01-01', 3, 122.459999, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (10393, 'SAVEA', 1, '1996-12-25', '1997-01-22', '1997-01-03', 3, 126.559998, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10394, 'HUNGC', 1, '1996-12-25', '1997-01-22', '1997-01-03', 3, 30.3400002, 'Hungry Coyote Import Store', 'City Center Plaza 516 Main St.', 'Elgin', 'OR', '97827', 'USA'); -INSERT INTO orders VALUES (10395, 'HILAA', 6, '1996-12-26', '1997-01-23', '1997-01-03', 1, 184.410004, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10396, 'FRANK', 1, '1996-12-27', '1997-01-10', '1997-01-06', 3, 135.350006, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10397, 'PRINI', 5, '1996-12-27', '1997-01-24', '1997-01-02', 1, 60.2599983, 'Princesa Isabel Vinhos', 'Estrada da saúde n. 58', 'Lisboa', NULL, '1756', 'Portugal'); -INSERT INTO orders VALUES (10398, 'SAVEA', 2, '1996-12-30', '1997-01-27', '1997-01-09', 3, 89.1600037, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10399, 'VAFFE', 8, '1996-12-31', '1997-01-14', '1997-01-08', 3, 27.3600006, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10400, 'EASTC', 1, '1997-01-01', '1997-01-29', '1997-01-16', 3, 83.9300003, 'Eastern Connection', '35 King George', 'London', NULL, 'WX3 6FW', 'UK'); -INSERT INTO orders VALUES (10401, 'RATTC', 1, '1997-01-01', '1997-01-29', '1997-01-10', 1, 12.5100002, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10402, 'ERNSH', 8, '1997-01-02', '1997-02-13', '1997-01-10', 2, 67.8799973, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10403, 'ERNSH', 4, '1997-01-03', '1997-01-31', '1997-01-09', 3, 73.7900009, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10404, 'MAGAA', 2, '1997-01-03', '1997-01-31', '1997-01-08', 1, 155.970001, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10405, 'LINOD', 1, '1997-01-06', '1997-02-03', '1997-01-22', 1, 34.8199997, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10406, 'QUEEN', 7, '1997-01-07', '1997-02-18', '1997-01-13', 1, 108.040001, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10407, 'OTTIK', 2, '1997-01-07', '1997-02-04', '1997-01-30', 2, 91.4800034, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (10408, 'FOLIG', 8, '1997-01-08', '1997-02-05', '1997-01-14', 1, 11.2600002, 'Folies gourmandes', '184, chaussée de Tournai', 'Lille', NULL, '59000', 'France'); -INSERT INTO orders VALUES (10409, 'OCEAN', 3, '1997-01-09', '1997-02-06', '1997-01-14', 1, 29.8299999, 'Océano Atlántico Ltda.', 'Ing. Gustavo Moncada 8585 Piso 20-A', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10410, 'BOTTM', 3, '1997-01-10', '1997-02-07', '1997-01-15', 3, 2.4000001, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10411, 'BOTTM', 9, '1997-01-10', '1997-02-07', '1997-01-21', 3, 23.6499996, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10412, 'WARTH', 8, '1997-01-13', '1997-02-10', '1997-01-15', 2, 3.76999998, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10413, 'LAMAI', 3, '1997-01-14', '1997-02-11', '1997-01-16', 2, 95.6600037, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10414, 'FAMIA', 2, '1997-01-14', '1997-02-11', '1997-01-17', 3, 21.4799995, 'Familia Arquibaldo', 'Rua Orós, 92', 'Sao Paulo', 'SP', '05442-030', 'Brazil'); -INSERT INTO orders VALUES (10415, 'HUNGC', 3, '1997-01-15', '1997-02-12', '1997-01-24', 1, 0.200000003, 'Hungry Coyote Import Store', 'City Center Plaza 516 Main St.', 'Elgin', 'OR', '97827', 'USA'); -INSERT INTO orders VALUES (10416, 'WARTH', 8, '1997-01-16', '1997-02-13', '1997-01-27', 3, 22.7199993, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10417, 'SIMOB', 4, '1997-01-16', '1997-02-13', '1997-01-28', 3, 70.2900009, 'Simons bistro', 'Vinbæltet 34', 'Kobenhavn', NULL, '1734', 'Denmark'); -INSERT INTO orders VALUES (10418, 'QUICK', 4, '1997-01-17', '1997-02-14', '1997-01-24', 1, 17.5499992, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10419, 'RICSU', 4, '1997-01-20', '1997-02-17', '1997-01-30', 2, 137.350006, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (10420, 'WELLI', 3, '1997-01-21', '1997-02-18', '1997-01-27', 1, 44.1199989, 'Wellington Importadora', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil'); -INSERT INTO orders VALUES (10421, 'QUEDE', 8, '1997-01-21', '1997-03-04', '1997-01-27', 1, 99.2300034, 'Que Delícia', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil'); -INSERT INTO orders VALUES (10422, 'FRANS', 2, '1997-01-22', '1997-02-19', '1997-01-31', 1, 3.01999998, 'Franchi S.p.A.', 'Via Monte Bianco 34', 'Torino', NULL, '10100', 'Italy'); -INSERT INTO orders VALUES (10423, 'GOURL', 6, '1997-01-23', '1997-02-06', '1997-02-24', 3, 24.5, 'Gourmet Lanchonetes', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil'); -INSERT INTO orders VALUES (10424, 'MEREP', 7, '1997-01-23', '1997-02-20', '1997-01-27', 2, 370.609985, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10425, 'LAMAI', 6, '1997-01-24', '1997-02-21', '1997-02-14', 2, 7.92999983, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10426, 'GALED', 4, '1997-01-27', '1997-02-24', '1997-02-06', 1, 18.6900005, 'Galería del gastronómo', 'Rambla de Cataluña, 23', 'Barcelona', NULL, '8022', 'Spain'); -INSERT INTO orders VALUES (10427, 'PICCO', 4, '1997-01-27', '1997-02-24', '1997-03-03', 2, 31.2900009, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (10428, 'REGGC', 7, '1997-01-28', '1997-02-25', '1997-02-04', 1, 11.0900002, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10429, 'HUNGO', 3, '1997-01-29', '1997-03-12', '1997-02-07', 2, 56.6300011, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10430, 'ERNSH', 4, '1997-01-30', '1997-02-13', '1997-02-03', 1, 458.779999, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10431, 'BOTTM', 4, '1997-01-30', '1997-02-13', '1997-02-07', 2, 44.1699982, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10432, 'SPLIR', 3, '1997-01-31', '1997-02-14', '1997-02-07', 2, 4.34000015, 'Split Rail Beer & Ale', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA'); -INSERT INTO orders VALUES (10433, 'PRINI', 3, '1997-02-03', '1997-03-03', '1997-03-04', 3, 73.8300018, 'Princesa Isabel Vinhos', 'Estrada da saúde n. 58', 'Lisboa', NULL, '1756', 'Portugal'); -INSERT INTO orders VALUES (10434, 'FOLKO', 3, '1997-02-03', '1997-03-03', '1997-02-13', 2, 17.9200001, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10435, 'CONSH', 8, '1997-02-04', '1997-03-18', '1997-02-07', 2, 9.21000004, 'Consolidated Holdings', 'Berkeley Gardens 12 Brewery', 'London', NULL, 'WX1 6LT', 'UK'); -INSERT INTO orders VALUES (10436, 'BLONP', 3, '1997-02-05', '1997-03-05', '1997-02-11', 2, 156.660004, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10437, 'WARTH', 8, '1997-02-05', '1997-03-05', '1997-02-12', 1, 19.9699993, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10438, 'TOMSP', 3, '1997-02-06', '1997-03-06', '1997-02-14', 2, 8.23999977, 'Toms Spezialitäten', 'Luisenstr. 48', 'Münster', NULL, '44087', 'Germany'); -INSERT INTO orders VALUES (10439, 'MEREP', 6, '1997-02-07', '1997-03-07', '1997-02-10', 3, 4.07000017, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10440, 'SAVEA', 4, '1997-02-10', '1997-03-10', '1997-02-28', 2, 86.5299988, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10441, 'OLDWO', 3, '1997-02-10', '1997-03-24', '1997-03-14', 2, 73.0199966, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (10442, 'ERNSH', 3, '1997-02-11', '1997-03-11', '1997-02-18', 2, 47.9399986, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10443, 'REGGC', 8, '1997-02-12', '1997-03-12', '1997-02-14', 1, 13.9499998, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10444, 'BERGS', 3, '1997-02-12', '1997-03-12', '1997-02-21', 3, 3.5, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10445, 'BERGS', 3, '1997-02-13', '1997-03-13', '1997-02-20', 1, 9.30000019, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10446, 'TOMSP', 6, '1997-02-14', '1997-03-14', '1997-02-19', 1, 14.6800003, 'Toms Spezialitäten', 'Luisenstr. 48', 'Münster', NULL, '44087', 'Germany'); -INSERT INTO orders VALUES (10447, 'RICAR', 4, '1997-02-14', '1997-03-14', '1997-03-07', 2, 68.6600037, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10448, 'RANCH', 4, '1997-02-17', '1997-03-17', '1997-02-24', 2, 38.8199997, 'Rancho grande', 'Av. del Libertador 900', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10449, 'BLONP', 3, '1997-02-18', '1997-03-18', '1997-02-27', 2, 53.2999992, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10450, 'VICTE', 8, '1997-02-19', '1997-03-19', '1997-03-11', 2, 7.23000002, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10451, 'QUICK', 4, '1997-02-19', '1997-03-05', '1997-03-12', 3, 189.089996, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10452, 'SAVEA', 8, '1997-02-20', '1997-03-20', '1997-02-26', 1, 140.259995, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10453, 'AROUT', 1, '1997-02-21', '1997-03-21', '1997-02-26', 2, 25.3600006, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10454, 'LAMAI', 4, '1997-02-21', '1997-03-21', '1997-02-25', 3, 2.74000001, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10455, 'WARTH', 8, '1997-02-24', '1997-04-07', '1997-03-03', 2, 180.449997, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10456, 'KOENE', 8, '1997-02-25', '1997-04-08', '1997-02-28', 2, 8.11999989, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10457, 'KOENE', 2, '1997-02-25', '1997-03-25', '1997-03-03', 1, 11.5699997, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10458, 'SUPRD', 7, '1997-02-26', '1997-03-26', '1997-03-04', 3, 147.059998, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10459, 'VICTE', 4, '1997-02-27', '1997-03-27', '1997-02-28', 2, 25.0900002, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10460, 'FOLKO', 8, '1997-02-28', '1997-03-28', '1997-03-03', 1, 16.2700005, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10461, 'LILAS', 1, '1997-02-28', '1997-03-28', '1997-03-05', 3, 148.610001, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10462, 'CONSH', 2, '1997-03-03', '1997-03-31', '1997-03-18', 1, 6.17000008, 'Consolidated Holdings', 'Berkeley Gardens 12 Brewery', 'London', NULL, 'WX1 6LT', 'UK'); -INSERT INTO orders VALUES (10463, 'SUPRD', 5, '1997-03-04', '1997-04-01', '1997-03-06', 3, 14.7799997, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10464, 'FURIB', 4, '1997-03-04', '1997-04-01', '1997-03-14', 2, 89, 'Furia Bacalhau e Frutos do Mar', 'Jardim das rosas n. 32', 'Lisboa', NULL, '1675', 'Portugal'); -INSERT INTO orders VALUES (10465, 'VAFFE', 1, '1997-03-05', '1997-04-02', '1997-03-14', 3, 145.039993, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10466, 'COMMI', 4, '1997-03-06', '1997-04-03', '1997-03-13', 1, 11.9300003, 'Comércio Mineiro', 'Av. dos Lusíadas, 23', 'Sao Paulo', 'SP', '05432-043', 'Brazil'); -INSERT INTO orders VALUES (10467, 'MAGAA', 8, '1997-03-06', '1997-04-03', '1997-03-11', 2, 4.92999983, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10468, 'KOENE', 3, '1997-03-07', '1997-04-04', '1997-03-12', 3, 44.1199989, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10469, 'WHITC', 1, '1997-03-10', '1997-04-07', '1997-03-14', 1, 60.1800003, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10470, 'BONAP', 4, '1997-03-11', '1997-04-08', '1997-03-14', 2, 64.5599976, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10471, 'BSBEV', 2, '1997-03-11', '1997-04-08', '1997-03-18', 3, 45.5900002, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (10472, 'SEVES', 8, '1997-03-12', '1997-04-09', '1997-03-19', 1, 4.19999981, 'Seven Seas Imports', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK'); -INSERT INTO orders VALUES (10473, 'ISLAT', 1, '1997-03-13', '1997-03-27', '1997-03-21', 3, 16.3700008, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10474, 'PERIC', 5, '1997-03-13', '1997-04-10', '1997-03-21', 2, 83.4899979, 'Pericles Comidas clásicas', 'Calle Dr. Jorge Cash 321', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10475, 'SUPRD', 9, '1997-03-14', '1997-04-11', '1997-04-04', 1, 68.5199966, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10476, 'HILAA', 8, '1997-03-17', '1997-04-14', '1997-03-24', 3, 4.40999985, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10477, 'PRINI', 5, '1997-03-17', '1997-04-14', '1997-03-25', 2, 13.0200005, 'Princesa Isabel Vinhos', 'Estrada da saúde n. 58', 'Lisboa', NULL, '1756', 'Portugal'); -INSERT INTO orders VALUES (10478, 'VICTE', 2, '1997-03-18', '1997-04-01', '1997-03-26', 3, 4.80999994, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10479, 'RATTC', 3, '1997-03-19', '1997-04-16', '1997-03-21', 3, 708.950012, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10480, 'FOLIG', 6, '1997-03-20', '1997-04-17', '1997-03-24', 2, 1.35000002, 'Folies gourmandes', '184, chaussée de Tournai', 'Lille', NULL, '59000', 'France'); -INSERT INTO orders VALUES (10481, 'RICAR', 8, '1997-03-20', '1997-04-17', '1997-03-25', 2, 64.3300018, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10482, 'LAZYK', 1, '1997-03-21', '1997-04-18', '1997-04-10', 3, 7.48000002, 'Lazy K Kountry Store', '12 Orchestra Terrace', 'Walla Walla', 'WA', '99362', 'USA'); -INSERT INTO orders VALUES (10483, 'WHITC', 7, '1997-03-24', '1997-04-21', '1997-04-25', 2, 15.2799997, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10484, 'BSBEV', 3, '1997-03-24', '1997-04-21', '1997-04-01', 3, 6.88000011, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (10485, 'LINOD', 4, '1997-03-25', '1997-04-08', '1997-03-31', 2, 64.4499969, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10486, 'HILAA', 1, '1997-03-26', '1997-04-23', '1997-04-02', 2, 30.5300007, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10487, 'QUEEN', 2, '1997-03-26', '1997-04-23', '1997-03-28', 2, 71.0699997, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10488, 'FRANK', 8, '1997-03-27', '1997-04-24', '1997-04-02', 2, 4.92999983, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10489, 'PICCO', 6, '1997-03-28', '1997-04-25', '1997-04-09', 2, 5.28999996, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (10490, 'HILAA', 7, '1997-03-31', '1997-04-28', '1997-04-03', 2, 210.190002, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10491, 'FURIB', 8, '1997-03-31', '1997-04-28', '1997-04-08', 3, 16.9599991, 'Furia Bacalhau e Frutos do Mar', 'Jardim das rosas n. 32', 'Lisboa', NULL, '1675', 'Portugal'); -INSERT INTO orders VALUES (10492, 'BOTTM', 3, '1997-04-01', '1997-04-29', '1997-04-11', 1, 62.8899994, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10493, 'LAMAI', 4, '1997-04-02', '1997-04-30', '1997-04-10', 3, 10.6400003, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10494, 'COMMI', 4, '1997-04-02', '1997-04-30', '1997-04-09', 2, 65.9899979, 'Comércio Mineiro', 'Av. dos Lusíadas, 23', 'Sao Paulo', 'SP', '05432-043', 'Brazil'); -INSERT INTO orders VALUES (10495, 'LAUGB', 3, '1997-04-03', '1997-05-01', '1997-04-11', 3, 4.6500001, 'Laughing Bacchus Wine Cellars', '2319 Elm St.', 'Vancouver', 'BC', 'V3F 2K1', 'Canada'); -INSERT INTO orders VALUES (10496, 'TRADH', 7, '1997-04-04', '1997-05-02', '1997-04-07', 2, 46.7700005, 'Tradiçao Hipermercados', 'Av. Inês de Castro, 414', 'Sao Paulo', 'SP', '05634-030', 'Brazil'); -INSERT INTO orders VALUES (10497, 'LEHMS', 7, '1997-04-04', '1997-05-02', '1997-04-07', 1, 36.2099991, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10498, 'HILAA', 8, '1997-04-07', '1997-05-05', '1997-04-11', 2, 29.75, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10499, 'LILAS', 4, '1997-04-08', '1997-05-06', '1997-04-16', 2, 102.019997, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10500, 'LAMAI', 6, '1997-04-09', '1997-05-07', '1997-04-17', 1, 42.6800003, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10501, 'BLAUS', 9, '1997-04-09', '1997-05-07', '1997-04-16', 3, 8.85000038, 'Blauer See Delikatessen', 'Forsterstr. 57', 'Mannheim', NULL, '68306', 'Germany'); -INSERT INTO orders VALUES (10502, 'PERIC', 2, '1997-04-10', '1997-05-08', '1997-04-29', 1, 69.3199997, 'Pericles Comidas clásicas', 'Calle Dr. Jorge Cash 321', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10503, 'HUNGO', 6, '1997-04-11', '1997-05-09', '1997-04-16', 2, 16.7399998, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10504, 'WHITC', 4, '1997-04-11', '1997-05-09', '1997-04-18', 3, 59.1300011, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10505, 'MEREP', 3, '1997-04-14', '1997-05-12', '1997-04-21', 3, 7.13000011, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10506, 'KOENE', 9, '1997-04-15', '1997-05-13', '1997-05-02', 2, 21.1900005, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10507, 'ANTON', 7, '1997-04-15', '1997-05-13', '1997-04-22', 1, 47.4500008, 'Antonio Moreno Taquería', 'Mataderos 2312', 'México D.F.', NULL, '05023', 'Mexico'); -INSERT INTO orders VALUES (10508, 'OTTIK', 1, '1997-04-16', '1997-05-14', '1997-05-13', 2, 4.98999977, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (10509, 'BLAUS', 4, '1997-04-17', '1997-05-15', '1997-04-29', 1, 0.150000006, 'Blauer See Delikatessen', 'Forsterstr. 57', 'Mannheim', NULL, '68306', 'Germany'); -INSERT INTO orders VALUES (10510, 'SAVEA', 6, '1997-04-18', '1997-05-16', '1997-04-28', 3, 367.630005, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10511, 'BONAP', 4, '1997-04-18', '1997-05-16', '1997-04-21', 3, 350.640015, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10512, 'FAMIA', 7, '1997-04-21', '1997-05-19', '1997-04-24', 2, 3.52999997, 'Familia Arquibaldo', 'Rua Orós, 92', 'Sao Paulo', 'SP', '05442-030', 'Brazil'); -INSERT INTO orders VALUES (10513, 'WANDK', 7, '1997-04-22', '1997-06-03', '1997-04-28', 1, 105.650002, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (10514, 'ERNSH', 3, '1997-04-22', '1997-05-20', '1997-05-16', 2, 789.950012, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10515, 'QUICK', 2, '1997-04-23', '1997-05-07', '1997-05-23', 1, 204.470001, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10516, 'HUNGO', 2, '1997-04-24', '1997-05-22', '1997-05-01', 3, 62.7799988, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10517, 'NORTS', 3, '1997-04-24', '1997-05-22', '1997-04-29', 3, 32.0699997, 'North/South', 'South House 300 Queensbridge', 'London', NULL, 'SW7 1RZ', 'UK'); -INSERT INTO orders VALUES (10518, 'TORTU', 4, '1997-04-25', '1997-05-09', '1997-05-05', 2, 218.149994, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10519, 'CHOPS', 6, '1997-04-28', '1997-05-26', '1997-05-01', 3, 91.7600021, 'Chop-suey Chinese', 'Hauptstr. 31', 'Bern', NULL, '3012', 'Switzerland'); -INSERT INTO orders VALUES (10520, 'SANTG', 7, '1997-04-29', '1997-05-27', '1997-05-01', 1, 13.3699999, 'Santé Gourmet', 'Erling Skakkes gate 78', 'Stavern', NULL, '4110', 'Norway'); -INSERT INTO orders VALUES (10521, 'CACTU', 8, '1997-04-29', '1997-05-27', '1997-05-02', 2, 17.2199993, 'Cactus Comidas para llevar', 'Cerrito 333', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10522, 'LEHMS', 4, '1997-04-30', '1997-05-28', '1997-05-06', 1, 45.3300018, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10523, 'SEVES', 7, '1997-05-01', '1997-05-29', '1997-05-30', 2, 77.6299973, 'Seven Seas Imports', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK'); -INSERT INTO orders VALUES (10524, 'BERGS', 1, '1997-05-01', '1997-05-29', '1997-05-07', 2, 244.789993, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10525, 'BONAP', 1, '1997-05-02', '1997-05-30', '1997-05-23', 2, 11.0600004, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10526, 'WARTH', 4, '1997-05-05', '1997-06-02', '1997-05-15', 2, 58.5900002, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10527, 'QUICK', 7, '1997-05-05', '1997-06-02', '1997-05-07', 1, 41.9000015, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10528, 'GREAL', 6, '1997-05-06', '1997-05-20', '1997-05-09', 2, 3.3499999, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (10529, 'MAISD', 5, '1997-05-07', '1997-06-04', '1997-05-09', 2, 66.6900024, 'Maison Dewey', 'Rue Joseph-Bens 532', 'Bruxelles', NULL, 'B-1180', 'Belgium'); -INSERT INTO orders VALUES (10530, 'PICCO', 3, '1997-05-08', '1997-06-05', '1997-05-12', 2, 339.220001, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (10531, 'OCEAN', 7, '1997-05-08', '1997-06-05', '1997-05-19', 1, 8.11999989, 'Océano Atlántico Ltda.', 'Ing. Gustavo Moncada 8585 Piso 20-A', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10532, 'EASTC', 7, '1997-05-09', '1997-06-06', '1997-05-12', 3, 74.4599991, 'Eastern Connection', '35 King George', 'London', NULL, 'WX3 6FW', 'UK'); -INSERT INTO orders VALUES (10533, 'FOLKO', 8, '1997-05-12', '1997-06-09', '1997-05-22', 1, 188.039993, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10534, 'LEHMS', 8, '1997-05-12', '1997-06-09', '1997-05-14', 2, 27.9400005, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10535, 'ANTON', 4, '1997-05-13', '1997-06-10', '1997-05-21', 1, 15.6400003, 'Antonio Moreno Taquería', 'Mataderos 2312', 'México D.F.', NULL, '05023', 'Mexico'); -INSERT INTO orders VALUES (10536, 'LEHMS', 3, '1997-05-14', '1997-06-11', '1997-06-06', 2, 58.8800011, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10537, 'RICSU', 1, '1997-05-14', '1997-05-28', '1997-05-19', 1, 78.8499985, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (10538, 'BSBEV', 9, '1997-05-15', '1997-06-12', '1997-05-16', 3, 4.86999989, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (10539, 'BSBEV', 6, '1997-05-16', '1997-06-13', '1997-05-23', 3, 12.3599997, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (10540, 'QUICK', 3, '1997-05-19', '1997-06-16', '1997-06-13', 3, 1007.64001, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10541, 'HANAR', 2, '1997-05-19', '1997-06-16', '1997-05-29', 1, 68.6500015, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10542, 'KOENE', 1, '1997-05-20', '1997-06-17', '1997-05-26', 3, 10.9499998, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10543, 'LILAS', 8, '1997-05-21', '1997-06-18', '1997-05-23', 2, 48.1699982, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10544, 'LONEP', 4, '1997-05-21', '1997-06-18', '1997-05-30', 1, 24.9099998, 'Lonesome Pine Restaurant', '89 Chiaroscuro Rd.', 'Portland', 'OR', '97219', 'USA'); -INSERT INTO orders VALUES (10545, 'LAZYK', 8, '1997-05-22', '1997-06-19', '1997-06-26', 2, 11.9200001, 'Lazy K Kountry Store', '12 Orchestra Terrace', 'Walla Walla', 'WA', '99362', 'USA'); -INSERT INTO orders VALUES (10546, 'VICTE', 1, '1997-05-23', '1997-06-20', '1997-05-27', 3, 194.720001, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10547, 'SEVES', 3, '1997-05-23', '1997-06-20', '1997-06-02', 2, 178.429993, 'Seven Seas Imports', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK'); -INSERT INTO orders VALUES (10548, 'TOMSP', 3, '1997-05-26', '1997-06-23', '1997-06-02', 2, 1.42999995, 'Toms Spezialitäten', 'Luisenstr. 48', 'Münster', NULL, '44087', 'Germany'); -INSERT INTO orders VALUES (10549, 'QUICK', 5, '1997-05-27', '1997-06-10', '1997-05-30', 1, 171.240005, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10550, 'GODOS', 7, '1997-05-28', '1997-06-25', '1997-06-06', 3, 4.32000017, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (10551, 'FURIB', 4, '1997-05-28', '1997-07-09', '1997-06-06', 3, 72.9499969, 'Furia Bacalhau e Frutos do Mar', 'Jardim das rosas n. 32', 'Lisboa', NULL, '1675', 'Portugal'); -INSERT INTO orders VALUES (10552, 'HILAA', 2, '1997-05-29', '1997-06-26', '1997-06-05', 1, 83.2200012, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10553, 'WARTH', 2, '1997-05-30', '1997-06-27', '1997-06-03', 2, 149.490005, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10554, 'OTTIK', 4, '1997-05-30', '1997-06-27', '1997-06-05', 3, 120.970001, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (10555, 'SAVEA', 6, '1997-06-02', '1997-06-30', '1997-06-04', 3, 252.490005, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10556, 'SIMOB', 2, '1997-06-03', '1997-07-15', '1997-06-13', 1, 9.80000019, 'Simons bistro', 'Vinbæltet 34', 'Kobenhavn', NULL, '1734', 'Denmark'); -INSERT INTO orders VALUES (10557, 'LEHMS', 9, '1997-06-03', '1997-06-17', '1997-06-06', 2, 96.7200012, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10558, 'AROUT', 1, '1997-06-04', '1997-07-02', '1997-06-10', 2, 72.9700012, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10559, 'BLONP', 6, '1997-06-05', '1997-07-03', '1997-06-13', 1, 8.05000019, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10560, 'FRANK', 8, '1997-06-06', '1997-07-04', '1997-06-09', 1, 36.6500015, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10561, 'FOLKO', 2, '1997-06-06', '1997-07-04', '1997-06-09', 2, 242.210007, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10562, 'REGGC', 1, '1997-06-09', '1997-07-07', '1997-06-12', 1, 22.9500008, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10563, 'RICAR', 2, '1997-06-10', '1997-07-22', '1997-06-24', 2, 60.4300003, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10564, 'RATTC', 4, '1997-06-10', '1997-07-08', '1997-06-16', 3, 13.75, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10565, 'MEREP', 8, '1997-06-11', '1997-07-09', '1997-06-18', 2, 7.1500001, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10566, 'BLONP', 9, '1997-06-12', '1997-07-10', '1997-06-18', 1, 88.4000015, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10567, 'HUNGO', 1, '1997-06-12', '1997-07-10', '1997-06-17', 1, 33.9700012, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10568, 'GALED', 3, '1997-06-13', '1997-07-11', '1997-07-09', 3, 6.53999996, 'Galería del gastronómo', 'Rambla de Cataluña, 23', 'Barcelona', NULL, '8022', 'Spain'); -INSERT INTO orders VALUES (10569, 'RATTC', 5, '1997-06-16', '1997-07-14', '1997-07-11', 1, 58.9799995, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10570, 'MEREP', 3, '1997-06-17', '1997-07-15', '1997-06-19', 3, 188.990005, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10571, 'ERNSH', 8, '1997-06-17', '1997-07-29', '1997-07-04', 3, 26.0599995, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10572, 'BERGS', 3, '1997-06-18', '1997-07-16', '1997-06-25', 2, 116.43, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10573, 'ANTON', 7, '1997-06-19', '1997-07-17', '1997-06-20', 3, 84.8399963, 'Antonio Moreno Taquería', 'Mataderos 2312', 'México D.F.', NULL, '05023', 'Mexico'); -INSERT INTO orders VALUES (10574, 'TRAIH', 4, '1997-06-19', '1997-07-17', '1997-06-30', 2, 37.5999985, 'Trail''s Head Gourmet Provisioners', '722 DaVinci Blvd.', 'Kirkland', 'WA', '98034', 'USA'); -INSERT INTO orders VALUES (10575, 'MORGK', 5, '1997-06-20', '1997-07-04', '1997-06-30', 1, 127.339996, 'Morgenstern Gesundkost', 'Heerstr. 22', 'Leipzig', NULL, '04179', 'Germany'); -INSERT INTO orders VALUES (10576, 'TORTU', 3, '1997-06-23', '1997-07-07', '1997-06-30', 3, 18.5599995, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10577, 'TRAIH', 9, '1997-06-23', '1997-08-04', '1997-06-30', 2, 25.4099998, 'Trail''s Head Gourmet Provisioners', '722 DaVinci Blvd.', 'Kirkland', 'WA', '98034', 'USA'); -INSERT INTO orders VALUES (10578, 'BSBEV', 4, '1997-06-24', '1997-07-22', '1997-07-25', 3, 29.6000004, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (10579, 'LETSS', 1, '1997-06-25', '1997-07-23', '1997-07-04', 2, 13.7299995, 'Let''s Stop N Shop', '87 Polk St. Suite 5', 'San Francisco', 'CA', '94117', 'USA'); -INSERT INTO orders VALUES (10580, 'OTTIK', 4, '1997-06-26', '1997-07-24', '1997-07-01', 3, 75.8899994, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (10581, 'FAMIA', 3, '1997-06-26', '1997-07-24', '1997-07-02', 1, 3.00999999, 'Familia Arquibaldo', 'Rua Orós, 92', 'Sao Paulo', 'SP', '05442-030', 'Brazil'); -INSERT INTO orders VALUES (10582, 'BLAUS', 3, '1997-06-27', '1997-07-25', '1997-07-14', 2, 27.7099991, 'Blauer See Delikatessen', 'Forsterstr. 57', 'Mannheim', NULL, '68306', 'Germany'); -INSERT INTO orders VALUES (10583, 'WARTH', 2, '1997-06-30', '1997-07-28', '1997-07-04', 2, 7.28000021, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10584, 'BLONP', 4, '1997-06-30', '1997-07-28', '1997-07-04', 1, 59.1399994, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10585, 'WELLI', 7, '1997-07-01', '1997-07-29', '1997-07-10', 1, 13.4099998, 'Wellington Importadora', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil'); -INSERT INTO orders VALUES (10586, 'REGGC', 9, '1997-07-02', '1997-07-30', '1997-07-09', 1, 0.479999989, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10587, 'QUEDE', 1, '1997-07-02', '1997-07-30', '1997-07-09', 1, 62.5200005, 'Que Delícia', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil'); -INSERT INTO orders VALUES (10588, 'QUICK', 2, '1997-07-03', '1997-07-31', '1997-07-10', 3, 194.669998, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10589, 'GREAL', 8, '1997-07-04', '1997-08-01', '1997-07-14', 2, 4.42000008, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (10590, 'MEREP', 4, '1997-07-07', '1997-08-04', '1997-07-14', 3, 44.7700005, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10591, 'VAFFE', 1, '1997-07-07', '1997-07-21', '1997-07-16', 1, 55.9199982, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10592, 'LEHMS', 3, '1997-07-08', '1997-08-05', '1997-07-16', 1, 32.0999985, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10593, 'LEHMS', 7, '1997-07-09', '1997-08-06', '1997-08-13', 2, 174.199997, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10594, 'OLDWO', 3, '1997-07-09', '1997-08-06', '1997-07-16', 2, 5.23999977, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (10595, 'ERNSH', 2, '1997-07-10', '1997-08-07', '1997-07-14', 1, 96.7799988, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10596, 'WHITC', 8, '1997-07-11', '1997-08-08', '1997-08-12', 1, 16.3400002, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10597, 'PICCO', 7, '1997-07-11', '1997-08-08', '1997-07-18', 3, 35.1199989, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (10598, 'RATTC', 1, '1997-07-14', '1997-08-11', '1997-07-18', 3, 44.4199982, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10599, 'BSBEV', 6, '1997-07-15', '1997-08-26', '1997-07-21', 3, 29.9799995, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (10600, 'HUNGC', 4, '1997-07-16', '1997-08-13', '1997-07-21', 1, 45.1300011, 'Hungry Coyote Import Store', 'City Center Plaza 516 Main St.', 'Elgin', 'OR', '97827', 'USA'); -INSERT INTO orders VALUES (10601, 'HILAA', 7, '1997-07-16', '1997-08-27', '1997-07-22', 1, 58.2999992, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10602, 'VAFFE', 8, '1997-07-17', '1997-08-14', '1997-07-22', 2, 2.92000008, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10603, 'SAVEA', 8, '1997-07-18', '1997-08-15', '1997-08-08', 2, 48.7700005, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10604, 'FURIB', 1, '1997-07-18', '1997-08-15', '1997-07-29', 1, 7.46000004, 'Furia Bacalhau e Frutos do Mar', 'Jardim das rosas n. 32', 'Lisboa', NULL, '1675', 'Portugal'); -INSERT INTO orders VALUES (10605, 'MEREP', 1, '1997-07-21', '1997-08-18', '1997-07-29', 2, 379.130005, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10606, 'TRADH', 4, '1997-07-22', '1997-08-19', '1997-07-31', 3, 79.4000015, 'Tradiçao Hipermercados', 'Av. Inês de Castro, 414', 'Sao Paulo', 'SP', '05634-030', 'Brazil'); -INSERT INTO orders VALUES (10607, 'SAVEA', 5, '1997-07-22', '1997-08-19', '1997-07-25', 1, 200.240005, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10608, 'TOMSP', 4, '1997-07-23', '1997-08-20', '1997-08-01', 2, 27.7900009, 'Toms Spezialitäten', 'Luisenstr. 48', 'Münster', NULL, '44087', 'Germany'); -INSERT INTO orders VALUES (10609, 'DUMON', 7, '1997-07-24', '1997-08-21', '1997-07-30', 2, 1.85000002, 'Du monde entier', '67, rue des Cinquante Otages', 'Nantes', NULL, '44000', 'France'); -INSERT INTO orders VALUES (10610, 'LAMAI', 8, '1997-07-25', '1997-08-22', '1997-08-06', 1, 26.7800007, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10611, 'WOLZA', 6, '1997-07-25', '1997-08-22', '1997-08-01', 2, 80.6500015, 'Wolski Zajazd', 'ul. Filtrowa 68', 'Warszawa', NULL, '01-012', 'Poland'); -INSERT INTO orders VALUES (10612, 'SAVEA', 1, '1997-07-28', '1997-08-25', '1997-08-01', 2, 544.080017, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10613, 'HILAA', 4, '1997-07-29', '1997-08-26', '1997-08-01', 2, 8.10999966, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10614, 'BLAUS', 8, '1997-07-29', '1997-08-26', '1997-08-01', 3, 1.92999995, 'Blauer See Delikatessen', 'Forsterstr. 57', 'Mannheim', NULL, '68306', 'Germany'); -INSERT INTO orders VALUES (10615, 'WILMK', 2, '1997-07-30', '1997-08-27', '1997-08-06', 3, 0.75, 'Wilman Kala', 'Keskuskatu 45', 'Helsinki', NULL, '21240', 'Finland'); -INSERT INTO orders VALUES (10616, 'GREAL', 1, '1997-07-31', '1997-08-28', '1997-08-05', 2, 116.529999, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (10617, 'GREAL', 4, '1997-07-31', '1997-08-28', '1997-08-04', 2, 18.5300007, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (10618, 'MEREP', 1, '1997-08-01', '1997-09-12', '1997-08-08', 1, 154.679993, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10619, 'MEREP', 3, '1997-08-04', '1997-09-01', '1997-08-07', 3, 91.0500031, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10620, 'LAUGB', 2, '1997-08-05', '1997-09-02', '1997-08-14', 3, 0.939999998, 'Laughing Bacchus Wine Cellars', '2319 Elm St.', 'Vancouver', 'BC', 'V3F 2K1', 'Canada'); -INSERT INTO orders VALUES (10621, 'ISLAT', 4, '1997-08-05', '1997-09-02', '1997-08-11', 2, 23.7299995, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10622, 'RICAR', 4, '1997-08-06', '1997-09-03', '1997-08-11', 3, 50.9700012, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10623, 'FRANK', 8, '1997-08-07', '1997-09-04', '1997-08-12', 2, 97.1800003, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10624, 'THECR', 4, '1997-08-07', '1997-09-04', '1997-08-19', 2, 94.8000031, 'The Cracker Box', '55 Grizzly Peak Rd.', 'Butte', 'MT', '59801', 'USA'); -INSERT INTO orders VALUES (10625, 'ANATR', 3, '1997-08-08', '1997-09-05', '1997-08-14', 1, 43.9000015, 'Ana Trujillo Emparedados y helados', 'Avda. de la Constitución 2222', 'México D.F.', NULL, '05021', 'Mexico'); -INSERT INTO orders VALUES (10626, 'BERGS', 1, '1997-08-11', '1997-09-08', '1997-08-20', 2, 138.690002, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10627, 'SAVEA', 8, '1997-08-11', '1997-09-22', '1997-08-21', 3, 107.459999, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10628, 'BLONP', 4, '1997-08-12', '1997-09-09', '1997-08-20', 3, 30.3600006, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10629, 'GODOS', 4, '1997-08-12', '1997-09-09', '1997-08-20', 3, 85.4599991, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (10630, 'KOENE', 1, '1997-08-13', '1997-09-10', '1997-08-19', 2, 32.3499985, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10631, 'LAMAI', 8, '1997-08-14', '1997-09-11', '1997-08-15', 1, 0.870000005, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10632, 'WANDK', 8, '1997-08-14', '1997-09-11', '1997-08-19', 1, 41.3800011, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (10633, 'ERNSH', 7, '1997-08-15', '1997-09-12', '1997-08-18', 3, 477.899994, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10634, 'FOLIG', 4, '1997-08-15', '1997-09-12', '1997-08-21', 3, 487.380005, 'Folies gourmandes', '184, chaussée de Tournai', 'Lille', NULL, '59000', 'France'); -INSERT INTO orders VALUES (10635, 'MAGAA', 8, '1997-08-18', '1997-09-15', '1997-08-21', 3, 47.4599991, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10636, 'WARTH', 4, '1997-08-19', '1997-09-16', '1997-08-26', 1, 1.14999998, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10637, 'QUEEN', 6, '1997-08-19', '1997-09-16', '1997-08-26', 1, 201.289993, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10638, 'LINOD', 3, '1997-08-20', '1997-09-17', '1997-09-01', 1, 158.440002, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10639, 'SANTG', 7, '1997-08-20', '1997-09-17', '1997-08-27', 3, 38.6399994, 'Santé Gourmet', 'Erling Skakkes gate 78', 'Stavern', NULL, '4110', 'Norway'); -INSERT INTO orders VALUES (10640, 'WANDK', 4, '1997-08-21', '1997-09-18', '1997-08-28', 1, 23.5499992, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (10641, 'HILAA', 4, '1997-08-22', '1997-09-19', '1997-08-26', 2, 179.610001, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10642, 'SIMOB', 7, '1997-08-22', '1997-09-19', '1997-09-05', 3, 41.8899994, 'Simons bistro', 'Vinbæltet 34', 'Kobenhavn', NULL, '1734', 'Denmark'); -INSERT INTO orders VALUES (10643, 'ALFKI', 6, '1997-08-25', '1997-09-22', '1997-09-02', 1, 29.4599991, 'Alfreds Futterkiste', 'Obere Str. 57', 'Berlin', NULL, '12209', 'Germany'); -INSERT INTO orders VALUES (10644, 'WELLI', 3, '1997-08-25', '1997-09-22', '1997-09-01', 2, 0.140000001, 'Wellington Importadora', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil'); -INSERT INTO orders VALUES (10645, 'HANAR', 4, '1997-08-26', '1997-09-23', '1997-09-02', 1, 12.4099998, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10646, 'HUNGO', 9, '1997-08-27', '1997-10-08', '1997-09-03', 3, 142.330002, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10647, 'QUEDE', 4, '1997-08-27', '1997-09-10', '1997-09-03', 2, 45.5400009, 'Que Delícia', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil'); -INSERT INTO orders VALUES (10648, 'RICAR', 5, '1997-08-28', '1997-10-09', '1997-09-09', 2, 14.25, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10649, 'MAISD', 5, '1997-08-28', '1997-09-25', '1997-08-29', 3, 6.19999981, 'Maison Dewey', 'Rue Joseph-Bens 532', 'Bruxelles', NULL, 'B-1180', 'Belgium'); -INSERT INTO orders VALUES (10650, 'FAMIA', 5, '1997-08-29', '1997-09-26', '1997-09-03', 3, 176.809998, 'Familia Arquibaldo', 'Rua Orós, 92', 'Sao Paulo', 'SP', '05442-030', 'Brazil'); -INSERT INTO orders VALUES (10651, 'WANDK', 8, '1997-09-01', '1997-09-29', '1997-09-11', 2, 20.6000004, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (10652, 'GOURL', 4, '1997-09-01', '1997-09-29', '1997-09-08', 2, 7.13999987, 'Gourmet Lanchonetes', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil'); -INSERT INTO orders VALUES (10653, 'FRANK', 1, '1997-09-02', '1997-09-30', '1997-09-19', 1, 93.25, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10654, 'BERGS', 5, '1997-09-02', '1997-09-30', '1997-09-11', 1, 55.2599983, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10655, 'REGGC', 1, '1997-09-03', '1997-10-01', '1997-09-11', 2, 4.40999985, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10656, 'GREAL', 6, '1997-09-04', '1997-10-02', '1997-09-10', 1, 57.1500015, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (10657, 'SAVEA', 2, '1997-09-04', '1997-10-02', '1997-09-15', 2, 352.690002, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10658, 'QUICK', 4, '1997-09-05', '1997-10-03', '1997-09-08', 1, 364.149994, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10659, 'QUEEN', 7, '1997-09-05', '1997-10-03', '1997-09-10', 2, 105.809998, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10660, 'HUNGC', 8, '1997-09-08', '1997-10-06', '1997-10-15', 1, 111.290001, 'Hungry Coyote Import Store', 'City Center Plaza 516 Main St.', 'Elgin', 'OR', '97827', 'USA'); -INSERT INTO orders VALUES (10661, 'HUNGO', 7, '1997-09-09', '1997-10-07', '1997-09-15', 3, 17.5499992, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10662, 'LONEP', 3, '1997-09-09', '1997-10-07', '1997-09-18', 2, 1.27999997, 'Lonesome Pine Restaurant', '89 Chiaroscuro Rd.', 'Portland', 'OR', '97219', 'USA'); -INSERT INTO orders VALUES (10663, 'BONAP', 2, '1997-09-10', '1997-09-24', '1997-10-03', 2, 113.150002, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10664, 'FURIB', 1, '1997-09-10', '1997-10-08', '1997-09-19', 3, 1.26999998, 'Furia Bacalhau e Frutos do Mar', 'Jardim das rosas n. 32', 'Lisboa', NULL, '1675', 'Portugal'); -INSERT INTO orders VALUES (10665, 'LONEP', 1, '1997-09-11', '1997-10-09', '1997-09-17', 2, 26.3099995, 'Lonesome Pine Restaurant', '89 Chiaroscuro Rd.', 'Portland', 'OR', '97219', 'USA'); -INSERT INTO orders VALUES (10666, 'RICSU', 7, '1997-09-12', '1997-10-10', '1997-09-22', 2, 232.419998, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (10667, 'ERNSH', 7, '1997-09-12', '1997-10-10', '1997-09-19', 1, 78.0899963, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10668, 'WANDK', 1, '1997-09-15', '1997-10-13', '1997-09-23', 2, 47.2200012, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (10669, 'SIMOB', 2, '1997-09-15', '1997-10-13', '1997-09-22', 1, 24.3899994, 'Simons bistro', 'Vinbæltet 34', 'Kobenhavn', NULL, '1734', 'Denmark'); -INSERT INTO orders VALUES (10670, 'FRANK', 4, '1997-09-16', '1997-10-14', '1997-09-18', 1, 203.479996, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10671, 'FRANR', 1, '1997-09-17', '1997-10-15', '1997-09-24', 1, 30.3400002, 'France restauration', '54, rue Royale', 'Nantes', NULL, '44000', 'France'); -INSERT INTO orders VALUES (10672, 'BERGS', 9, '1997-09-17', '1997-10-01', '1997-09-26', 2, 95.75, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10673, 'WILMK', 2, '1997-09-18', '1997-10-16', '1997-09-19', 1, 22.7600002, 'Wilman Kala', 'Keskuskatu 45', 'Helsinki', NULL, '21240', 'Finland'); -INSERT INTO orders VALUES (10674, 'ISLAT', 4, '1997-09-18', '1997-10-16', '1997-09-30', 2, 0.899999976, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10675, 'FRANK', 5, '1997-09-19', '1997-10-17', '1997-09-23', 2, 31.8500004, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10676, 'TORTU', 2, '1997-09-22', '1997-10-20', '1997-09-29', 2, 2.00999999, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10677, 'ANTON', 1, '1997-09-22', '1997-10-20', '1997-09-26', 3, 4.03000021, 'Antonio Moreno Taquería', 'Mataderos 2312', 'México D.F.', NULL, '05023', 'Mexico'); -INSERT INTO orders VALUES (10678, 'SAVEA', 7, '1997-09-23', '1997-10-21', '1997-10-16', 3, 388.980011, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10679, 'BLONP', 8, '1997-09-23', '1997-10-21', '1997-09-30', 3, 27.9400005, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10680, 'OLDWO', 1, '1997-09-24', '1997-10-22', '1997-09-26', 1, 26.6100006, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (10681, 'GREAL', 3, '1997-09-25', '1997-10-23', '1997-09-30', 3, 76.1299973, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (10682, 'ANTON', 3, '1997-09-25', '1997-10-23', '1997-10-01', 2, 36.1300011, 'Antonio Moreno Taquería', 'Mataderos 2312', 'México D.F.', NULL, '05023', 'Mexico'); -INSERT INTO orders VALUES (10683, 'DUMON', 2, '1997-09-26', '1997-10-24', '1997-10-01', 1, 4.4000001, 'Du monde entier', '67, rue des Cinquante Otages', 'Nantes', NULL, '44000', 'France'); -INSERT INTO orders VALUES (10684, 'OTTIK', 3, '1997-09-26', '1997-10-24', '1997-09-30', 1, 145.630005, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (10685, 'GOURL', 4, '1997-09-29', '1997-10-13', '1997-10-03', 2, 33.75, 'Gourmet Lanchonetes', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil'); -INSERT INTO orders VALUES (10686, 'PICCO', 2, '1997-09-30', '1997-10-28', '1997-10-08', 1, 96.5, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (10687, 'HUNGO', 9, '1997-09-30', '1997-10-28', '1997-10-30', 2, 296.429993, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10688, 'VAFFE', 4, '1997-10-01', '1997-10-15', '1997-10-07', 2, 299.089996, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10689, 'BERGS', 1, '1997-10-01', '1997-10-29', '1997-10-07', 2, 13.4200001, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10690, 'HANAR', 1, '1997-10-02', '1997-10-30', '1997-10-03', 1, 15.8000002, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10691, 'QUICK', 2, '1997-10-03', '1997-11-14', '1997-10-22', 2, 810.049988, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10692, 'ALFKI', 4, '1997-10-03', '1997-10-31', '1997-10-13', 2, 61.0200005, 'Alfred''s Futterkiste', 'Obere Str. 57', 'Berlin', NULL, '12209', 'Germany'); -INSERT INTO orders VALUES (10693, 'WHITC', 3, '1997-10-06', '1997-10-20', '1997-10-10', 3, 139.339996, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10694, 'QUICK', 8, '1997-10-06', '1997-11-03', '1997-10-09', 3, 398.359985, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10695, 'WILMK', 7, '1997-10-07', '1997-11-18', '1997-10-14', 1, 16.7199993, 'Wilman Kala', 'Keskuskatu 45', 'Helsinki', NULL, '21240', 'Finland'); -INSERT INTO orders VALUES (10696, 'WHITC', 8, '1997-10-08', '1997-11-19', '1997-10-14', 3, 102.550003, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10697, 'LINOD', 3, '1997-10-08', '1997-11-05', '1997-10-14', 1, 45.5200005, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10698, 'ERNSH', 4, '1997-10-09', '1997-11-06', '1997-10-17', 1, 272.470001, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10699, 'MORGK', 3, '1997-10-09', '1997-11-06', '1997-10-13', 3, 0.579999983, 'Morgenstern Gesundkost', 'Heerstr. 22', 'Leipzig', NULL, '04179', 'Germany'); -INSERT INTO orders VALUES (10700, 'SAVEA', 3, '1997-10-10', '1997-11-07', '1997-10-16', 1, 65.0999985, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10701, 'HUNGO', 6, '1997-10-13', '1997-10-27', '1997-10-15', 3, 220.309998, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10702, 'ALFKI', 4, '1997-10-13', '1997-11-24', '1997-10-21', 1, 23.9400005, 'Alfred''s Futterkiste', 'Obere Str. 57', 'Berlin', NULL, '12209', 'Germany'); -INSERT INTO orders VALUES (10703, 'FOLKO', 6, '1997-10-14', '1997-11-11', '1997-10-20', 2, 152.300003, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10704, 'QUEEN', 6, '1997-10-14', '1997-11-11', '1997-11-07', 1, 4.78000021, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10705, 'HILAA', 9, '1997-10-15', '1997-11-12', '1997-11-18', 2, 3.51999998, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10706, 'OLDWO', 8, '1997-10-16', '1997-11-13', '1997-10-21', 3, 135.630005, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (10707, 'AROUT', 4, '1997-10-16', '1997-10-30', '1997-10-23', 3, 21.7399998, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10708, 'THEBI', 6, '1997-10-17', '1997-11-28', '1997-11-05', 2, 2.96000004, 'The Big Cheese', '89 Jefferson Way Suite 2', 'Portland', 'OR', '97201', 'USA'); -INSERT INTO orders VALUES (10709, 'GOURL', 1, '1997-10-17', '1997-11-14', '1997-11-20', 3, 210.800003, 'Gourmet Lanchonetes', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil'); -INSERT INTO orders VALUES (10710, 'FRANS', 1, '1997-10-20', '1997-11-17', '1997-10-23', 1, 4.98000002, 'Franchi S.p.A.', 'Via Monte Bianco 34', 'Torino', NULL, '10100', 'Italy'); -INSERT INTO orders VALUES (10711, 'SAVEA', 5, '1997-10-21', '1997-12-02', '1997-10-29', 2, 52.4099998, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10712, 'HUNGO', 3, '1997-10-21', '1997-11-18', '1997-10-31', 1, 89.9300003, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10713, 'SAVEA', 1, '1997-10-22', '1997-11-19', '1997-10-24', 1, 167.050003, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10714, 'SAVEA', 5, '1997-10-22', '1997-11-19', '1997-10-27', 3, 24.4899998, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10715, 'BONAP', 3, '1997-10-23', '1997-11-06', '1997-10-29', 1, 63.2000008, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10716, 'RANCH', 4, '1997-10-24', '1997-11-21', '1997-10-27', 2, 22.5699997, 'Rancho grande', 'Av. del Libertador 900', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10717, 'FRANK', 1, '1997-10-24', '1997-11-21', '1997-10-29', 2, 59.25, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10718, 'KOENE', 1, '1997-10-27', '1997-11-24', '1997-10-29', 3, 170.880005, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10719, 'LETSS', 8, '1997-10-27', '1997-11-24', '1997-11-05', 2, 51.4399986, 'Let''s Stop N Shop', '87 Polk St. Suite 5', 'San Francisco', 'CA', '94117', 'USA'); -INSERT INTO orders VALUES (10720, 'QUEDE', 8, '1997-10-28', '1997-11-11', '1997-11-05', 2, 9.52999973, 'Que Delícia', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil'); -INSERT INTO orders VALUES (10721, 'QUICK', 5, '1997-10-29', '1997-11-26', '1997-10-31', 3, 48.9199982, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10722, 'SAVEA', 8, '1997-10-29', '1997-12-10', '1997-11-04', 1, 74.5800018, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10723, 'WHITC', 3, '1997-10-30', '1997-11-27', '1997-11-25', 1, 21.7199993, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10724, 'MEREP', 8, '1997-10-30', '1997-12-11', '1997-11-05', 2, 57.75, 'Mère Paillarde', '43 rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada'); -INSERT INTO orders VALUES (10725, 'FAMIA', 4, '1997-10-31', '1997-11-28', '1997-11-05', 3, 10.8299999, 'Familia Arquibaldo', 'Rua Orós, 92', 'Sao Paulo', 'SP', '05442-030', 'Brazil'); -INSERT INTO orders VALUES (10726, 'EASTC', 4, '1997-11-03', '1997-11-17', '1997-12-05', 1, 16.5599995, 'Eastern Connection', '35 King George', 'London', NULL, 'WX3 6FW', 'UK'); -INSERT INTO orders VALUES (10727, 'REGGC', 2, '1997-11-03', '1997-12-01', '1997-12-05', 1, 89.9000015, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10728, 'QUEEN', 4, '1997-11-04', '1997-12-02', '1997-11-11', 2, 58.3300018, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10729, 'LINOD', 8, '1997-11-04', '1997-12-16', '1997-11-14', 3, 141.059998, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10730, 'BONAP', 5, '1997-11-05', '1997-12-03', '1997-11-14', 1, 20.1200008, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10731, 'CHOPS', 7, '1997-11-06', '1997-12-04', '1997-11-14', 1, 96.6500015, 'Chop-suey Chinese', 'Hauptstr. 31', 'Bern', NULL, '3012', 'Switzerland'); -INSERT INTO orders VALUES (10732, 'BONAP', 3, '1997-11-06', '1997-12-04', '1997-11-07', 1, 16.9699993, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10733, 'BERGS', 1, '1997-11-07', '1997-12-05', '1997-11-10', 3, 110.110001, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10734, 'GOURL', 2, '1997-11-07', '1997-12-05', '1997-11-12', 3, 1.63, 'Gourmet Lanchonetes', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil'); -INSERT INTO orders VALUES (10735, 'LETSS', 6, '1997-11-10', '1997-12-08', '1997-11-21', 2, 45.9700012, 'Let''s Stop N Shop', '87 Polk St. Suite 5', 'San Francisco', 'CA', '94117', 'USA'); -INSERT INTO orders VALUES (10736, 'HUNGO', 9, '1997-11-11', '1997-12-09', '1997-11-21', 2, 44.0999985, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10737, 'VINET', 2, '1997-11-11', '1997-12-09', '1997-11-18', 2, 7.78999996, 'Vins et alcools Chevalier', '59 rue de l''Abbaye', 'Reims', NULL, '51100', 'France'); -INSERT INTO orders VALUES (10738, 'SPECD', 2, '1997-11-12', '1997-12-10', '1997-11-18', 1, 2.91000009, 'Spécialités du monde', '25, rue Lauriston', 'Paris', NULL, '75016', 'France'); -INSERT INTO orders VALUES (10739, 'VINET', 3, '1997-11-12', '1997-12-10', '1997-11-17', 3, 11.0799999, 'Vins et alcools Chevalier', '59 rue de l''Abbaye', 'Reims', NULL, '51100', 'France'); -INSERT INTO orders VALUES (10740, 'WHITC', 4, '1997-11-13', '1997-12-11', '1997-11-25', 2, 81.8799973, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10741, 'AROUT', 4, '1997-11-14', '1997-11-28', '1997-11-18', 3, 10.96, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10742, 'BOTTM', 3, '1997-11-14', '1997-12-12', '1997-11-18', 3, 243.729996, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10743, 'AROUT', 1, '1997-11-17', '1997-12-15', '1997-11-21', 2, 23.7199993, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10744, 'VAFFE', 6, '1997-11-17', '1997-12-15', '1997-11-24', 1, 69.1900024, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10745, 'QUICK', 9, '1997-11-18', '1997-12-16', '1997-11-27', 1, 3.51999998, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10746, 'CHOPS', 1, '1997-11-19', '1997-12-17', '1997-11-21', 3, 31.4300003, 'Chop-suey Chinese', 'Hauptstr. 31', 'Bern', NULL, '3012', 'Switzerland'); -INSERT INTO orders VALUES (10747, 'PICCO', 6, '1997-11-19', '1997-12-17', '1997-11-26', 1, 117.330002, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (10748, 'SAVEA', 3, '1997-11-20', '1997-12-18', '1997-11-28', 1, 232.550003, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10749, 'ISLAT', 4, '1997-11-20', '1997-12-18', '1997-12-19', 2, 61.5299988, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10750, 'WARTH', 9, '1997-11-21', '1997-12-19', '1997-11-24', 1, 79.3000031, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10751, 'RICSU', 3, '1997-11-24', '1997-12-22', '1997-12-03', 3, 130.789993, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (10752, 'NORTS', 2, '1997-11-24', '1997-12-22', '1997-11-28', 3, 1.38999999, 'North/South', 'South House 300 Queensbridge', 'London', NULL, 'SW7 1RZ', 'UK'); -INSERT INTO orders VALUES (10753, 'FRANS', 3, '1997-11-25', '1997-12-23', '1997-11-27', 1, 7.69999981, 'Franchi S.p.A.', 'Via Monte Bianco 34', 'Torino', NULL, '10100', 'Italy'); -INSERT INTO orders VALUES (10754, 'MAGAA', 6, '1997-11-25', '1997-12-23', '1997-11-27', 3, 2.38000011, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10755, 'BONAP', 4, '1997-11-26', '1997-12-24', '1997-11-28', 2, 16.7099991, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10756, 'SPLIR', 8, '1997-11-27', '1997-12-25', '1997-12-02', 2, 73.2099991, 'Split Rail Beer & Ale', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA'); -INSERT INTO orders VALUES (10757, 'SAVEA', 6, '1997-11-27', '1997-12-25', '1997-12-15', 1, 8.18999958, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10758, 'RICSU', 3, '1997-11-28', '1997-12-26', '1997-12-04', 3, 138.169998, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (10759, 'ANATR', 3, '1997-11-28', '1997-12-26', '1997-12-12', 3, 11.9899998, 'Ana Trujillo Emparedados y helados', 'Avda. de la Constitución 2222', 'México D.F.', NULL, '05021', 'Mexico'); -INSERT INTO orders VALUES (10760, 'MAISD', 4, '1997-12-01', '1997-12-29', '1997-12-10', 1, 155.639999, 'Maison Dewey', 'Rue Joseph-Bens 532', 'Bruxelles', NULL, 'B-1180', 'Belgium'); -INSERT INTO orders VALUES (10761, 'RATTC', 5, '1997-12-02', '1997-12-30', '1997-12-08', 2, 18.6599998, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10762, 'FOLKO', 3, '1997-12-02', '1997-12-30', '1997-12-09', 1, 328.73999, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10763, 'FOLIG', 3, '1997-12-03', '1997-12-31', '1997-12-08', 3, 37.3499985, 'Folies gourmandes', '184, chaussée de Tournai', 'Lille', NULL, '59000', 'France'); -INSERT INTO orders VALUES (10764, 'ERNSH', 6, '1997-12-03', '1997-12-31', '1997-12-08', 3, 145.449997, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10765, 'QUICK', 3, '1997-12-04', '1998-01-01', '1997-12-09', 3, 42.7400017, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10766, 'OTTIK', 4, '1997-12-05', '1998-01-02', '1997-12-09', 1, 157.550003, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (10767, 'SUPRD', 4, '1997-12-05', '1998-01-02', '1997-12-15', 3, 1.59000003, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10768, 'AROUT', 3, '1997-12-08', '1998-01-05', '1997-12-15', 2, 146.320007, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10769, 'VAFFE', 3, '1997-12-08', '1998-01-05', '1997-12-12', 1, 65.0599976, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10770, 'HANAR', 8, '1997-12-09', '1998-01-06', '1997-12-17', 3, 5.32000017, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10771, 'ERNSH', 9, '1997-12-10', '1998-01-07', '1998-01-02', 2, 11.1899996, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10772, 'LEHMS', 3, '1997-12-10', '1998-01-07', '1997-12-19', 2, 91.2799988, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10773, 'ERNSH', 1, '1997-12-11', '1998-01-08', '1997-12-16', 3, 96.4300003, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10774, 'FOLKO', 4, '1997-12-11', '1997-12-25', '1997-12-12', 1, 48.2000008, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10775, 'THECR', 7, '1997-12-12', '1998-01-09', '1997-12-26', 1, 20.25, 'The Cracker Box', '55 Grizzly Peak Rd.', 'Butte', 'MT', '59801', 'USA'); -INSERT INTO orders VALUES (10776, 'ERNSH', 1, '1997-12-15', '1998-01-12', '1997-12-18', 3, 351.529999, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10777, 'GOURL', 7, '1997-12-15', '1997-12-29', '1998-01-21', 2, 3.00999999, 'Gourmet Lanchonetes', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil'); -INSERT INTO orders VALUES (10778, 'BERGS', 3, '1997-12-16', '1998-01-13', '1997-12-24', 1, 6.78999996, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10779, 'MORGK', 3, '1997-12-16', '1998-01-13', '1998-01-14', 2, 58.1300011, 'Morgenstern Gesundkost', 'Heerstr. 22', 'Leipzig', NULL, '04179', 'Germany'); -INSERT INTO orders VALUES (10780, 'LILAS', 2, '1997-12-16', '1997-12-30', '1997-12-25', 1, 42.1300011, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10781, 'WARTH', 2, '1997-12-17', '1998-01-14', '1997-12-19', 3, 73.1600037, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (10782, 'CACTU', 9, '1997-12-17', '1998-01-14', '1997-12-22', 3, 1.10000002, 'Cactus Comidas para llevar', 'Cerrito 333', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10783, 'HANAR', 4, '1997-12-18', '1998-01-15', '1997-12-19', 2, 124.980003, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10784, 'MAGAA', 4, '1997-12-18', '1998-01-15', '1997-12-22', 3, 70.0899963, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10785, 'GROSR', 1, '1997-12-18', '1998-01-15', '1997-12-24', 3, 1.50999999, 'GROSELLA-Restaurante', '5ª Ave. Los Palos Grandes', 'Caracas', 'DF', '1081', 'Venezuela'); -INSERT INTO orders VALUES (10786, 'QUEEN', 8, '1997-12-19', '1998-01-16', '1997-12-23', 1, 110.870003, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10787, 'LAMAI', 2, '1997-12-19', '1998-01-02', '1997-12-26', 1, 249.929993, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10788, 'QUICK', 1, '1997-12-22', '1998-01-19', '1998-01-19', 2, 42.7000008, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10789, 'FOLIG', 1, '1997-12-22', '1998-01-19', '1997-12-31', 2, 100.599998, 'Folies gourmandes', '184, chaussée de Tournai', 'Lille', NULL, '59000', 'France'); -INSERT INTO orders VALUES (10790, 'GOURL', 6, '1997-12-22', '1998-01-19', '1997-12-26', 1, 28.2299995, 'Gourmet Lanchonetes', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil'); -INSERT INTO orders VALUES (10791, 'FRANK', 6, '1997-12-23', '1998-01-20', '1998-01-01', 2, 16.8500004, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10792, 'WOLZA', 1, '1997-12-23', '1998-01-20', '1997-12-31', 3, 23.7900009, 'Wolski Zajazd', 'ul. Filtrowa 68', 'Warszawa', NULL, '01-012', 'Poland'); -INSERT INTO orders VALUES (10793, 'AROUT', 3, '1997-12-24', '1998-01-21', '1998-01-08', 3, 4.51999998, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10794, 'QUEDE', 6, '1997-12-24', '1998-01-21', '1998-01-02', 1, 21.4899998, 'Que Delícia', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil'); -INSERT INTO orders VALUES (10795, 'ERNSH', 8, '1997-12-24', '1998-01-21', '1998-01-20', 2, 126.660004, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10796, 'HILAA', 3, '1997-12-25', '1998-01-22', '1998-01-14', 1, 26.5200005, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10797, 'DRACD', 7, '1997-12-25', '1998-01-22', '1998-01-05', 2, 33.3499985, 'Drachenblut Delikatessen', 'Walserweg 21', 'Aachen', NULL, '52066', 'Germany'); -INSERT INTO orders VALUES (10798, 'ISLAT', 2, '1997-12-26', '1998-01-23', '1998-01-05', 1, 2.32999992, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10799, 'KOENE', 9, '1997-12-26', '1998-02-06', '1998-01-05', 3, 30.7600002, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10800, 'SEVES', 1, '1997-12-26', '1998-01-23', '1998-01-05', 3, 137.440002, 'Seven Seas Imports', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK'); -INSERT INTO orders VALUES (10801, 'BOLID', 4, '1997-12-29', '1998-01-26', '1997-12-31', 2, 97.0899963, 'Bólido Comidas preparadas', 'C/ Araquil, 67', 'Madrid', NULL, '28023', 'Spain'); -INSERT INTO orders VALUES (10802, 'SIMOB', 4, '1997-12-29', '1998-01-26', '1998-01-02', 2, 257.26001, 'Simons bistro', 'Vinbæltet 34', 'Kobenhavn', NULL, '1734', 'Denmark'); -INSERT INTO orders VALUES (10803, 'WELLI', 4, '1997-12-30', '1998-01-27', '1998-01-06', 1, 55.2299995, 'Wellington Importadora', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil'); -INSERT INTO orders VALUES (10804, 'SEVES', 6, '1997-12-30', '1998-01-27', '1998-01-07', 2, 27.3299999, 'Seven Seas Imports', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK'); -INSERT INTO orders VALUES (10805, 'THEBI', 2, '1997-12-30', '1998-01-27', '1998-01-09', 3, 237.339996, 'The Big Cheese', '89 Jefferson Way Suite 2', 'Portland', 'OR', '97201', 'USA'); -INSERT INTO orders VALUES (10806, 'VICTE', 3, '1997-12-31', '1998-01-28', '1998-01-05', 2, 22.1100006, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10807, 'FRANS', 4, '1997-12-31', '1998-01-28', '1998-01-30', 1, 1.36000001, 'Franchi S.p.A.', 'Via Monte Bianco 34', 'Torino', NULL, '10100', 'Italy'); -INSERT INTO orders VALUES (10808, 'OLDWO', 2, '1998-01-01', '1998-01-29', '1998-01-09', 3, 45.5299988, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (10809, 'WELLI', 7, '1998-01-01', '1998-01-29', '1998-01-07', 1, 4.86999989, 'Wellington Importadora', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil'); -INSERT INTO orders VALUES (10810, 'LAUGB', 2, '1998-01-01', '1998-01-29', '1998-01-07', 3, 4.32999992, 'Laughing Bacchus Wine Cellars', '2319 Elm St.', 'Vancouver', 'BC', 'V3F 2K1', 'Canada'); -INSERT INTO orders VALUES (10811, 'LINOD', 8, '1998-01-02', '1998-01-30', '1998-01-08', 1, 31.2199993, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10812, 'REGGC', 5, '1998-01-02', '1998-01-30', '1998-01-12', 1, 59.7799988, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10813, 'RICAR', 1, '1998-01-05', '1998-02-02', '1998-01-09', 1, 47.3800011, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10814, 'VICTE', 3, '1998-01-05', '1998-02-02', '1998-01-14', 3, 130.940002, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10815, 'SAVEA', 2, '1998-01-05', '1998-02-02', '1998-01-14', 3, 14.6199999, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10816, 'GREAL', 4, '1998-01-06', '1998-02-03', '1998-02-04', 2, 719.780029, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (10817, 'KOENE', 3, '1998-01-06', '1998-01-20', '1998-01-13', 2, 306.070007, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10818, 'MAGAA', 7, '1998-01-07', '1998-02-04', '1998-01-12', 3, 65.4800034, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10819, 'CACTU', 2, '1998-01-07', '1998-02-04', '1998-01-16', 3, 19.7600002, 'Cactus Comidas para llevar', 'Cerrito 333', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10820, 'RATTC', 3, '1998-01-07', '1998-02-04', '1998-01-13', 2, 37.5200005, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10821, 'SPLIR', 1, '1998-01-08', '1998-02-05', '1998-01-15', 1, 36.6800003, 'Split Rail Beer & Ale', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA'); -INSERT INTO orders VALUES (10822, 'TRAIH', 6, '1998-01-08', '1998-02-05', '1998-01-16', 3, 7, 'Trail''s Head Gourmet Provisioners', '722 DaVinci Blvd.', 'Kirkland', 'WA', '98034', 'USA'); -INSERT INTO orders VALUES (10823, 'LILAS', 5, '1998-01-09', '1998-02-06', '1998-01-13', 2, 163.970001, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10824, 'FOLKO', 8, '1998-01-09', '1998-02-06', '1998-01-30', 1, 1.23000002, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10825, 'DRACD', 1, '1998-01-09', '1998-02-06', '1998-01-14', 1, 79.25, 'Drachenblut Delikatessen', 'Walserweg 21', 'Aachen', NULL, '52066', 'Germany'); -INSERT INTO orders VALUES (10826, 'BLONP', 6, '1998-01-12', '1998-02-09', '1998-02-06', 1, 7.09000015, 'Blondel père et fils', '24, place Kléber', 'Strasbourg', NULL, '67000', 'France'); -INSERT INTO orders VALUES (10827, 'BONAP', 1, '1998-01-12', '1998-01-26', '1998-02-06', 2, 63.5400009, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10828, 'RANCH', 9, '1998-01-13', '1998-01-27', '1998-02-04', 1, 90.8499985, 'Rancho grande', 'Av. del Libertador 900', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10829, 'ISLAT', 9, '1998-01-13', '1998-02-10', '1998-01-23', 1, 154.720001, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10830, 'TRADH', 4, '1998-01-13', '1998-02-24', '1998-01-21', 2, 81.8300018, 'Tradiçao Hipermercados', 'Av. Inês de Castro, 414', 'Sao Paulo', 'SP', '05634-030', 'Brazil'); -INSERT INTO orders VALUES (10831, 'SANTG', 3, '1998-01-14', '1998-02-11', '1998-01-23', 2, 72.1900024, 'Santé Gourmet', 'Erling Skakkes gate 78', 'Stavern', NULL, '4110', 'Norway'); -INSERT INTO orders VALUES (10832, 'LAMAI', 2, '1998-01-14', '1998-02-11', '1998-01-19', 2, 43.2599983, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10833, 'OTTIK', 6, '1998-01-15', '1998-02-12', '1998-01-23', 2, 71.4899979, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (10834, 'TRADH', 1, '1998-01-15', '1998-02-12', '1998-01-19', 3, 29.7800007, 'Tradiçao Hipermercados', 'Av. Inês de Castro, 414', 'Sao Paulo', 'SP', '05634-030', 'Brazil'); -INSERT INTO orders VALUES (10835, 'ALFKI', 1, '1998-01-15', '1998-02-12', '1998-01-21', 3, 69.5299988, 'Alfred''s Futterkiste', 'Obere Str. 57', 'Berlin', NULL, '12209', 'Germany'); -INSERT INTO orders VALUES (10836, 'ERNSH', 7, '1998-01-16', '1998-02-13', '1998-01-21', 1, 411.880005, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10837, 'BERGS', 9, '1998-01-16', '1998-02-13', '1998-01-23', 3, 13.3199997, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10838, 'LINOD', 3, '1998-01-19', '1998-02-16', '1998-01-23', 3, 59.2799988, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10839, 'TRADH', 3, '1998-01-19', '1998-02-16', '1998-01-22', 3, 35.4300003, 'Tradiçao Hipermercados', 'Av. Inês de Castro, 414', 'Sao Paulo', 'SP', '05634-030', 'Brazil'); -INSERT INTO orders VALUES (10840, 'LINOD', 4, '1998-01-19', '1998-03-02', '1998-02-16', 2, 2.71000004, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10841, 'SUPRD', 5, '1998-01-20', '1998-02-17', '1998-01-29', 2, 424.299988, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10842, 'TORTU', 1, '1998-01-20', '1998-02-17', '1998-01-29', 3, 54.4199982, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10843, 'VICTE', 4, '1998-01-21', '1998-02-18', '1998-01-26', 2, 9.26000023, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10844, 'PICCO', 8, '1998-01-21', '1998-02-18', '1998-01-26', 2, 25.2199993, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (10845, 'QUICK', 8, '1998-01-21', '1998-02-04', '1998-01-30', 1, 212.979996, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10846, 'SUPRD', 2, '1998-01-22', '1998-03-05', '1998-01-23', 3, 56.4599991, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10847, 'SAVEA', 4, '1998-01-22', '1998-02-05', '1998-02-10', 3, 487.570007, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10848, 'CONSH', 7, '1998-01-23', '1998-02-20', '1998-01-29', 2, 38.2400017, 'Consolidated Holdings', 'Berkeley Gardens 12 Brewery', 'London', NULL, 'WX1 6LT', 'UK'); -INSERT INTO orders VALUES (10849, 'KOENE', 9, '1998-01-23', '1998-02-20', '1998-01-30', 2, 0.560000002, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10850, 'VICTE', 1, '1998-01-23', '1998-03-06', '1998-01-30', 1, 49.1899986, 'Victuailles en stock', '2, rue du Commerce', 'Lyon', NULL, '69004', 'France'); -INSERT INTO orders VALUES (10851, 'RICAR', 5, '1998-01-26', '1998-02-23', '1998-02-02', 1, 160.550003, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10852, 'RATTC', 8, '1998-01-26', '1998-02-09', '1998-01-30', 1, 174.050003, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10853, 'BLAUS', 9, '1998-01-27', '1998-02-24', '1998-02-03', 2, 53.8300018, 'Blauer See Delikatessen', 'Forsterstr. 57', 'Mannheim', NULL, '68306', 'Germany'); -INSERT INTO orders VALUES (10854, 'ERNSH', 3, '1998-01-27', '1998-02-24', '1998-02-05', 2, 100.220001, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10855, 'OLDWO', 3, '1998-01-27', '1998-02-24', '1998-02-04', 1, 170.970001, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (10856, 'ANTON', 3, '1998-01-28', '1998-02-25', '1998-02-10', 2, 58.4300003, 'Antonio Moreno Taquería', 'Mataderos 2312', 'México D.F.', NULL, '05023', 'Mexico'); -INSERT INTO orders VALUES (10857, 'BERGS', 8, '1998-01-28', '1998-02-25', '1998-02-06', 2, 188.850006, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10858, 'LACOR', 2, '1998-01-29', '1998-02-26', '1998-02-03', 1, 52.5099983, 'La corne d''abondance', '67, avenue de l''Europe', 'Versailles', NULL, '78000', 'France'); -INSERT INTO orders VALUES (10859, 'FRANK', 1, '1998-01-29', '1998-02-26', '1998-02-02', 2, 76.0999985, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10860, 'FRANR', 3, '1998-01-29', '1998-02-26', '1998-02-04', 3, 19.2600002, 'France restauration', '54, rue Royale', 'Nantes', NULL, '44000', 'France'); -INSERT INTO orders VALUES (10861, 'WHITC', 4, '1998-01-30', '1998-02-27', '1998-02-17', 2, 14.9300003, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10862, 'LEHMS', 8, '1998-01-30', '1998-03-13', '1998-02-02', 2, 53.2299995, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10863, 'HILAA', 4, '1998-02-02', '1998-03-02', '1998-02-17', 2, 30.2600002, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10864, 'AROUT', 4, '1998-02-02', '1998-03-02', '1998-02-09', 2, 3.03999996, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10865, 'QUICK', 2, '1998-02-02', '1998-02-16', '1998-02-12', 1, 348.140015, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10866, 'BERGS', 5, '1998-02-03', '1998-03-03', '1998-02-12', 1, 109.110001, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10867, 'LONEP', 6, '1998-02-03', '1998-03-17', '1998-02-11', 1, 1.92999995, 'Lonesome Pine Restaurant', '89 Chiaroscuro Rd.', 'Portland', 'OR', '97219', 'USA'); -INSERT INTO orders VALUES (10868, 'QUEEN', 7, '1998-02-04', '1998-03-04', '1998-02-23', 2, 191.270004, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10869, 'SEVES', 5, '1998-02-04', '1998-03-04', '1998-02-09', 1, 143.279999, 'Seven Seas Imports', '90 Wadhurst Rd.', 'London', NULL, 'OX15 4NB', 'UK'); -INSERT INTO orders VALUES (10870, 'WOLZA', 5, '1998-02-04', '1998-03-04', '1998-02-13', 3, 12.04, 'Wolski Zajazd', 'ul. Filtrowa 68', 'Warszawa', NULL, '01-012', 'Poland'); -INSERT INTO orders VALUES (10871, 'BONAP', 9, '1998-02-05', '1998-03-05', '1998-02-10', 2, 112.269997, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10872, 'GODOS', 5, '1998-02-05', '1998-03-05', '1998-02-09', 2, 175.320007, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (10873, 'WILMK', 4, '1998-02-06', '1998-03-06', '1998-02-09', 1, 0.819999993, 'Wilman Kala', 'Keskuskatu 45', 'Helsinki', NULL, '21240', 'Finland'); -INSERT INTO orders VALUES (10874, 'GODOS', 5, '1998-02-06', '1998-03-06', '1998-02-11', 2, 19.5799999, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (10875, 'BERGS', 4, '1998-02-06', '1998-03-06', '1998-03-03', 2, 32.3699989, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10876, 'BONAP', 7, '1998-02-09', '1998-03-09', '1998-02-12', 3, 60.4199982, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10877, 'RICAR', 1, '1998-02-09', '1998-03-09', '1998-02-19', 1, 38.0600014, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (10878, 'QUICK', 4, '1998-02-10', '1998-03-10', '1998-02-12', 1, 46.6899986, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10879, 'WILMK', 3, '1998-02-10', '1998-03-10', '1998-02-12', 3, 8.5, 'Wilman Kala', 'Keskuskatu 45', 'Helsinki', NULL, '21240', 'Finland'); -INSERT INTO orders VALUES (10880, 'FOLKO', 7, '1998-02-10', '1998-03-24', '1998-02-18', 1, 88.0100021, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10881, 'CACTU', 4, '1998-02-11', '1998-03-11', '1998-02-18', 1, 2.83999991, 'Cactus Comidas para llevar', 'Cerrito 333', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10882, 'SAVEA', 4, '1998-02-11', '1998-03-11', '1998-02-20', 3, 23.1000004, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10883, 'LONEP', 8, '1998-02-12', '1998-03-12', '1998-02-20', 3, 0.529999971, 'Lonesome Pine Restaurant', '89 Chiaroscuro Rd.', 'Portland', 'OR', '97219', 'USA'); -INSERT INTO orders VALUES (10884, 'LETSS', 4, '1998-02-12', '1998-03-12', '1998-02-13', 2, 90.9700012, 'Let''s Stop N Shop', '87 Polk St. Suite 5', 'San Francisco', 'CA', '94117', 'USA'); -INSERT INTO orders VALUES (10885, 'SUPRD', 6, '1998-02-12', '1998-03-12', '1998-02-18', 3, 5.63999987, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10886, 'HANAR', 1, '1998-02-13', '1998-03-13', '1998-03-02', 1, 4.98999977, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10887, 'GALED', 8, '1998-02-13', '1998-03-13', '1998-02-16', 3, 1.25, 'Galería del gastronómo', 'Rambla de Cataluña, 23', 'Barcelona', NULL, '8022', 'Spain'); -INSERT INTO orders VALUES (10888, 'GODOS', 1, '1998-02-16', '1998-03-16', '1998-02-23', 2, 51.8699989, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (10889, 'RATTC', 9, '1998-02-16', '1998-03-16', '1998-02-23', 3, 280.609985, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10890, 'DUMON', 7, '1998-02-16', '1998-03-16', '1998-02-18', 1, 32.7599983, 'Du monde entier', '67, rue des Cinquante Otages', 'Nantes', NULL, '44000', 'France'); -INSERT INTO orders VALUES (10891, 'LEHMS', 7, '1998-02-17', '1998-03-17', '1998-02-19', 2, 20.3700008, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10892, 'MAISD', 4, '1998-02-17', '1998-03-17', '1998-02-19', 2, 120.269997, 'Maison Dewey', 'Rue Joseph-Bens 532', 'Bruxelles', NULL, 'B-1180', 'Belgium'); -INSERT INTO orders VALUES (10893, 'KOENE', 9, '1998-02-18', '1998-03-18', '1998-02-20', 2, 77.7799988, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (10894, 'SAVEA', 1, '1998-02-18', '1998-03-18', '1998-02-20', 1, 116.129997, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10895, 'ERNSH', 3, '1998-02-18', '1998-03-18', '1998-02-23', 1, 162.75, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10896, 'MAISD', 7, '1998-02-19', '1998-03-19', '1998-02-27', 3, 32.4500008, 'Maison Dewey', 'Rue Joseph-Bens 532', 'Bruxelles', NULL, 'B-1180', 'Belgium'); -INSERT INTO orders VALUES (10897, 'HUNGO', 3, '1998-02-19', '1998-03-19', '1998-02-25', 2, 603.539978, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10898, 'OCEAN', 4, '1998-02-20', '1998-03-20', '1998-03-06', 2, 1.26999998, 'Océano Atlántico Ltda.', 'Ing. Gustavo Moncada 8585 Piso 20-A', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10899, 'LILAS', 5, '1998-02-20', '1998-03-20', '1998-02-26', 3, 1.21000004, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10900, 'WELLI', 1, '1998-02-20', '1998-03-20', '1998-03-04', 2, 1.65999997, 'Wellington Importadora', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil'); -INSERT INTO orders VALUES (10901, 'HILAA', 4, '1998-02-23', '1998-03-23', '1998-02-26', 1, 62.0900002, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10902, 'FOLKO', 1, '1998-02-23', '1998-03-23', '1998-03-03', 1, 44.1500015, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10903, 'HANAR', 3, '1998-02-24', '1998-03-24', '1998-03-04', 3, 36.7099991, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10904, 'WHITC', 3, '1998-02-24', '1998-03-24', '1998-02-27', 3, 162.949997, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (10905, 'WELLI', 9, '1998-02-24', '1998-03-24', '1998-03-06', 2, 13.7200003, 'Wellington Importadora', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil'); -INSERT INTO orders VALUES (10906, 'WOLZA', 4, '1998-02-25', '1998-03-11', '1998-03-03', 3, 26.2900009, 'Wolski Zajazd', 'ul. Filtrowa 68', 'Warszawa', NULL, '01-012', 'Poland'); -INSERT INTO orders VALUES (10907, 'SPECD', 6, '1998-02-25', '1998-03-25', '1998-02-27', 3, 9.18999958, 'Spécialités du monde', '25, rue Lauriston', 'Paris', NULL, '75016', 'France'); -INSERT INTO orders VALUES (10908, 'REGGC', 4, '1998-02-26', '1998-03-26', '1998-03-06', 2, 32.9599991, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10909, 'SANTG', 1, '1998-02-26', '1998-03-26', '1998-03-10', 2, 53.0499992, 'Santé Gourmet', 'Erling Skakkes gate 78', 'Stavern', NULL, '4110', 'Norway'); -INSERT INTO orders VALUES (10910, 'WILMK', 1, '1998-02-26', '1998-03-26', '1998-03-04', 3, 38.1100006, 'Wilman Kala', 'Keskuskatu 45', 'Helsinki', NULL, '21240', 'Finland'); -INSERT INTO orders VALUES (10911, 'GODOS', 3, '1998-02-26', '1998-03-26', '1998-03-05', 1, 38.1899986, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (10912, 'HUNGO', 2, '1998-02-26', '1998-03-26', '1998-03-18', 2, 580.909973, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10913, 'QUEEN', 4, '1998-02-26', '1998-03-26', '1998-03-04', 1, 33.0499992, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10914, 'QUEEN', 6, '1998-02-27', '1998-03-27', '1998-03-02', 1, 21.1900005, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10915, 'TORTU', 2, '1998-02-27', '1998-03-27', '1998-03-02', 2, 3.50999999, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10916, 'RANCH', 1, '1998-02-27', '1998-03-27', '1998-03-09', 2, 63.7700005, 'Rancho grande', 'Av. del Libertador 900', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10917, 'ROMEY', 4, '1998-03-02', '1998-03-30', '1998-03-11', 2, 8.28999996, 'Romero y tomillo', 'Gran Vía, 1', 'Madrid', NULL, '28001', 'Spain'); -INSERT INTO orders VALUES (10918, 'BOTTM', 3, '1998-03-02', '1998-03-30', '1998-03-11', 3, 48.8300018, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10919, 'LINOD', 2, '1998-03-02', '1998-03-30', '1998-03-04', 2, 19.7999992, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10920, 'AROUT', 4, '1998-03-03', '1998-03-31', '1998-03-09', 2, 29.6100006, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10921, 'VAFFE', 1, '1998-03-03', '1998-04-14', '1998-03-09', 1, 176.479996, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10922, 'HANAR', 5, '1998-03-03', '1998-03-31', '1998-03-05', 3, 62.7400017, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10923, 'LAMAI', 7, '1998-03-03', '1998-04-14', '1998-03-13', 3, 68.2600021, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (10924, 'BERGS', 3, '1998-03-04', '1998-04-01', '1998-04-08', 2, 151.520004, 'Berglunds snabbköp', 'Berguvsvägen 8', 'Luleå', NULL, 'S-958 22', 'Sweden'); -INSERT INTO orders VALUES (10925, 'HANAR', 3, '1998-03-04', '1998-04-01', '1998-03-13', 1, 2.26999998, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10926, 'ANATR', 4, '1998-03-04', '1998-04-01', '1998-03-11', 3, 39.9199982, 'Ana Trujillo Emparedados y helados', 'Avda. de la Constitución 2222', 'México D.F.', NULL, '05021', 'Mexico'); -INSERT INTO orders VALUES (10927, 'LACOR', 4, '1998-03-05', '1998-04-02', '1998-04-08', 1, 19.7900009, 'La corne d''abondance', '67, avenue de l''Europe', 'Versailles', NULL, '78000', 'France'); -INSERT INTO orders VALUES (10928, 'GALED', 1, '1998-03-05', '1998-04-02', '1998-03-18', 1, 1.36000001, 'Galería del gastronómo', 'Rambla de Cataluña, 23', 'Barcelona', NULL, '8022', 'Spain'); -INSERT INTO orders VALUES (10929, 'FRANK', 6, '1998-03-05', '1998-04-02', '1998-03-12', 1, 33.9300003, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (10930, 'SUPRD', 4, '1998-03-06', '1998-04-17', '1998-03-18', 3, 15.5500002, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (10931, 'RICSU', 4, '1998-03-06', '1998-03-20', '1998-03-19', 2, 13.6000004, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (10932, 'BONAP', 8, '1998-03-06', '1998-04-03', '1998-03-24', 1, 134.639999, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10933, 'ISLAT', 6, '1998-03-06', '1998-04-03', '1998-03-16', 3, 54.1500015, 'Island Trading', 'Garden House Crowther Way', 'Cowes', 'Isle of Wight', 'PO31 7PJ', 'UK'); -INSERT INTO orders VALUES (10934, 'LEHMS', 3, '1998-03-09', '1998-04-06', '1998-03-12', 3, 32.0099983, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (10935, 'WELLI', 4, '1998-03-09', '1998-04-06', '1998-03-18', 3, 47.5900002, 'Wellington Importadora', 'Rua do Mercado, 12', 'Resende', 'SP', '08737-363', 'Brazil'); -INSERT INTO orders VALUES (10936, 'GREAL', 3, '1998-03-09', '1998-04-06', '1998-03-18', 2, 33.6800003, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (10937, 'CACTU', 7, '1998-03-10', '1998-03-24', '1998-03-13', 3, 31.5100002, 'Cactus Comidas para llevar', 'Cerrito 333', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10938, 'QUICK', 3, '1998-03-10', '1998-04-07', '1998-03-16', 2, 31.8899994, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10939, 'MAGAA', 2, '1998-03-10', '1998-04-07', '1998-03-13', 2, 76.3300018, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10940, 'BONAP', 8, '1998-03-11', '1998-04-08', '1998-03-23', 3, 19.7700005, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (10941, 'SAVEA', 7, '1998-03-11', '1998-04-08', '1998-03-20', 2, 400.809998, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10942, 'REGGC', 9, '1998-03-11', '1998-04-08', '1998-03-18', 3, 17.9500008, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (10943, 'BSBEV', 4, '1998-03-11', '1998-04-08', '1998-03-19', 2, 2.17000008, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (10944, 'BOTTM', 6, '1998-03-12', '1998-03-26', '1998-03-13', 3, 52.9199982, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10945, 'MORGK', 4, '1998-03-12', '1998-04-09', '1998-03-18', 1, 10.2200003, 'Morgenstern Gesundkost', 'Heerstr. 22', 'Leipzig', NULL, '04179', 'Germany'); -INSERT INTO orders VALUES (10946, 'VAFFE', 1, '1998-03-12', '1998-04-09', '1998-03-19', 2, 27.2000008, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10947, 'BSBEV', 3, '1998-03-13', '1998-04-10', '1998-03-16', 2, 3.25999999, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (10948, 'GODOS', 3, '1998-03-13', '1998-04-10', '1998-03-19', 3, 23.3899994, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (10949, 'BOTTM', 2, '1998-03-13', '1998-04-10', '1998-03-17', 3, 74.4400024, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10950, 'MAGAA', 1, '1998-03-16', '1998-04-13', '1998-03-23', 2, 2.5, 'Magazzini Alimentari Riuniti', 'Via Ludovico il Moro 22', 'Bergamo', NULL, '24100', 'Italy'); -INSERT INTO orders VALUES (10951, 'RICSU', 9, '1998-03-16', '1998-04-27', '1998-04-07', 2, 30.8500004, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (10952, 'ALFKI', 1, '1998-03-16', '1998-04-27', '1998-03-24', 1, 40.4199982, 'Alfred''s Futterkiste', 'Obere Str. 57', 'Berlin', NULL, '12209', 'Germany'); -INSERT INTO orders VALUES (10953, 'AROUT', 9, '1998-03-16', '1998-03-30', '1998-03-25', 2, 23.7199993, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (10954, 'LINOD', 5, '1998-03-17', '1998-04-28', '1998-03-20', 1, 27.9099998, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (10955, 'FOLKO', 8, '1998-03-17', '1998-04-14', '1998-03-20', 2, 3.25999999, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10956, 'BLAUS', 6, '1998-03-17', '1998-04-28', '1998-03-20', 2, 44.6500015, 'Blauer See Delikatessen', 'Forsterstr. 57', 'Mannheim', NULL, '68306', 'Germany'); -INSERT INTO orders VALUES (10957, 'HILAA', 8, '1998-03-18', '1998-04-15', '1998-03-27', 3, 105.360001, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10958, 'OCEAN', 7, '1998-03-18', '1998-04-15', '1998-03-27', 2, 49.5600014, 'Océano Atlántico Ltda.', 'Ing. Gustavo Moncada 8585 Piso 20-A', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10959, 'GOURL', 6, '1998-03-18', '1998-04-29', '1998-03-23', 2, 4.98000002, 'Gourmet Lanchonetes', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil'); -INSERT INTO orders VALUES (10960, 'HILAA', 3, '1998-03-19', '1998-04-02', '1998-04-08', 1, 2.07999992, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10961, 'QUEEN', 8, '1998-03-19', '1998-04-16', '1998-03-30', 1, 104.470001, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (10962, 'QUICK', 8, '1998-03-19', '1998-04-16', '1998-03-23', 2, 275.790009, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10963, 'FURIB', 9, '1998-03-19', '1998-04-16', '1998-03-26', 3, 2.70000005, 'Furia Bacalhau e Frutos do Mar', 'Jardim das rosas n. 32', 'Lisboa', NULL, '1675', 'Portugal'); -INSERT INTO orders VALUES (10964, 'SPECD', 3, '1998-03-20', '1998-04-17', '1998-03-24', 2, 87.3799973, 'Spécialités du monde', '25, rue Lauriston', 'Paris', NULL, '75016', 'France'); -INSERT INTO orders VALUES (10965, 'OLDWO', 6, '1998-03-20', '1998-04-17', '1998-03-30', 3, 144.380005, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (10966, 'CHOPS', 4, '1998-03-20', '1998-04-17', '1998-04-08', 1, 27.1900005, 'Chop-suey Chinese', 'Hauptstr. 31', 'Bern', NULL, '3012', 'Switzerland'); -INSERT INTO orders VALUES (10967, 'TOMSP', 2, '1998-03-23', '1998-04-20', '1998-04-02', 2, 62.2200012, 'Toms Spezialitäten', 'Luisenstr. 48', 'Münster', NULL, '44087', 'Germany'); -INSERT INTO orders VALUES (10968, 'ERNSH', 1, '1998-03-23', '1998-04-20', '1998-04-01', 3, 74.5999985, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10969, 'COMMI', 1, '1998-03-23', '1998-04-20', '1998-03-30', 2, 0.209999993, 'Comércio Mineiro', 'Av. dos Lusíadas, 23', 'Sao Paulo', 'SP', '05432-043', 'Brazil'); -INSERT INTO orders VALUES (10970, 'BOLID', 9, '1998-03-24', '1998-04-07', '1998-04-24', 1, 16.1599998, 'Bólido Comidas preparadas', 'C/ Araquil, 67', 'Madrid', NULL, '28023', 'Spain'); -INSERT INTO orders VALUES (10971, 'FRANR', 2, '1998-03-24', '1998-04-21', '1998-04-02', 2, 121.82, 'France restauration', '54, rue Royale', 'Nantes', NULL, '44000', 'France'); -INSERT INTO orders VALUES (10972, 'LACOR', 4, '1998-03-24', '1998-04-21', '1998-03-26', 2, 0.0199999996, 'La corne d''abondance', '67, avenue de l''Europe', 'Versailles', NULL, '78000', 'France'); -INSERT INTO orders VALUES (10973, 'LACOR', 6, '1998-03-24', '1998-04-21', '1998-03-27', 2, 15.1700001, 'La corne d''abondance', '67, avenue de l''Europe', 'Versailles', NULL, '78000', 'France'); -INSERT INTO orders VALUES (10974, 'SPLIR', 3, '1998-03-25', '1998-04-08', '1998-04-03', 3, 12.96, 'Split Rail Beer & Ale', 'P.O. Box 555', 'Lander', 'WY', '82520', 'USA'); -INSERT INTO orders VALUES (10975, 'BOTTM', 1, '1998-03-25', '1998-04-22', '1998-03-27', 3, 32.2700005, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10976, 'HILAA', 1, '1998-03-25', '1998-05-06', '1998-04-03', 1, 37.9700012, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (10977, 'FOLKO', 8, '1998-03-26', '1998-04-23', '1998-04-10', 3, 208.5, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10978, 'MAISD', 9, '1998-03-26', '1998-04-23', '1998-04-23', 2, 32.8199997, 'Maison Dewey', 'Rue Joseph-Bens 532', 'Bruxelles', NULL, 'B-1180', 'Belgium'); -INSERT INTO orders VALUES (10979, 'ERNSH', 8, '1998-03-26', '1998-04-23', '1998-03-31', 2, 353.070007, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10980, 'FOLKO', 4, '1998-03-27', '1998-05-08', '1998-04-17', 1, 1.25999999, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10981, 'HANAR', 1, '1998-03-27', '1998-04-24', '1998-04-02', 2, 193.369995, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (10982, 'BOTTM', 2, '1998-03-27', '1998-04-24', '1998-04-08', 1, 14.0100002, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (10983, 'SAVEA', 2, '1998-03-27', '1998-04-24', '1998-04-06', 2, 657.539978, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10984, 'SAVEA', 1, '1998-03-30', '1998-04-27', '1998-04-03', 3, 211.220001, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (10985, 'HUNGO', 2, '1998-03-30', '1998-04-27', '1998-04-02', 1, 91.5100021, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (10986, 'OCEAN', 8, '1998-03-30', '1998-04-27', '1998-04-21', 2, 217.860001, 'Océano Atlántico Ltda.', 'Ing. Gustavo Moncada 8585 Piso 20-A', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (10987, 'EASTC', 8, '1998-03-31', '1998-04-28', '1998-04-06', 1, 185.479996, 'Eastern Connection', '35 King George', 'London', NULL, 'WX3 6FW', 'UK'); -INSERT INTO orders VALUES (10988, 'RATTC', 3, '1998-03-31', '1998-04-28', '1998-04-10', 2, 61.1399994, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (10989, 'QUEDE', 2, '1998-03-31', '1998-04-28', '1998-04-02', 1, 34.7599983, 'Que Delícia', 'Rua da Panificadora, 12', 'Rio de Janeiro', 'RJ', '02389-673', 'Brazil'); -INSERT INTO orders VALUES (10990, 'ERNSH', 2, '1998-04-01', '1998-05-13', '1998-04-07', 3, 117.610001, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (10991, 'QUICK', 1, '1998-04-01', '1998-04-29', '1998-04-07', 1, 38.5099983, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10992, 'THEBI', 1, '1998-04-01', '1998-04-29', '1998-04-03', 3, 4.26999998, 'The Big Cheese', '89 Jefferson Way Suite 2', 'Portland', 'OR', '97201', 'USA'); -INSERT INTO orders VALUES (10993, 'FOLKO', 7, '1998-04-01', '1998-04-29', '1998-04-10', 3, 8.81000042, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (10994, 'VAFFE', 2, '1998-04-02', '1998-04-16', '1998-04-09', 3, 65.5299988, 'Vaffeljernet', 'Smagsloget 45', 'Århus', NULL, '8200', 'Denmark'); -INSERT INTO orders VALUES (10995, 'PERIC', 1, '1998-04-02', '1998-04-30', '1998-04-06', 3, 46, 'Pericles Comidas clásicas', 'Calle Dr. Jorge Cash 321', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (10996, 'QUICK', 4, '1998-04-02', '1998-04-30', '1998-04-10', 2, 1.12, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (10997, 'LILAS', 8, '1998-04-03', '1998-05-15', '1998-04-13', 2, 73.9100037, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (10998, 'WOLZA', 8, '1998-04-03', '1998-04-17', '1998-04-17', 2, 20.3099995, 'Wolski Zajazd', 'ul. Filtrowa 68', 'Warszawa', NULL, '01-012', 'Poland'); -INSERT INTO orders VALUES (10999, 'OTTIK', 6, '1998-04-03', '1998-05-01', '1998-04-10', 2, 96.3499985, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (11000, 'RATTC', 2, '1998-04-06', '1998-05-04', '1998-04-14', 3, 55.1199989, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); -INSERT INTO orders VALUES (11001, 'FOLKO', 2, '1998-04-06', '1998-05-04', '1998-04-14', 2, 197.300003, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (11002, 'SAVEA', 4, '1998-04-06', '1998-05-04', '1998-04-16', 1, 141.160004, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (11003, 'THECR', 3, '1998-04-06', '1998-05-04', '1998-04-08', 3, 14.9099998, 'The Cracker Box', '55 Grizzly Peak Rd.', 'Butte', 'MT', '59801', 'USA'); -INSERT INTO orders VALUES (11004, 'MAISD', 3, '1998-04-07', '1998-05-05', '1998-04-20', 1, 44.8400002, 'Maison Dewey', 'Rue Joseph-Bens 532', 'Bruxelles', NULL, 'B-1180', 'Belgium'); -INSERT INTO orders VALUES (11005, 'WILMK', 2, '1998-04-07', '1998-05-05', '1998-04-10', 1, 0.75, 'Wilman Kala', 'Keskuskatu 45', 'Helsinki', NULL, '21240', 'Finland'); -INSERT INTO orders VALUES (11006, 'GREAL', 3, '1998-04-07', '1998-05-05', '1998-04-15', 2, 25.1900005, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (11007, 'PRINI', 8, '1998-04-08', '1998-05-06', '1998-04-13', 2, 202.240005, 'Princesa Isabel Vinhos', 'Estrada da saúde n. 58', 'Lisboa', NULL, '1756', 'Portugal'); -INSERT INTO orders VALUES (11008, 'ERNSH', 7, '1998-04-08', '1998-05-06', NULL, 3, 79.4599991, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (11009, 'GODOS', 2, '1998-04-08', '1998-05-06', '1998-04-10', 1, 59.1100006, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (11010, 'REGGC', 2, '1998-04-09', '1998-05-07', '1998-04-21', 2, 28.7099991, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (11011, 'ALFKI', 3, '1998-04-09', '1998-05-07', '1998-04-13', 1, 1.21000004, 'Alfred''s Futterkiste', 'Obere Str. 57', 'Berlin', NULL, '12209', 'Germany'); -INSERT INTO orders VALUES (11012, 'FRANK', 1, '1998-04-09', '1998-04-23', '1998-04-17', 3, 242.949997, 'Frankenversand', 'Berliner Platz 43', 'München', NULL, '80805', 'Germany'); -INSERT INTO orders VALUES (11013, 'ROMEY', 2, '1998-04-09', '1998-05-07', '1998-04-10', 1, 32.9900017, 'Romero y tomillo', 'Gran Vía, 1', 'Madrid', NULL, '28001', 'Spain'); -INSERT INTO orders VALUES (11014, 'LINOD', 2, '1998-04-10', '1998-05-08', '1998-04-15', 3, 23.6000004, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (11015, 'SANTG', 2, '1998-04-10', '1998-04-24', '1998-04-20', 2, 4.61999989, 'Santé Gourmet', 'Erling Skakkes gate 78', 'Stavern', NULL, '4110', 'Norway'); -INSERT INTO orders VALUES (11016, 'AROUT', 9, '1998-04-10', '1998-05-08', '1998-04-13', 2, 33.7999992, 'Around the Horn', 'Brook Farm Stratford St. Mary', 'Colchester', 'Essex', 'CO7 6JX', 'UK'); -INSERT INTO orders VALUES (11017, 'ERNSH', 9, '1998-04-13', '1998-05-11', '1998-04-20', 2, 754.26001, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (11018, 'LONEP', 4, '1998-04-13', '1998-05-11', '1998-04-16', 2, 11.6499996, 'Lonesome Pine Restaurant', '89 Chiaroscuro Rd.', 'Portland', 'OR', '97219', 'USA'); -INSERT INTO orders VALUES (11019, 'RANCH', 6, '1998-04-13', '1998-05-11', NULL, 3, 3.17000008, 'Rancho grande', 'Av. del Libertador 900', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (11020, 'OTTIK', 2, '1998-04-14', '1998-05-12', '1998-04-16', 2, 43.2999992, 'Ottilies Käseladen', 'Mehrheimerstr. 369', 'Köln', NULL, '50739', 'Germany'); -INSERT INTO orders VALUES (11021, 'QUICK', 3, '1998-04-14', '1998-05-12', '1998-04-21', 1, 297.179993, 'QUICK-Stop', 'Taucherstraße 10', 'Cunewalde', NULL, '01307', 'Germany'); -INSERT INTO orders VALUES (11022, 'HANAR', 9, '1998-04-14', '1998-05-12', '1998-05-04', 2, 6.26999998, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (11023, 'BSBEV', 1, '1998-04-14', '1998-04-28', '1998-04-24', 2, 123.830002, 'B''s Beverages', 'Fauntleroy Circus', 'London', NULL, 'EC2 5NT', 'UK'); -INSERT INTO orders VALUES (11024, 'EASTC', 4, '1998-04-15', '1998-05-13', '1998-04-20', 1, 74.3600006, 'Eastern Connection', '35 King George', 'London', NULL, 'WX3 6FW', 'UK'); -INSERT INTO orders VALUES (11025, 'WARTH', 6, '1998-04-15', '1998-05-13', '1998-04-24', 3, 29.1700001, 'Wartian Herkku', 'Torikatu 38', 'Oulu', NULL, '90110', 'Finland'); -INSERT INTO orders VALUES (11026, 'FRANS', 4, '1998-04-15', '1998-05-13', '1998-04-28', 1, 47.0900002, 'Franchi S.p.A.', 'Via Monte Bianco 34', 'Torino', NULL, '10100', 'Italy'); -INSERT INTO orders VALUES (11027, 'BOTTM', 1, '1998-04-16', '1998-05-14', '1998-04-20', 1, 52.5200005, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (11028, 'KOENE', 2, '1998-04-16', '1998-05-14', '1998-04-22', 1, 29.5900002, 'Königlich Essen', 'Maubelstr. 90', 'Brandenburg', NULL, '14776', 'Germany'); -INSERT INTO orders VALUES (11029, 'CHOPS', 4, '1998-04-16', '1998-05-14', '1998-04-27', 1, 47.8400002, 'Chop-suey Chinese', 'Hauptstr. 31', 'Bern', NULL, '3012', 'Switzerland'); -INSERT INTO orders VALUES (11030, 'SAVEA', 7, '1998-04-17', '1998-05-15', '1998-04-27', 2, 830.75, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (11031, 'SAVEA', 6, '1998-04-17', '1998-05-15', '1998-04-24', 2, 227.220001, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (11032, 'WHITC', 2, '1998-04-17', '1998-05-15', '1998-04-23', 3, 606.190002, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (11033, 'RICSU', 7, '1998-04-17', '1998-05-15', '1998-04-23', 3, 84.7399979, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (11034, 'OLDWO', 8, '1998-04-20', '1998-06-01', '1998-04-27', 1, 40.3199997, 'Old World Delicatessen', '2743 Bering St.', 'Anchorage', 'AK', '99508', 'USA'); -INSERT INTO orders VALUES (11035, 'SUPRD', 2, '1998-04-20', '1998-05-18', '1998-04-24', 2, 0.170000002, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (11036, 'DRACD', 8, '1998-04-20', '1998-05-18', '1998-04-22', 3, 149.470001, 'Drachenblut Delikatessen', 'Walserweg 21', 'Aachen', NULL, '52066', 'Germany'); -INSERT INTO orders VALUES (11037, 'GODOS', 7, '1998-04-21', '1998-05-19', '1998-04-27', 1, 3.20000005, 'Godos Cocina Típica', 'C/ Romero, 33', 'Sevilla', NULL, '41101', 'Spain'); -INSERT INTO orders VALUES (11038, 'SUPRD', 1, '1998-04-21', '1998-05-19', '1998-04-30', 2, 29.5900002, 'Suprêmes délices', 'Boulevard Tirou, 255', 'Charleroi', NULL, 'B-6000', 'Belgium'); -INSERT INTO orders VALUES (11039, 'LINOD', 1, '1998-04-21', '1998-05-19', NULL, 2, 65, 'LINO-Delicateses', 'Ave. 5 de Mayo Porlamar', 'I. de Margarita', 'Nueva Esparta', '4980', 'Venezuela'); -INSERT INTO orders VALUES (11040, 'GREAL', 4, '1998-04-22', '1998-05-20', NULL, 3, 18.8400002, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (11041, 'CHOPS', 3, '1998-04-22', '1998-05-20', '1998-04-28', 2, 48.2200012, 'Chop-suey Chinese', 'Hauptstr. 31', 'Bern', NULL, '3012', 'Switzerland'); -INSERT INTO orders VALUES (11042, 'COMMI', 2, '1998-04-22', '1998-05-06', '1998-05-01', 1, 29.9899998, 'Comércio Mineiro', 'Av. dos Lusíadas, 23', 'Sao Paulo', 'SP', '05432-043', 'Brazil'); -INSERT INTO orders VALUES (11043, 'SPECD', 5, '1998-04-22', '1998-05-20', '1998-04-29', 2, 8.80000019, 'Spécialités du monde', '25, rue Lauriston', 'Paris', NULL, '75016', 'France'); -INSERT INTO orders VALUES (11044, 'WOLZA', 4, '1998-04-23', '1998-05-21', '1998-05-01', 1, 8.72000027, 'Wolski Zajazd', 'ul. Filtrowa 68', 'Warszawa', NULL, '01-012', 'Poland'); -INSERT INTO orders VALUES (11045, 'BOTTM', 6, '1998-04-23', '1998-05-21', NULL, 2, 70.5800018, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (11046, 'WANDK', 8, '1998-04-23', '1998-05-21', '1998-04-24', 2, 71.6399994, 'Die Wandernde Kuh', 'Adenauerallee 900', 'Stuttgart', NULL, '70563', 'Germany'); -INSERT INTO orders VALUES (11047, 'EASTC', 7, '1998-04-24', '1998-05-22', '1998-05-01', 3, 46.6199989, 'Eastern Connection', '35 King George', 'London', NULL, 'WX3 6FW', 'UK'); -INSERT INTO orders VALUES (11048, 'BOTTM', 7, '1998-04-24', '1998-05-22', '1998-04-30', 3, 24.1200008, 'Bottom-Dollar Markets', '23 Tsawassen Blvd.', 'Tsawassen', 'BC', 'T2F 8M4', 'Canada'); -INSERT INTO orders VALUES (11049, 'GOURL', 3, '1998-04-24', '1998-05-22', '1998-05-04', 1, 8.34000015, 'Gourmet Lanchonetes', 'Av. Brasil, 442', 'Campinas', 'SP', '04876-786', 'Brazil'); -INSERT INTO orders VALUES (11050, 'FOLKO', 8, '1998-04-27', '1998-05-25', '1998-05-05', 2, 59.4099998, 'Folk och fä HB', 'Åkergatan 24', 'Bräcke', NULL, 'S-844 67', 'Sweden'); -INSERT INTO orders VALUES (11051, 'LAMAI', 7, '1998-04-27', '1998-05-25', NULL, 3, 2.78999996, 'La maison d''Asie', '1 rue Alsace-Lorraine', 'Toulouse', NULL, '31000', 'France'); -INSERT INTO orders VALUES (11052, 'HANAR', 3, '1998-04-27', '1998-05-25', '1998-05-01', 1, 67.2600021, 'Hanari Carnes', 'Rua do Paço, 67', 'Rio de Janeiro', 'RJ', '05454-876', 'Brazil'); -INSERT INTO orders VALUES (11053, 'PICCO', 2, '1998-04-27', '1998-05-25', '1998-04-29', 2, 53.0499992, 'Piccolo und mehr', 'Geislweg 14', 'Salzburg', NULL, '5020', 'Austria'); -INSERT INTO orders VALUES (11054, 'CACTU', 8, '1998-04-28', '1998-05-26', NULL, 1, 0.330000013, 'Cactus Comidas para llevar', 'Cerrito 333', 'Buenos Aires', NULL, '1010', 'Argentina'); -INSERT INTO orders VALUES (11055, 'HILAA', 7, '1998-04-28', '1998-05-26', '1998-05-05', 2, 120.919998, 'HILARION-Abastos', 'Carrera 22 con Ave. Carlos Soublette #8-35', 'San Cristóbal', 'Táchira', '5022', 'Venezuela'); -INSERT INTO orders VALUES (11056, 'EASTC', 8, '1998-04-28', '1998-05-12', '1998-05-01', 2, 278.959991, 'Eastern Connection', '35 King George', 'London', NULL, 'WX3 6FW', 'UK'); -INSERT INTO orders VALUES (11057, 'NORTS', 3, '1998-04-29', '1998-05-27', '1998-05-01', 3, 4.13000011, 'North/South', 'South House 300 Queensbridge', 'London', NULL, 'SW7 1RZ', 'UK'); -INSERT INTO orders VALUES (11058, 'BLAUS', 9, '1998-04-29', '1998-05-27', NULL, 3, 31.1399994, 'Blauer See Delikatessen', 'Forsterstr. 57', 'Mannheim', NULL, '68306', 'Germany'); -INSERT INTO orders VALUES (11059, 'RICAR', 2, '1998-04-29', '1998-06-10', NULL, 2, 85.8000031, 'Ricardo Adocicados', 'Av. Copacabana, 267', 'Rio de Janeiro', 'RJ', '02389-890', 'Brazil'); -INSERT INTO orders VALUES (11060, 'FRANS', 2, '1998-04-30', '1998-05-28', '1998-05-04', 2, 10.9799995, 'Franchi S.p.A.', 'Via Monte Bianco 34', 'Torino', NULL, '10100', 'Italy'); -INSERT INTO orders VALUES (11061, 'GREAL', 4, '1998-04-30', '1998-06-11', NULL, 3, 14.0100002, 'Great Lakes Food Market', '2732 Baker Blvd.', 'Eugene', 'OR', '97403', 'USA'); -INSERT INTO orders VALUES (11062, 'REGGC', 4, '1998-04-30', '1998-05-28', NULL, 2, 29.9300003, 'Reggiani Caseifici', 'Strada Provinciale 124', 'Reggio Emilia', NULL, '42100', 'Italy'); -INSERT INTO orders VALUES (11063, 'HUNGO', 3, '1998-04-30', '1998-05-28', '1998-05-06', 2, 81.7300034, 'Hungry Owl All-Night Grocers', '8 Johnstown Road', 'Cork', 'Co. Cork', NULL, 'Ireland'); -INSERT INTO orders VALUES (11064, 'SAVEA', 1, '1998-05-01', '1998-05-29', '1998-05-04', 1, 30.0900002, 'Save-a-lot Markets', '187 Suffolk Ln.', 'Boise', 'ID', '83720', 'USA'); -INSERT INTO orders VALUES (11065, 'LILAS', 8, '1998-05-01', '1998-05-29', NULL, 1, 12.9099998, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (11066, 'WHITC', 7, '1998-05-01', '1998-05-29', '1998-05-04', 2, 44.7200012, 'White Clover Markets', '1029 - 12th Ave. S.', 'Seattle', 'WA', '98124', 'USA'); -INSERT INTO orders VALUES (11067, 'DRACD', 1, '1998-05-04', '1998-05-18', '1998-05-06', 2, 7.98000002, 'Drachenblut Delikatessen', 'Walserweg 21', 'Aachen', NULL, '52066', 'Germany'); -INSERT INTO orders VALUES (11068, 'QUEEN', 8, '1998-05-04', '1998-06-01', NULL, 2, 81.75, 'Queen Cozinha', 'Alameda dos Canàrios, 891', 'Sao Paulo', 'SP', '05487-020', 'Brazil'); -INSERT INTO orders VALUES (11069, 'TORTU', 1, '1998-05-04', '1998-06-01', '1998-05-06', 2, 15.6700001, 'Tortuga Restaurante', 'Avda. Azteca 123', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (11070, 'LEHMS', 2, '1998-05-05', '1998-06-02', NULL, 1, 136, 'Lehmanns Marktstand', 'Magazinweg 7', 'Frankfurt a.M.', NULL, '60528', 'Germany'); -INSERT INTO orders VALUES (11071, 'LILAS', 1, '1998-05-05', '1998-06-02', NULL, 1, 0.930000007, 'LILA-Supermercado', 'Carrera 52 con Ave. Bolívar #65-98 Llano Largo', 'Barquisimeto', 'Lara', '3508', 'Venezuela'); -INSERT INTO orders VALUES (11072, 'ERNSH', 4, '1998-05-05', '1998-06-02', NULL, 2, 258.640015, 'Ernst Handel', 'Kirchgasse 6', 'Graz', NULL, '8010', 'Austria'); -INSERT INTO orders VALUES (11073, 'PERIC', 2, '1998-05-05', '1998-06-02', NULL, 2, 24.9500008, 'Pericles Comidas clásicas', 'Calle Dr. Jorge Cash 321', 'México D.F.', NULL, '05033', 'Mexico'); -INSERT INTO orders VALUES (11074, 'SIMOB', 7, '1998-05-06', '1998-06-03', NULL, 2, 18.4400005, 'Simons bistro', 'Vinbæltet 34', 'Kobenhavn', NULL, '1734', 'Denmark'); -INSERT INTO orders VALUES (11075, 'RICSU', 8, '1998-05-06', '1998-06-03', NULL, 2, 6.19000006, 'Richter Supermarkt', 'Starenweg 5', 'Genève', NULL, '1204', 'Switzerland'); -INSERT INTO orders VALUES (11076, 'BONAP', 4, '1998-05-06', '1998-06-03', NULL, 2, 38.2799988, 'Bon app''', '12, rue des Bouchers', 'Marseille', NULL, '13008', 'France'); -INSERT INTO orders VALUES (11077, 'RATTC', 1, '1998-05-06', '1998-06-03', NULL, 2, 8.52999973, 'Rattlesnake Canyon Grocery', '2817 Milton Dr.', 'Albuquerque', 'NM', '87110', 'USA'); - - --- --- Data for Name: products; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO products VALUES (1, 'Chai', 8, 1, '10 boxes x 30 bags', 18, 39, 0, 10, 1); -INSERT INTO products VALUES (2, 'Chang', 1, 1, '24 - 12 oz bottles', 19, 17, 40, 25, 1); -INSERT INTO products VALUES (3, 'Aniseed Syrup', 1, 2, '12 - 550 ml bottles', 10, 13, 70, 25, 0); -INSERT INTO products VALUES (4, 'Chef Anton''s Cajun Seasoning', 2, 2, '48 - 6 oz jars', 22, 53, 0, 0, 0); -INSERT INTO products VALUES (5, 'Chef Anton''s Gumbo Mix', 2, 2, '36 boxes', 21.3500004, 0, 0, 0, 1); -INSERT INTO products VALUES (6, 'Grandma''s Boysenberry Spread', 3, 2, '12 - 8 oz jars', 25, 120, 0, 25, 0); -INSERT INTO products VALUES (7, 'Uncle Bob''s Organic Dried Pears', 3, 7, '12 - 1 lb pkgs.', 30, 15, 0, 10, 0); -INSERT INTO products VALUES (8, 'Northwoods Cranberry Sauce', 3, 2, '12 - 12 oz jars', 40, 6, 0, 0, 0); -INSERT INTO products VALUES (9, 'Mishi Kobe Niku', 4, 6, '18 - 500 g pkgs.', 97, 29, 0, 0, 1); -INSERT INTO products VALUES (10, 'Ikura', 4, 8, '12 - 200 ml jars', 31, 31, 0, 0, 0); -INSERT INTO products VALUES (11, 'Queso Cabrales', 5, 4, '1 kg pkg.', 21, 22, 30, 30, 0); -INSERT INTO products VALUES (12, 'Queso Manchego La Pastora', 5, 4, '10 - 500 g pkgs.', 38, 86, 0, 0, 0); -INSERT INTO products VALUES (13, 'Konbu', 6, 8, '2 kg box', 6, 24, 0, 5, 0); -INSERT INTO products VALUES (14, 'Tofu', 6, 7, '40 - 100 g pkgs.', 23.25, 35, 0, 0, 0); -INSERT INTO products VALUES (15, 'Genen Shouyu', 6, 2, '24 - 250 ml bottles', 13, 39, 0, 5, 0); -INSERT INTO products VALUES (16, 'Pavlova', 7, 3, '32 - 500 g boxes', 17.4500008, 29, 0, 10, 0); -INSERT INTO products VALUES (17, 'Alice Mutton', 7, 6, '20 - 1 kg tins', 39, 0, 0, 0, 1); -INSERT INTO products VALUES (18, 'Carnarvon Tigers', 7, 8, '16 kg pkg.', 62.5, 42, 0, 0, 0); -INSERT INTO products VALUES (19, 'Teatime Chocolate Biscuits', 8, 3, '10 boxes x 12 pieces', 9.19999981, 25, 0, 5, 0); -INSERT INTO products VALUES (20, 'Sir Rodney''s Marmalade', 8, 3, '30 gift boxes', 81, 40, 0, 0, 0); -INSERT INTO products VALUES (21, 'Sir Rodney''s Scones', 8, 3, '24 pkgs. x 4 pieces', 10, 3, 40, 5, 0); -INSERT INTO products VALUES (22, 'Gustaf''s Knäckebröd', 9, 5, '24 - 500 g pkgs.', 21, 104, 0, 25, 0); -INSERT INTO products VALUES (23, 'Tunnbröd', 9, 5, '12 - 250 g pkgs.', 9, 61, 0, 25, 0); -INSERT INTO products VALUES (24, 'Guaraná Fantástica', 10, 1, '12 - 355 ml cans', 4.5, 20, 0, 0, 1); -INSERT INTO products VALUES (25, 'NuNuCa Nuß-Nougat-Creme', 11, 3, '20 - 450 g glasses', 14, 76, 0, 30, 0); -INSERT INTO products VALUES (26, 'Gumbär Gummibärchen', 11, 3, '100 - 250 g bags', 31.2299995, 15, 0, 0, 0); -INSERT INTO products VALUES (27, 'Schoggi Schokolade', 11, 3, '100 - 100 g pieces', 43.9000015, 49, 0, 30, 0); 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-INSERT INTO products VALUES (59, 'Raclette Courdavault', 28, 4, '5 kg pkg.', 55, 79, 0, 0, 0); -INSERT INTO products VALUES (60, 'Camembert Pierrot', 28, 4, '15 - 300 g rounds', 34, 19, 0, 0, 0); -INSERT INTO products VALUES (61, 'Sirop d''érable', 29, 2, '24 - 500 ml bottles', 28.5, 113, 0, 25, 0); -INSERT INTO products VALUES (62, 'Tarte au sucre', 29, 3, '48 pies', 49.2999992, 17, 0, 0, 0); -INSERT INTO products VALUES (63, 'Vegie-spread', 7, 2, '15 - 625 g jars', 43.9000015, 24, 0, 5, 0); -INSERT INTO products VALUES (64, 'Wimmers gute Semmelknödel', 12, 5, '20 bags x 4 pieces', 33.25, 22, 80, 30, 0); -INSERT INTO products VALUES (65, 'Louisiana Fiery Hot Pepper Sauce', 2, 2, '32 - 8 oz bottles', 21.0499992, 76, 0, 0, 0); -INSERT INTO products VALUES (66, 'Louisiana Hot Spiced Okra', 2, 2, '24 - 8 oz jars', 17, 4, 100, 20, 0); -INSERT INTO products VALUES (67, 'Laughing Lumberjack Lager', 16, 1, '24 - 12 oz bottles', 14, 52, 0, 10, 0); -INSERT INTO products VALUES (68, 'Scottish Longbreads', 8, 3, '10 boxes x 8 pieces', 12.5, 6, 10, 15, 0); -INSERT INTO products VALUES (69, 'Gudbrandsdalsost', 15, 4, '10 kg pkg.', 36, 26, 0, 15, 0); -INSERT INTO products VALUES (70, 'Outback Lager', 7, 1, '24 - 355 ml bottles', 15, 15, 10, 30, 0); -INSERT INTO products VALUES (71, 'Flotemysost', 15, 4, '10 - 500 g pkgs.', 21.5, 26, 0, 0, 0); -INSERT INTO products VALUES (72, 'Mozzarella di Giovanni', 14, 4, '24 - 200 g pkgs.', 34.7999992, 14, 0, 0, 0); -INSERT INTO products VALUES (73, 'Röd Kaviar', 17, 8, '24 - 150 g jars', 15, 101, 0, 5, 0); -INSERT INTO products VALUES (74, 'Longlife Tofu', 4, 7, '5 kg pkg.', 10, 4, 20, 5, 0); -INSERT INTO products VALUES (75, 'Rhönbräu Klosterbier', 12, 1, '24 - 0.5 l bottles', 7.75, 125, 0, 25, 0); -INSERT INTO products VALUES (76, 'Lakkalikööri', 23, 1, '500 ml', 18, 57, 0, 20, 0); -INSERT INTO products VALUES (77, 'Original Frankfurter grüne Soße', 12, 2, '12 boxes', 13, 32, 0, 15, 0); - - --- --- Data for Name: region; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO region VALUES (1, 'Eastern'); -INSERT INTO region VALUES (2, 'Western'); -INSERT INTO region VALUES (3, 'Northern'); -INSERT INTO region VALUES (4, 'Southern'); - - --- --- Data for Name: shippers; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO shippers VALUES (1, 'Speedy Express', '(503) 555-9831'); -INSERT INTO shippers VALUES (2, 'United Package', '(503) 555-3199'); -INSERT INTO shippers VALUES (3, 'Federal Shipping', '(503) 555-9931'); -INSERT INTO shippers VALUES (4, 'Alliance Shippers', '1-800-222-0451'); -INSERT INTO shippers VALUES (5, 'UPS', '1-800-782-7892'); -INSERT INTO shippers VALUES (6, 'DHL', '1-800-225-5345'); - - --- --- Data for Name: shippers_tmp; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO shippers_tmp VALUES (1, 'Speedy Express', '(503) 555-9831'); -INSERT INTO shippers_tmp VALUES (2, 'United Package', '(503) 555-3199'); -INSERT INTO shippers_tmp VALUES (3, 'Federal Shipping', '(503) 555-9931'); -INSERT INTO shippers_tmp VALUES (4, 'Alliance Shippers', '1-800-222-0451'); -INSERT INTO shippers_tmp VALUES (5, 'UPS', '1-800-782-7892'); -INSERT INTO shippers_tmp VALUES (6, 'DHL', '1-800-225-5345'); - - --- --- Data for Name: suppliers; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO suppliers VALUES (1, 'Exotic Liquids', 'Charlotte Cooper', 'Purchasing Manager', '49 Gilbert St.', 'London', NULL, 'EC1 4SD', 'UK', '(171) 555-2222', NULL, NULL); -INSERT INTO suppliers VALUES (2, 'New Orleans Cajun Delights', 'Shelley Burke', 'Order Administrator', 'P.O. Box 78934', 'New Orleans', 'LA', '70117', 'USA', '(100) 555-4822', NULL, '#CAJUN.HTM#'); -INSERT INTO suppliers VALUES (3, 'Grandma Kelly''s Homestead', 'Regina Murphy', 'Sales Representative', '707 Oxford Rd.', 'Ann Arbor', 'MI', '48104', 'USA', '(313) 555-5735', '(313) 555-3349', NULL); -INSERT INTO suppliers VALUES (4, 'Tokyo Traders', 'Yoshi Nagase', 'Marketing Manager', '9-8 Sekimai Musashino-shi', 'Tokyo', NULL, '100', 'Japan', '(03) 3555-5011', NULL, NULL); -INSERT INTO suppliers VALUES (5, 'Cooperativa de Quesos ''Las Cabras''', 'Antonio del Valle Saavedra', 'Export Administrator', 'Calle del Rosal 4', 'Oviedo', 'Asturias', '33007', 'Spain', '(98) 598 76 54', NULL, NULL); -INSERT INTO suppliers VALUES (6, 'Mayumi''s', 'Mayumi Ohno', 'Marketing Representative', '92 Setsuko Chuo-ku', 'Osaka', NULL, '545', 'Japan', '(06) 431-7877', NULL, 'Mayumi''s (on the World Wide Web)#http://www.microsoft.com/accessdev/sampleapps/mayumi.htm#'); -INSERT INTO suppliers VALUES (7, 'Pavlova, Ltd.', 'Ian Devling', 'Marketing Manager', '74 Rose St. Moonie Ponds', 'Melbourne', 'Victoria', '3058', 'Australia', '(03) 444-2343', '(03) 444-6588', NULL); -INSERT INTO suppliers VALUES (8, 'Specialty Biscuits, Ltd.', 'Peter Wilson', 'Sales Representative', '29 King''s Way', 'Manchester', NULL, 'M14 GSD', 'UK', '(161) 555-4448', NULL, NULL); -INSERT INTO suppliers VALUES (9, 'PB Knäckebröd AB', 'Lars Peterson', 'Sales Agent', 'Kaloadagatan 13', 'Göteborg', NULL, 'S-345 67', 'Sweden', '031-987 65 43', '031-987 65 91', NULL); -INSERT INTO suppliers VALUES (10, 'Refrescos Americanas LTDA', 'Carlos Diaz', 'Marketing Manager', 'Av. das Americanas 12.890', 'Sao Paulo', NULL, '5442', 'Brazil', '(11) 555 4640', NULL, NULL); -INSERT INTO suppliers VALUES (11, 'Heli Süßwaren GmbH & Co. KG', 'Petra Winkler', 'Sales Manager', 'Tiergartenstraße 5', 'Berlin', NULL, '10785', 'Germany', '(010) 9984510', NULL, NULL); -INSERT INTO suppliers VALUES (12, 'Plutzer Lebensmittelgroßmärkte AG', 'Martin Bein', 'International Marketing Mgr.', 'Bogenallee 51', 'Frankfurt', NULL, '60439', 'Germany', '(069) 992755', NULL, 'Plutzer (on the World Wide Web)#http://www.microsoft.com/accessdev/sampleapps/plutzer.htm#'); -INSERT INTO suppliers VALUES (13, 'Nord-Ost-Fisch Handelsgesellschaft mbH', 'Sven Petersen', 'Coordinator Foreign Markets', 'Frahmredder 112a', 'Cuxhaven', NULL, '27478', 'Germany', '(04721) 8713', '(04721) 8714', NULL); -INSERT INTO suppliers VALUES (14, 'Formaggi Fortini s.r.l.', 'Elio Rossi', 'Sales Representative', 'Viale Dante, 75', 'Ravenna', NULL, '48100', 'Italy', '(0544) 60323', '(0544) 60603', '#FORMAGGI.HTM#'); -INSERT INTO suppliers VALUES (15, 'Norske Meierier', 'Beate Vileid', 'Marketing Manager', 'Hatlevegen 5', 'Sandvika', NULL, '1320', 'Norway', '(0)2-953010', NULL, NULL); -INSERT INTO suppliers VALUES (16, 'Bigfoot Breweries', 'Cheryl Saylor', 'Regional Account Rep.', '3400 - 8th Avenue Suite 210', 'Bend', 'OR', '97101', 'USA', '(503) 555-9931', NULL, NULL); -INSERT INTO suppliers VALUES (17, 'Svensk Sjöföda AB', 'Michael Björn', 'Sales Representative', 'Brovallavägen 231', 'Stockholm', NULL, 'S-123 45', 'Sweden', '08-123 45 67', NULL, NULL); -INSERT INTO suppliers VALUES (18, 'Aux joyeux ecclésiastiques', 'Guylène Nodier', 'Sales Manager', '203, Rue des Francs-Bourgeois', 'Paris', NULL, '75004', 'France', '(1) 03.83.00.68', '(1) 03.83.00.62', NULL); -INSERT INTO suppliers VALUES (19, 'New England Seafood Cannery', 'Robb Merchant', 'Wholesale Account Agent', 'Order Processing Dept. 2100 Paul Revere Blvd.', 'Boston', 'MA', '02134', 'USA', '(617) 555-3267', '(617) 555-3389', NULL); -INSERT INTO suppliers VALUES (20, 'Leka Trading', 'Chandra Leka', 'Owner', '471 Serangoon Loop, Suite #402', 'Singapore', NULL, '0512', 'Singapore', '555-8787', NULL, NULL); -INSERT INTO suppliers VALUES (21, 'Lyngbysild', 'Niels Petersen', 'Sales Manager', 'Lyngbysild Fiskebakken 10', 'Lyngby', NULL, '2800', 'Denmark', '43844108', '43844115', NULL); -INSERT INTO suppliers VALUES (22, 'Zaanse Snoepfabriek', 'Dirk Luchte', 'Accounting Manager', 'Verkoop Rijnweg 22', 'Zaandam', NULL, '9999 ZZ', 'Netherlands', '(12345) 1212', '(12345) 1210', NULL); -INSERT INTO suppliers VALUES (23, 'Karkki Oy', 'Anne Heikkonen', 'Product Manager', 'Valtakatu 12', 'Lappeenranta', NULL, '53120', 'Finland', '(953) 10956', NULL, NULL); -INSERT INTO suppliers VALUES (24, 'G''day, Mate', 'Wendy Mackenzie', 'Sales Representative', '170 Prince Edward Parade Hunter''s Hill', 'Sydney', 'NSW', '2042', 'Australia', '(02) 555-5914', '(02) 555-4873', 'G''day Mate (on the World Wide Web)#http://www.microsoft.com/accessdev/sampleapps/gdaymate.htm#'); -INSERT INTO suppliers VALUES (25, 'Ma Maison', 'Jean-Guy Lauzon', 'Marketing Manager', '2960 Rue St. Laurent', 'Montréal', 'Québec', 'H1J 1C3', 'Canada', '(514) 555-9022', NULL, NULL); -INSERT INTO suppliers VALUES (26, 'Pasta Buttini s.r.l.', 'Giovanni Giudici', 'Order Administrator', 'Via dei Gelsomini, 153', 'Salerno', NULL, '84100', 'Italy', '(089) 6547665', '(089) 6547667', NULL); -INSERT INTO suppliers VALUES (27, 'Escargots Nouveaux', 'Marie Delamare', 'Sales Manager', '22, rue H. Voiron', 'Montceau', NULL, '71300', 'France', '85.57.00.07', NULL, NULL); -INSERT INTO suppliers VALUES (28, 'Gai pâturage', 'Eliane Noz', 'Sales Representative', 'Bat. B 3, rue des Alpes', 'Annecy', NULL, '74000', 'France', '38.76.98.06', '38.76.98.58', NULL); -INSERT INTO suppliers VALUES (29, 'Forêts d''érables', 'Chantal Goulet', 'Accounting Manager', '148 rue Chasseur', 'Ste-Hyacinthe', 'Québec', 'J2S 7S8', 'Canada', '(514) 555-2955', '(514) 555-2921', NULL); - - --- --- Data for Name: territories; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO territories VALUES ('01581', 'Westboro', 1); -INSERT INTO territories VALUES ('01730', 'Bedford', 1); -INSERT INTO territories VALUES ('01833', 'Georgetow', 1); -INSERT INTO territories VALUES ('02116', 'Boston', 1); -INSERT INTO territories VALUES ('02139', 'Cambridge', 1); -INSERT INTO territories VALUES ('02184', 'Braintree', 1); -INSERT INTO territories VALUES ('02903', 'Providence', 1); -INSERT INTO territories VALUES ('03049', 'Hollis', 3); -INSERT INTO territories VALUES ('03801', 'Portsmouth', 3); -INSERT INTO territories VALUES ('06897', 'Wilton', 1); -INSERT INTO territories VALUES ('07960', 'Morristown', 1); -INSERT INTO territories VALUES ('08837', 'Edison', 1); -INSERT INTO territories VALUES ('10019', 'New York', 1); -INSERT INTO territories VALUES ('10038', 'New York', 1); -INSERT INTO territories VALUES ('11747', 'Mellvile', 1); -INSERT INTO territories VALUES ('14450', 'Fairport', 1); -INSERT INTO territories VALUES ('19428', 'Philadelphia', 3); -INSERT INTO territories VALUES ('19713', 'Neward', 1); -INSERT INTO territories VALUES ('20852', 'Rockville', 1); -INSERT INTO territories VALUES ('27403', 'Greensboro', 1); -INSERT INTO territories VALUES ('27511', 'Cary', 1); -INSERT INTO territories VALUES ('29202', 'Columbia', 4); -INSERT INTO territories VALUES ('30346', 'Atlanta', 4); -INSERT INTO territories VALUES ('31406', 'Savannah', 4); -INSERT INTO territories VALUES ('32859', 'Orlando', 4); -INSERT INTO territories VALUES ('33607', 'Tampa', 4); -INSERT INTO territories VALUES ('40222', 'Louisville', 1); -INSERT INTO territories VALUES ('44122', 'Beachwood', 3); -INSERT INTO territories VALUES ('45839', 'Findlay', 3); -INSERT INTO territories VALUES ('48075', 'Southfield', 3); -INSERT INTO territories VALUES ('48084', 'Troy', 3); -INSERT INTO territories VALUES ('48304', 'Bloomfield Hills', 3); -INSERT INTO territories VALUES ('53404', 'Racine', 3); -INSERT INTO territories VALUES ('55113', 'Roseville', 3); -INSERT INTO territories VALUES ('55439', 'Minneapolis', 3); -INSERT INTO territories VALUES ('60179', 'Hoffman Estates', 2); -INSERT INTO territories VALUES ('60601', 'Chicago', 2); -INSERT INTO territories VALUES ('72716', 'Bentonville', 4); -INSERT INTO territories VALUES ('75234', 'Dallas', 4); -INSERT INTO territories VALUES ('78759', 'Austin', 4); -INSERT INTO territories VALUES ('80202', 'Denver', 2); -INSERT INTO territories VALUES ('80909', 'Colorado Springs', 2); -INSERT INTO territories VALUES ('85014', 'Phoenix', 2); -INSERT INTO territories VALUES ('85251', 'Scottsdale', 2); -INSERT INTO territories VALUES ('90405', 'Santa Monica', 2); -INSERT INTO territories VALUES ('94025', 'Menlo Park', 2); -INSERT INTO territories VALUES ('94105', 'San Francisco', 2); -INSERT INTO territories VALUES ('95008', 'Campbell', 2); -INSERT INTO territories VALUES ('95054', 'Santa Clara', 2); -INSERT INTO territories VALUES ('95060', 'Santa Cruz', 2); -INSERT INTO territories VALUES ('98004', 'Bellevue', 2); -INSERT INTO territories VALUES ('98052', 'Redmond', 2); -INSERT INTO territories VALUES ('98104', 'Seattle', 2); - - --- --- Data for Name: usstates; Type: TABLE DATA; Schema: public; Owner: postgres --- - -INSERT INTO usstates VALUES (1, 'Alabama', 'AL', 'south'); -INSERT INTO usstates VALUES (2, 'Alaska', 'AK', 'north'); -INSERT INTO usstates VALUES (3, 'Arizona', 'AZ', 'west'); -INSERT INTO usstates VALUES (4, 'Arkansas', 'AR', 'south'); -INSERT INTO usstates VALUES (5, 'California', 'CA', 'west'); -INSERT INTO usstates VALUES (6, 'Colorado', 'CO', 'west'); -INSERT INTO usstates VALUES (7, 'Connecticut', 'CT', 'east'); -INSERT INTO usstates VALUES (8, 'Delaware', 'DE', 'east'); -INSERT INTO usstates VALUES (9, 'District of Columbia', 'DC', 'east'); -INSERT INTO usstates VALUES (10, 'Florida', 'FL', 'south'); -INSERT INTO usstates VALUES (11, 'Georgia', 'GA', 'south'); -INSERT INTO usstates VALUES (12, 'Hawaii', 'HI', 'west'); -INSERT INTO usstates VALUES (13, 'Idaho', 'ID', 'midwest'); -INSERT INTO usstates VALUES (14, 'Illinois', 'IL', 'midwest'); -INSERT INTO usstates VALUES (15, 'Indiana', 'IN', 'midwest'); -INSERT INTO usstates VALUES (16, 'Iowa', 'IO', 'midwest'); -INSERT INTO usstates VALUES (17, 'Kansas', 'KS', 'midwest'); -INSERT INTO usstates VALUES (18, 'Kentucky', 'KY', 'south'); -INSERT INTO usstates VALUES (19, 'Louisiana', 'LA', 'south'); -INSERT INTO usstates VALUES (20, 'Maine', 'ME', 'north'); -INSERT INTO usstates VALUES (21, 'Maryland', 'MD', 'east'); -INSERT INTO usstates VALUES (22, 'Massachusetts', 'MA', 'north'); -INSERT INTO usstates VALUES (23, 'Michigan', 'MI', 'north'); -INSERT INTO usstates VALUES (24, 'Minnesota', 'MN', 'north'); -INSERT INTO usstates VALUES (25, 'Mississippi', 'MS', 'south'); -INSERT INTO usstates VALUES (26, 'Missouri', 'MO', 'south'); -INSERT INTO usstates VALUES (27, 'Montana', 'MT', 'west'); -INSERT INTO usstates VALUES (28, 'Nebraska', 'NE', 'midwest'); -INSERT INTO usstates VALUES (29, 'Nevada', 'NV', 'west'); -INSERT INTO usstates VALUES (30, 'New Hampshire', 'NH', 'east'); -INSERT INTO usstates VALUES (31, 'New Jersey', 'NJ', 'east'); -INSERT INTO usstates VALUES (32, 'New Mexico', 'NM', 'west'); -INSERT INTO usstates VALUES (33, 'New York', 'NY', 'east'); -INSERT INTO usstates VALUES (34, 'North Carolina', 'NC', 'east'); -INSERT INTO usstates VALUES (35, 'North Dakota', 'ND', 'midwest'); -INSERT INTO usstates VALUES (36, 'Ohio', 'OH', 'midwest'); -INSERT INTO usstates VALUES (37, 'Oklahoma', 'OK', 'midwest'); -INSERT INTO usstates VALUES (38, 'Oregon', 'OR', 'west'); -INSERT INTO usstates VALUES (39, 'Pennsylvania', 'PA', 'east'); -INSERT INTO usstates VALUES (40, 'Rhode Island', 'RI', 'east'); -INSERT INTO usstates VALUES (41, 'South Carolina', 'SC', 'east'); -INSERT INTO usstates VALUES (42, 'South Dakota', 'SD', 'midwest'); -INSERT INTO usstates VALUES (43, 'Tennessee', 'TN', 'midwest'); -INSERT INTO usstates VALUES (44, 'Texas', 'TX', 'west'); -INSERT INTO usstates VALUES (45, 'Utah', 'UT', 'west'); -INSERT INTO usstates VALUES (46, 'Vermont', 'VT', 'east'); -INSERT INTO usstates VALUES (47, 'Virginia', 'VA', 'east'); -INSERT INTO usstates VALUES (48, 'Washington', 'WA', 'west'); -INSERT INTO usstates VALUES (49, 'West Virginia', 'WV', 'south'); -INSERT INTO usstates VALUES (50, 'Wisconsin', 'WI', 'midwest'); -INSERT INTO usstates VALUES (51, 'Wyoming', 'WY', 'west'); - - --- --- Name: pk_categories; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY categories - ADD CONSTRAINT pk_categories PRIMARY KEY (CategoryID); - - --- --- Name: pk_customercustomerdemo; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY customercustomerdemo - ADD CONSTRAINT pk_customercustomerdemo PRIMARY KEY (CustomerID, CustomerTypeID); - - --- --- Name: pk_customerdemographics; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY customerdemographics - ADD CONSTRAINT pk_customerdemographics PRIMARY KEY (CustomerTypeID); - - --- --- Name: pk_customers; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY customers - ADD CONSTRAINT pk_customers PRIMARY KEY (CustomerID); - - --- --- Name: pk_employees; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY employees - ADD CONSTRAINT pk_employees PRIMARY KEY (EmployeeID); - - --- --- Name: pk_employeeterritories; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY employeeterritories - ADD CONSTRAINT pk_employeeterritories PRIMARY KEY (EmployeeID, TerritoryID); - - --- --- Name: pk_order_details; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY order_details - ADD CONSTRAINT pk_order_details PRIMARY KEY (OrderID, ProductID); - - --- --- Name: pk_orders; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY orders - ADD CONSTRAINT pk_orders PRIMARY KEY (OrderID); - - --- --- Name: pk_products; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY products - ADD CONSTRAINT pk_products PRIMARY KEY (ProductID); - - --- --- Name: pk_region; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY region - ADD CONSTRAINT pk_region PRIMARY KEY (RegionID); - - --- --- Name: pk_shippers; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY shippers - ADD CONSTRAINT pk_shippers PRIMARY KEY (ShipperID); - - --- --- Name: pk_shippers_tmp; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY shippers_tmp - ADD CONSTRAINT pk_shippers_tmp PRIMARY KEY (ShipperID); - - --- --- Name: pk_suppliers; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY suppliers - ADD CONSTRAINT pk_suppliers PRIMARY KEY (SupplierID); - - --- --- Name: pk_territories; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: --- - -ALTER TABLE ONLY territories - ADD CONSTRAINT pk_territories PRIMARY KEY (TerritoryID); - - --- --- Name: public; Type: ACL; Schema: -; Owner: postgres --- - -REVOKE ALL ON SCHEMA public FROM PUBLIC; -REVOKE ALL ON SCHEMA public FROM postgres; -GRANT ALL ON SCHEMA public TO postgres; -GRANT ALL ON SCHEMA public TO PUBLIC; - - --- --- PostgreSQL database dump complete --- - diff --git a/modules/ROOT/examples/render.rb b/modules/ROOT/examples/render.rb deleted file mode 100755 index 50b53ac0..00000000 --- a/modules/ROOT/examples/render.rb +++ /dev/null @@ -1,76 +0,0 @@ -#!/usr/bin/env ruby -require 'rubygems' -require 'bundler/setup' - -require 'dotenv' -Dotenv.load - -require 'logger' -require 'ascii_press' - -LOGGER = Logger.new(STDOUT) - -require './html_transformer' # Neo Tech specific - -ASCIIDOC_TEMPLATES_DIR = ENV['ASCIIDOC_TEMPLATES_DIR'] || '_templates' -IMAGE_BASE_URL = ENV['IMAGE_BASE_URL'] || 'https://dist.neo4j.com/wp-content/uploads/' -EXAMPLES = ENV['EXAMPLES'] || 'https://github.com/neo4j-examples' -CYPHERMANUAL = ENV['CYPHERMANUAL'] || 'https://neo4j.com/docs/cypher-manual/current' -DRIVERMANUAL = ENV['DRIVERMANUAL'] || 'https://neo4j.com/docs/driver-manual/current' -OPSMANUAL = ENV['OPSMANUAL'] || 'http://neo4j.com/docs/operations-manual/current' -GITHUB = ENV['GITHUB'] || 'https://github.com/neo4j-contrib/developer-resources/tree/gh-pages' -ASCIIDOC_ATTRIBUTES = %W(allow-uri-read - icons=font - linkattrs - source-highlighter=codemirror - img=#{IMAGE_BASE_URL} - examples=#{EXAMPLES} - cyphermanual=#{CYPHERMANUAL} - drivermanual=#{DRIVERMANUAL} - opsmanual=#{OPSMANUAL} - github=#{GITHUB}) - -raise 'Usage: feed me asciidoctor files (or pass `all` to find all files)' if ARGV.empty? - -adoc_file_paths = if ARGV == ['all'] - `find . -mindepth 2 -maxdepth 4 -name "*.adoc"`.split(/[\n\r]+/) -else - ARGV -end - -# AsciiPress.verify_adoc_slugs!(adoc_file_paths) -publish = ENV['BLOG_REST_HOSTNAME'] && ENV['BLOG_REST_USERNAME'] && ENV['BLOG_REST_PASSWORD'] && ENV['PUBLISH'] - -renderer = AsciiPress::Renderer.new(after_conversion: (publish ? HtmlTransformer.method(:transform) : nil), - asciidoc_options: { - attributes: ASCIIDOC_ATTRIBUTES, - header_footer: true, - safe: 0, - template_dir: ASCIIDOC_TEMPLATES_DIR, - }) - -if publish - syncer = AsciiPress::WordPressHttpSyncer.new( - ENV['BLOG_REST_HOSTNAME'], - ENV['BLOG_REST_USERNAME'], - ENV['BLOG_REST_PASSWORD'], - "developer", - renderer, - post_status: 'publish') -end - -if syncer - puts "Syncing to WordPress" - syncer.sync(adoc_file_paths, {}) -else - FileUtils.mkdir_p('developer') - `cp -r assets developer/` - adoc_file_paths.each do |adoc_file_path| - dir = File.join('developer', File.dirname(adoc_file_path)) - FileUtils.mkdir_p(dir) - html_file_path = File.join(dir, 'index.html') - - LOGGER.info "Rendering #{adoc_file_path} to #{html_file_path}" - File.open(html_file_path, 'w') { |f| f << renderer.render(adoc_file_path).html } - end -end diff --git a/modules/ROOT/examples/render_stdout.rb b/modules/ROOT/examples/render_stdout.rb deleted file mode 100755 index 563efe83..00000000 --- a/modules/ROOT/examples/render_stdout.rb +++ /dev/null @@ -1,53 +0,0 @@ -#!ruby -require 'rubygems' -require 'bundler/setup' - -require 'dotenv' -Dotenv.load - -require 'logger' -require 'ascii_press' - -LOGGER = Logger.new(STDOUT) - -require './html_transformer' # Neo Tech specific - -ASCIIDOC_TEMPLATES_DIR = ENV['ASCIIDOC_TEMPLATES_DIR'] || '_templates' -IMAGE_BASE_URL = ENV['IMAGE_BASE_URL'] || 'https://dist.neo4j.com/wp-content/uploads/' # '.' -EXAMPLES = ENV['EXAMPLES'] || 'https://github.com/neo4j-examples' -CYPHERMANUAL = ENV['CYPHERMANUAL'] || 'https://neo4j.com/docs/cypher-manual/current' -DRIVERMANUAL = ENV['DRIVERMANUAL'] || 'https://neo4j.com/docs/driver-manual/current' -OPSMANUAL = ENV['OPSMANUAL'] || 'http://neo4j.com/docs/operations-manual/current' -GITHUB = ENV['GITHUB'] || 'https://github.com/neo4j-contrib/developer/tree/gh-pages' -ASCIIDOC_ATTRIBUTES = %W(allow-uri-read - icons=font - linkattrs - source-highlighter=codemirror - img=#{IMAGE_BASE_URL} - examples=#{EXAMPLES} - cyphermanual=#{CYPHERMANUAL} - drivermanual=#{DRIVERMANUAL} - opsmanual=#{OPSMANUAL} - github=#{GITHUB}) - -#raise 'Usage: feed me asciidoctor files' if ARGV.empty? - -adoc_file_paths = ARGV - -# AsciiPress.verify_adoc_slugs!(adoc_file_paths) - -renderer = AsciiPress::Renderer.new( # after_conversion: HtmlTransformer.method(:transform), - asciidoc_options: { - attributes: ASCIIDOC_ATTRIBUTES, - header_footer: true, - safe: 0, - template_dir: ASCIIDOC_TEMPLATES_DIR, - }) - -adoc_file_paths.each do |adoc_file_path| - dir = File.dirname(adoc_file_path) - FileUtils.mkdir_p(dir) - html_file_path = File.join(dir, 'index.html') - - $stdout.write renderer.render(adoc_file_path).html -end diff --git a/modules/ROOT/examples/robots.txt b/modules/ROOT/examples/robots.txt deleted file mode 100644 index 1f53798b..00000000 --- a/modules/ROOT/examples/robots.txt +++ /dev/null @@ -1,2 +0,0 @@ -User-agent: * -Disallow: / diff --git a/modules/ROOT/examples/social-page.rb b/modules/ROOT/examples/social-page.rb deleted file mode 100644 index 94f0671a..00000000 --- a/modules/ROOT/examples/social-page.rb +++ /dev/null @@ -1,58 +0,0 @@ - -# gem install neo4j-core -require 'neo4j-core' -session = Neo4j::Session.open(:server_db, 'http://localhost:7474', basic_auth: {username: 'neo4j', password: ''}) - -# Insert data -insert_query = """ -UNWIND {pairs} as pair -MERGE (p1:Person {name:pair[0]}) -MERGE (p2:Person {name:pair[1]}) -MERGE (p1)-[:KNOWS]-(p2); -""" - -data = [["Jim","Mike"],["Jim","Billy"],["Anna","Jim"], - ["Anna","Mike"],["Sally","Anna"],["Joe","Sally"], - ["Joe","Bob"],["Bob","Sally"]] - -session.query(insert_query, pairs: data) - -# Friends of a friend - -foaf_query = """ -MATCH (person:Person)-[:KNOWS]-(friend)-[:KNOWS]-(foaf) -WHERE person.name = $name - AND NOT (person)-[:KNOWS]-(foaf) -RETURN foaf.name AS name -""" - -response = session.query(foaf_query, name: 'Joe') -response.each do |row| - puts row[:name] -end - -# Common friends - -common_friends_query = """ -MATCH (user:Person)-[:KNOWS]-(friend)-[:KNOWS]-(foaf:Person) -WHERE user.name = {user} AND foaf.name = {foaf} -RETURN friend.name AS friend -""" - -response = session.query(common_friends_query, user: 'Joe', foaf: 'Sally') -response.each do |row| - puts row[:friend] -end - -# Connecting paths - -connecting_paths_query = """ -MATCH path = shortestPath((p1:Person)-[:KNOWS*..6]-(p2:Person)) -WHERE p1.name = {name1} AND p2.name = {name2} -RETURN path -""" - -response = session.query(connecting_paths_query, name1: 'Joe', name2: 'Billy') -response.each do |row| - puts row -end \ No newline at end of file diff --git a/modules/ROOT/examples/social.frag0.txt b/modules/ROOT/examples/social.frag0.txt deleted file mode 100644 index d667ad40..00000000 --- a/modules/ROOT/examples/social.frag0.txt +++ /dev/null @@ -1,8 +0,0 @@ -MATCH - (person:Person)-[:KNOWS]-(friend:Person)-[:KNOWS]- - (foaf:Person) -WHERE - person.name = "Joe" - AND NOT (person)-[:KNOWS]-(foaf) -RETURN - foaf diff --git a/modules/ROOT/examples/social.frag1.txt b/modules/ROOT/examples/social.frag1.txt deleted file mode 100644 index 2a8e7836..00000000 --- a/modules/ROOT/examples/social.frag1.txt +++ /dev/null @@ -1,7 +0,0 @@ -MATCH - (user:Person)-[:KNOWS]-(friend)- - [:KNOWS]-(foaf:Person) -WHERE - user.name = "Joe" AND foaf.name = "Sally" -RETURN - friend.name AS friend diff --git a/modules/ROOT/examples/social.frag2.txt b/modules/ROOT/examples/social.frag2.txt deleted file mode 100644 index dccc9221..00000000 --- a/modules/ROOT/examples/social.frag2.txt +++ /dev/null @@ -1,8 +0,0 @@ -MATCH - path = shortestPath( - (p1:Person)-[:KNOWS*..6]-(p2:Person) - ) -WHERE - p1.name = "Joe" AND p2.name = "Billy" -RETURN - path diff --git a/modules/ROOT/examples/transformer_spec.rb b/modules/ROOT/examples/transformer_spec.rb deleted file mode 100644 index d2315319..00000000 --- a/modules/ROOT/examples/transformer_spec.rb +++ /dev/null @@ -1,61 +0,0 @@ -#!/usr/bin/env ruby -require 'rubygems' -require 'bundler/setup' - -require './html_transformer' -RSpec.describe HtmlTransformer do - describe '.transform' do - subject { HtmlTransformer.transform(fragment.split(/\s*[\n\r]\s*+/)) } - - context 'original links' do - let(:fragment) do - <<-FRAGMENT -
- -
Cypher Query Language
- FRAGMENT - end - - it { should match('') } - end - - context "links with a trailing slash" do - let(:fragment) do - <<-FRAGMENT -
- -
Language Guides
- - FRAGMENT - end - - it { should match('') } - end - - context "developer-resources links" do - let(:fragment) do - <<-FRAGMENT -
- -
Language Guides
- - FRAGMENT - end - - it { should match('') } - end - - context 'multiple links' do - let(:fragment) do - <<-FRAGMENT -
  • Guide: Cloud Deployment
    • -
  • Sizing + Hardware Calculator
    • -
  • Performance Tuning
    • -
  • Clustering Neo4j
    • - FRAGMENT - end - - it { should match ('/developer/guide-clustering-neo4j') } - end - end -end diff --git a/modules/ROOT/examples/word_press_syncer.rb b/modules/ROOT/examples/word_press_syncer.rb deleted file mode 100644 index 16423f5b..00000000 --- a/modules/ROOT/examples/word_press_syncer.rb +++ /dev/null @@ -1,64 +0,0 @@ -require 'rubypress' -require 'logger' - -class WordPressSyncer - def initialize(blog_id, username, password, options = {}) - @blog_id = blog_id - @wp_client = Rubypress::Client.new(host: @blog_id, username: username, password: password) - @post_type = options[:post_type] || 'developer' - @logger = options[:logger] || Logger.new(STDOUT) - - @all_pages = @wp_client.getPosts(filter: {post_type: @post_type, number: 1000}) - @logger.info "Got #{@all_pages.length} pages from the database" - end - - def sync(title, post_name, html, custom_fields = {}) - content = { - post_type: @post_type, - post_date: Time.now - 60*60*24*30, - post_content: html, - post_title: title, - post_name: post_name, - post_status: 'publish', - custom_fields: custom_fields - } - - pages = @all_pages.select { |page| page['post_name'] == post_name } - @logger.info "Got #{pages.length} pages matching the post_name '#{post_name}'" - - page = pages.sort_by {|hash| hash['post_id'].to_i }.first - - if page - if page['custom_fields'] - content[:custom_fields].each do |f| - found = page['custom_fields'].find { |field| field['key'] == f[:key] } - f['id'] = found['id'] if found - end - end - - post_id = page['post_id'].to_i - - @logger.info "Editing #{post_id} on _#{@blog_id}_ custom-field #{content[:custom_fields].inspect}" - - send_message(:editPost, blog_id: @blog_id, post_id: post_id, content: content) - else - @logger.info "Making a new post for '#{title}' on _#{@blog_id}_" - - send_message(:newPost, blog_id: @blog_id, content: content) - end - end - - private - - def send_message(message, *args) - @wp_client.send(message, *args).tap do |result| - raise "WordPress #{message} failed!" if !result - end - end -end - - - - - - diff --git a/modules/ROOT/images/AdministratorLearningPath.png b/modules/ROOT/images/AdministratorLearningPath.png deleted file mode 100644 index 1235daa1..00000000 Binary files a/modules/ROOT/images/AdministratorLearningPath.png and /dev/null differ diff --git a/modules/ROOT/images/DataScientistLearningPath.png b/modules/ROOT/images/DataScientistLearningPath.png deleted file mode 100644 index 2c07009b..00000000 Binary files a/modules/ROOT/images/DataScientistLearningPath.png and /dev/null differ diff --git a/modules/ROOT/images/DeveloperLearningPath.png b/modules/ROOT/images/DeveloperLearningPath.png deleted file mode 100644 index dbcfa545..00000000 Binary files a/modules/ROOT/images/DeveloperLearningPath.png and /dev/null differ diff --git a/modules/ROOT/images/GraphGallery.jpg b/modules/ROOT/images/GraphGallery.jpg deleted file mode 100644 index 68fda574..00000000 Binary files a/modules/ROOT/images/GraphGallery.jpg and 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      -
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    • - Order -
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    • - Supplier -
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    • - Employee -
      • -
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    • - CONTAINS -
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    • - PART_OF -
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    diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-CONTAINS.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-CONTAINS.svg deleted file mode 100644 index 2250a43e..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-CONTAINS.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/)CONTAINSCONTAINSCONTAINSCONTAINSCONTAINSCONTAINSCONTAINSCONTAINSCONTAINSCONTAINS 10847 Chai 10918 10576 10935 11031 10689 10370 10863 10838 10590 \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Categories.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Categories.svg deleted file mode 100644 index 99346f46..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Categories.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/) Beverag… Condim… Confecti… Dairy Products Grains/… \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Employees.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Employees.svg deleted file mode 100644 index 245aeb1b..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Employees.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/) Sales Repres… Vice President, Sales Sales Repres… Sales Repres… Sales Manager \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Orders.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Orders.svg deleted file mode 100644 index a95dde54..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Orders.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/) 10248 10249 10250 10251 10252 \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-PART_OF.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-PART_OF.svg deleted file mode 100644 index 3d8d1d34..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-PART_OF.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/)PART_OFPART_OFPART_OFPART_OFPART_OFPART_OFPART_OFPART_OFPART_OFPART_OF Beverag… Chartreu… Chai Sasquat… Lakkalik… Côte de Blaye Rhönbräu Steeleye Stout Laughing Lumber… Guaraná Fantásti… Chang \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Products.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Products.svg deleted file mode 100644 index 6ed882bd..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Products.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/) Chai Chang Aniseed Syrup Chef Anton's Cajun Sea… Chef Anton's Gumbo Mix \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-REPORTS_TO.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-REPORTS_TO.svg deleted file mode 100644 index 33e2eb19..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-REPORTS_TO.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/)REPORTS_TOREPORTS_TOREPORTS_TOREPORTS_TOREPORTS_TOREPORTS_TO Sales Repres… Vice President, Sales Sales Repres… Sales Repres… Sales Manager Inside Sales Coordi… Sales Repres… \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-SOLD.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-SOLD.svg deleted file mode 100644 index 0e9194f7..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-SOLD.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/)SOLDSOLDSOLDSOLDSOLDSOLDSOLDSOLDSOLDSOLD 10886 Sales Repres… 10387 10894 10800 10304 10292 10340 10910 10400 10976 \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-SUPPLIES.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-SUPPLIES.svg deleted file mode 100644 index 7ec59bcc..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-SUPPLIES.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/)SUPPLIESSUPPLIESSUPPLIESSUPPLIESSUPPLIESSUPPLIESSUPPLIESSUPPLIESSUPPLIESSUPPLIES Exotic Liquids Chang Aniseed Syrup New Orleans Cajun Deli… Louisiana Hot Spi… Chef Anton's Cajun Sea… Louisiana Fiery Hot Chef Anton's Gumbo Mix Grandma Kelly's Northwo… Grandm… Uncle Bob's Organic Dried Tokyo Traders Mishi Kobe Ni… \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Suppliers.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Suppliers.svg deleted file mode 100644 index 06f73ed8..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/import-guide-Suppliers.svg +++ /dev/null @@ -1 +0,0 @@ -Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/) Exotic Liquids New Orleans Cajun Deli… Grandma Kelly's Tokyo Traders Coopera… \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/northwind_graph_simple.jpg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/northwind_graph_simple.jpg deleted file mode 100644 index 0a71fd69..00000000 Binary files a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/northwind_graph_simple.jpg and /dev/null differ diff --git a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/northwind_graph_simple.svg b/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/northwind_graph_simple.svg deleted file mode 100644 index a9948fdb..00000000 --- a/modules/ROOT/images/_cdn/data-import/guide-importing-data-and-etl/northwind_graph_simple.svg +++ /dev/null @@ -1 +0,0 @@ -SOLDREPORTS_TOCONTAINSPART_OFSUPPLIESEmployeeOrderEmployeeProductCategorySupplier \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/airline.png b/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/airline.png deleted file mode 100644 index 59ba78fc..00000000 Binary files a/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/airline.png and /dev/null differ diff --git a/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/airline.svg b/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/airline.svg deleted file mode 100644 index c7d0b008..00000000 --- a/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/airline.svg +++ /dev/null @@ -1 +0,0 @@ -DESTINATIONORIGINAIRLINEAirportAirportFlightAirlinecode: stringcode: stringflightNumber: departure: arrival: date: cancelled: diverted: stringLocalTimeLocalTimeDatebooleanbooleanname: string \ No newline at end of file diff --git a/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/boolean_refactoring.png b/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/boolean_refactoring.png deleted file mode 100644 index eb1d8558..00000000 Binary files a/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/boolean_refactoring.png and /dev/null differ diff --git a/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/boolean_refactoring.svg b/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/boolean_refactoring.svg deleted file mode 100644 index de652fbd..00000000 --- a/modules/ROOT/images/_cdn/data-modeling/graph-model-refactoring/img/boolean_refactoring.svg +++ /dev/null @@ -1 +0,0 @@ -CONNECTED_TOAirportAirportcode: stringcode: stringairline: flightNumber: departure: arrival: date: cancelled: diverted: stringstringLocalTimeLocalTimeDatebooleanboolean \ No newline at end of file diff --git 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/dev/null differ diff --git a/modules/ROOT/images/phpneo4j.png b/modules/ROOT/images/phpneo4j.png deleted file mode 100644 index 71308977..00000000 Binary files a/modules/ROOT/images/phpneo4j.png and /dev/null differ diff --git a/modules/ROOT/images/python.png b/modules/ROOT/images/python.png deleted file mode 100644 index 883d3f1e..00000000 Binary files a/modules/ROOT/images/python.png and /dev/null differ diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index b2b01e2e..69ff70ee 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -1,236 +1 @@ * https://neo4j.com/developer/[Neo4j Developer Center] -// * *For Beginners* - -// * xref:get-started.adoc[Getting Started] -// ** xref:graph-database.adoc[What is a Graph Database?] -// ** xref:intro-videos.adoc[Intro to Graph DBs Video Series] -// ** xref:graph-db-vs-rdbms.adoc[Concepts: RDBMS to Graph] -// ** xref:graph-db-vs-nosql.adoc[Concepts: NoSQL to Graph] -// ** xref:getting-started-resources.adoc[Getting Started Resources] - -// * xref:graph-platform.adoc[Neo4j Graph Platform] -// ** xref:neo4j-database.adoc[Neo4j Database] -// ** xref:neo4j-desktop.adoc[Neo4j Desktop] -// ** xref:neo4j-browser.adoc[Neo4j Browser] -// *** xref:browser-guide-list.adoc[Available Neo4j Browser Guides] -// *** xref:guide-create-neo4j-browser-guide.adoc[Tutorial: Create Custom Browser Guide] -// ** xref:neo4j-bloom.adoc[Neo4j Bloom] -// ** xref:neo4j-etl.adoc[How-To: Neo4j ETL Tool] -// ** xref:neo4j-apoc.adoc[Neo4j APOC Library] -// ** xref:graphql.adoc[Neo4j & GraphQL] - -// * xref:cypher:index.adoc[Cypher Query Language] -// ** xref:cypher:intro-cypher.adoc[Getting Started with Cypher] -// *** xref:cypher:querying.adoc[Writing Queries] -// *** xref:cypher:updating.adoc[Updating] -// *** xref:cypher:filtering-query-results.adoc[Filtering Query Results] -// *** xref:cypher:controlling-query-processing.adoc[Controlling Query Processing] -// *** xref:cypher:dates-datetimes-durations.adoc[Working with Dates] -// *** xref:cypher:subqueries.adoc[Using Subqueries] -// *** xref:cypher:guide-cypher-basics.adoc[Tutorial: Getting Started with Cypher] -// *** xref:cypher:guide-build-a-recommendation-engine.adoc[Tutorial: Build a Recommendation Engine] -// ** xref:cypher:guide-sql-to-cypher.adoc[Comparing SQL and Cypher] -// ** xref:cypher:procedures-functions.adoc[User Defined Procedures & Functions] -// ** xref:cypher:filtering-show.adoc[Filtering SHOW commands] -// ** xref:cypher:replanning.adoc[Cypher Runtime and Replanning] -// ** xref:cypher-style-guide.adoc[Cypher Style Guide] -// ** xref:cypher:resources.adoc[Cypher Resources] - -// * *For Developers* -// * xref:data-modeling.adoc[Graph Data Modeling] -// ** xref:guide-data-modeling.adoc[Graph Modeling Guidelines] -// ** xref:relational-to-graph-modeling.adoc[Modeling: RDBMS to Graph] -// ** xref:modeling-designs.adoc[Modeling Designs] -// ** xref:modeling-tips.adoc[Graph Modeling Tips] -// ** xref:graph-model-refactoring.adoc[Refactoring a graph model] -// ** xref:graphgist-portal.adoc[Interactive Graph Models] - -// * xref:data-import.adoc[Data Import] -// ** xref:guide-import-csv.adoc[Importing CSV] -// ** xref:guide-import-json-rest-api.adoc[Importing API Data] -// ** xref:relational-to-graph-import.adoc[Import: RDBMS to Graph] -// ** xref:guide-importing-data-and-etl.adoc[How-To: Import Northwind Dataset] -// ** xref:desktop-csv-import.adoc[How-To: Desktop CSV Import] -// ** xref:example-data.adoc[Example Datasets] - -// * xref:language-guides.adoc[Drivers & Language Guides] -// ** xref:java.adoc[Java] -// *** xref:spring-data-neo4j.adoc[Spring] -// **** xref:spring-data-neo4j.adoc[Spring Data Neo4j] -// **** xref:java-driver-spring-boot-starter.adoc[Java Driver Spring Boot Starter] -// *** xref:java-frameworks.adoc[Quarkus, Helidon, Micronaut] -// *** xref:neo4j-ogm.adoc[Neo4j Object Graph Mapper] -// *** xref:java-procedures.adoc[Procedures and Functions] -// *** xref:java-third-party.adoc[Third-party libraries] -// *** Courses: -// **** link:https://graphacademy.neo4j.com/courses/app-java/?ref=guides[Building Neo4j Applications with Java^] -// ** xref:dotnet.adoc[.NET] -// *** link:https://graphacademy.neo4j.com/courses/app-dotnet/?ref=guides[Building Neo4j Applications with .NET^] -// ** xref:javascript.adoc[JavaScript] -// *** link:https://graphacademy.neo4j.com/courses/app-nodejs/?ref=guides[Building Neo4j Applications with Node.js^] -// *** link:https://graphacademy.neo4j.com/courses/app-typescript/?ref=guides[Building Neo4j Applications with TypeScript^] -// ** xref:python.adoc[Python] -// *** link:https://graphacademy.neo4j.com/courses/app-python/?ref=guides[Building Neo4j Applications with Python^] -// ** xref:go.adoc[Go] -// *** link:https://graphacademy.neo4j.com/courses/app-go/?ref=guides[Building Neo4j Applications with Go^] -// ** xref:ruby.adoc[Ruby] -// ** xref:php.adoc[PHP] -// ** xref:erlang-elixir.adoc[Erlang & Elixir] -// ** xref:perl.adoc[Perl] - -// * Building Applications -// // ** xref:example-project.adoc[Example: all Stacks (Movies)] -// ** link:https://graphacademy.neo4j.com/courses/app-java/?ref=guides[Building Apps with Java] -// ** link:https://graphacademy.neo4j.com/courses/app-python/?ref=guides[Building Apps with Python] -// ** link:https://graphacademy.neo4j.com/courses/app-nodejs/?ref=guides[Building Apps with Node.js] -// ** link:https://graphacademy.neo4j.com/courses/app-dotnet/?ref=guides[Building Apps with .NET] -// ** link:https://graphacademy.neo4j.com/courses/app-go/?ref=guides[Building Apps with Go] -// ** link:https://graphacademy.neo4j.com/courses/app-typescript/?ref=guides[Building Apps with TypeScript] - -// // ** xref:js-movie-app.adoc[Tutorial: JavaScript/Express and React (IMDB)] -// // ** xref:ruby-course.adoc[Tutorial: Ruby & Rails (Books)] - -// * xref:integration.adoc[Neo4j Tools & Integrations] -// ** link:https://neo4j.com/docs/spark/current/[Neo4j Connector for Apache Spark] -// ** link:https://neo4j.com/labs/kafka/4.0/[Neo4j Connector for Apache Kafka] -// ** link:https://neo4j.com/bi-connector/[Neo4j Connector for Business Intelligence] - -// * xref:graph-apps:index.adoc[Graph Apps] -// ** xref:graph-apps:featured.adoc[Featured Graph Apps] -// ** xref:graph-apps:building-a-graph-app.adoc[Building Graph Apps] - -// * Courses -// ** link:https://graphacademy.neo4j.com/courses/neo4j-fundamentals/?ref=guides[Neo4j Fundamentals^] -// ** link:https://graphacademy.neo4j.com/courses/cypher-fundamentals/?ref=guides[Cypher Fundamentals^] -// ** link:https://graphacademy.neo4j.com/courses/modeling-fundamentals/?ref=guides[Graph Data Modeling Fundamentals^] -// ** link:https://graphacademy.neo4j.com/courses/importing-data/?ref=guides[Importing CSV Data into Neo4j^] -// ** link:https://graphacademy.neo4j.com/categories/developer/?ref=guides[Courses for Neo4j Developers^] -// ** link:https://graphacademy.neo4j.com/categories/data-scientist/?ref=guides[Courses for Data Scientists^] - -// // ** link:https://neo4j.com/graphacademy/training-overview-40/enrollment/[1.1 Overview of Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-querying-40/enrollment/[1.2 Querying with Cypher in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-updating-40/enrollment/[1.3 Creating Nodes and Relationships in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-best-practices-40/enrollment/[1.4 Using Indexes and Query Best Practices in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-importing-data-40/enrollment/[1.5 Importing Data with Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/online-training/graph-data-modeling/[2. Graph Data Modeling^] -// // ** link:https://neo4j.com/graphacademy/online-training/implementing-graph-data-models-40/[3. Implementing Graph Models in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/online-training/cypher-query-tuning-40/[4. Query Tuning in Neo4j 4.x^] - -// * *For Data Scientists* -// * xref:graph-data-science:index.adoc[Neo4j Graph Data Science] -// ** xref:graph-data-science:graph-algorithms.adoc[Graph Algorithms] -// ** xref:graph-data-science:neuler-no-code-graph-algorithms.adoc[NEuler: No-code Graph Algorithms] -// ** xref:graph-data-science:nlp/index.adoc[Natural Language Processing (NLP)] -// ** Tutorials -// *** xref:graph-data-science:applied-graph-embeddings.adoc[Graph Embeddings] -// *** xref:graph-data-science:build-knowledge-graph-nlp-ontologies.adoc[Build a Knowledge Graph with NLP and Ontologies] -// ** How-To Guides -// *** xref:graph-data-science:nlp/entity-extraction.adoc[Entity Extraction with APOC NLP] -// *** xref:graph-data-science:link-prediction/scikit-learn.adoc[Link Prediction with scikit-learn] -// *** xref:graph-data-science:link-prediction/aws-sagemaker-autopilot-automl.adoc[Link Prediction with AutoML] -// *** xref:graph-data-science:node-classification.adoc[Node Classification with GDSL] -// *** xref:graph-data-science:link-prediction/graph-data-science-library.adoc[Link Prediction with GDSL] -// ** Concepts -// *** xref:graph-data-science:graph-search-algorithms.adoc[Graph Search Algorithms] -// *** xref:graph-data-science:path-finding-graph-algorithms.adoc[Path Finding Algorithms] -// *** xref:graph-data-science:centrality-graph-algorithms.adoc[Centrality Algorithms] -// *** xref:graph-data-science:community-detection-graph-algorithms.adoc[Community Detection Algorithms] -// *** xref:graph-data-science:graph-embeddings.adoc[Graph Embeddings] -// *** xref:graph-data-science:link-prediction/index.adoc[Link Prediction] -// *** xref:graph-data-science:connected-feature-extraction.adoc[Connected Feature Extraction] -// ** Courses -// *** link:https://graphacademy.neo4j.com/courses/gds-product-introduction/?ref=guides[Introduction to Neo4j Graph Data Science^] -// *** link:https://graphacademy.neo4j.com/courses/graph-data-science-fundamentals/?ref=guides[Neo4j Graph Data Science Fundamentals^] -// *** link:https://graphacademy.neo4j.com/courses/gds-shortest-paths/?ref=guides[Path Finding with GDS^] -// *** link:https://graphacademy.neo4j.com/categories/data-scientist/?ref=guides[All Courses for Data Scientists^] - -// * xref:graph-visualization.adoc[Graph Visualization] -// ** xref:tools-graph-visualization.adoc[Visualization Tools] - -// * Courses -// ** link:https://graphacademy.neo4j.com/courses/neo4j-fundamentals/?ref=guides[Neo4j Fundamentals^] -// ** link:https://graphacademy.neo4j.com/courses/cypher-fundamentals/?ref=guides[Cypher Fundamentals^] -// ** link:https://graphacademy.neo4j.com/courses/modeling-fundamentals/?ref=guides[Graph Data Modeling Fundamentals^] -// ** link:https://graphacademy.neo4j.com/courses/importing-data/?ref=guides[Importing CSV Data into Neo4j^] -// ** link:https://graphacademy.neo4j.com/categories/developer/?ref=guides[Courses for Neo4j Developers^] -// ** link:https://graphacademy.neo4j.com/categories/data-scientist/?ref=guides[Courses for Data Scientists^] - -// // ** link:https://neo4j.com/graphacademy/training-overview-40/enrollment/[1.1 Overview of Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-querying-40/enrollment/[1.2 Querying with Cypher in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-updating-40/enrollment/[1.3 Creating Nodes and Relationships in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-best-practices-40/enrollment/[1.4 Using Indexes and Query Best Practices in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-importing-data-40/enrollment/[1.5 Importing Data with Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/online-training/intro-graph-algos-40/[2. Intro to Graph Algorithms in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-gdsds-40/enrollment/[3. Using a Machine Learning Workflow for Link Prediction^] -// // ** link:https://neo4j.com/graphacademy/training-gdsaa-40/enrollment/[4. Applied Graph Data Science for Web Applications^] - -// * *For Administrators* - -// * xref:in-production.adoc[Neo4j Administration] -// ** xref:memory-management.adoc[How-To: Memory Management] -// ** Tutorials -// *** xref:manage-multiple-databases.adoc[Managing Multiple Databases] -// *** xref:multi-tenancy-worked-example.adoc[Multi Tenancy Worked Example] -// ** xref:neo4j-fabric-sharding.adoc[Sharding Graphs with Fabric] -// ** xref:guide-performance-tuning.adoc[Performance Tuning] - -// * xref:docker.adoc[Docker & Neo4j] -// ** xref:docker-run-neo4j.adoc[How-To: Run Neo4j in Docker] - -// // * xref:aura-cloud-dbaas.adoc[Neo4j AuraDB] -// // ** xref:aura-connect-neo4j-desktop.adoc[Connect from Neo4j Desktop] -// // ** xref:aura-connect-cypher-shell.adoc[Connect from Cypher Shell] -// // ** xref:aura-connect-driver.adoc[Connect from your application] -// // ** xref:aura-data-import.adoc[Data Import with Neo4j AuraDB] -// // ** xref:aura-grandstack.adoc[Deploying a GRANDstack application to AuraDB] -// // ** xref:aura-bloom.adoc[Bloom Visualization with AuraDB] -// // ** xref:aura-monitoring.adoc[Monitoring] - -// * xref:guide-cloud-deployment.adoc[Neo4j in the Cloud] -// ** xref:guide-orchestration.adoc[Orchestration Tools] -// ** xref:neo4j-google-cloud-launcher.adoc[Tutorial: Deploy Neo4j Cluster on GCP] - -// * Courses -// ** link:https://graphacademy.neo4j.com/courses/neo4j-fundamentals/?ref=guides[Neo4j Fundamentals^] -// ** link:https://graphacademy.neo4j.com/courses/cypher-fundamentals/?ref=guides[Cypher Fundamentals^] -// ** link:https://graphacademy.neo4j.com/courses/modeling-fundamentals/?ref=guides[Graph Data Modeling Fundamentals^] -// ** link:https://graphacademy.neo4j.com/courses/importing-data/?ref=guides[Importing CSV Data into Neo4j^] -// ** link:https://graphacademy.neo4j.com/categories/developer/?ref=guides[Courses for Neo4j Developers^] -// ** link:https://graphacademy.neo4j.com/categories/data-scientist/?ref=guides[Courses for Data Scientists^] - -// // ** link:https://neo4j.com/graphacademy/training-overview-40/enrollment/[1. Overview of Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-querying-40/enrollment/[2. Querying with Cypher in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/online-training/basic-neo4j-admin-40/[3. Basic Neo4j 4.x Admin^] -// // ** link:https://neo4j.com/graphacademy/online-training/graph-data-modeling/[4. Graph Data Modeling^] -// // ** link:https://neo4j.com/graphacademy/training-updating-40/enrollment/[5. Creating Nodes and Relationships in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-best-practices-40/enrollment/[6. Using Indexes and Query Best Practices in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/training-importing-data-40/enrollment/[7. Importing Data with Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/online-training/cypher-query-tuning-40/[8. Query Tuning in Neo4j 4.x^] -// // ** link:https://neo4j.com/graphacademy/online-training/neo4j-administration/[(older installations) Neo4j 3.5 Administration^] - -// * *For Everyone* - - -// * link:https://neo4j.com/tag/twin4j/[Developer Newsletter^] - -// * xref:download-materials.adoc[Free Downloadable Neo4j Presentation Materials] - -// * xref:online-meetup.adoc[Twitch & YouTube Live Streams] -// ** link:https://neo4j.com/videos/[Neo4j Videos^] -// ** link:https://neo4j.com/speaker-program/[Speaker Program: Share your Story^] - -// * Community Support -// ** link:https://community.neo4j.com/[Community Forum^] -// ** link:https://discord.gg/neo4j[Discord Chat^] -// ** link:https://stackoverflow.com/questions/tagged/neo4j[StackOverflow^] - -// * Learn with GraphAcademy -// ** link:https://graphcademy.neo4j.com/?ref=guides[Free Online Courses^] -// ** link:https://graphacademy.neo4j.com/certification/[Neo4j Certification^] - -// * xref:resources.adoc[Documentation & Resources] -// ** link:https://neo4j.com/docs/[Neo4j Documentation^] - -// * xref:contribute.adoc[Contributing to Neo4j] -// ** xref:cla.adoc[Contributor License Agreement] -// ** xref:contributing-code.adoc[Code Contributions] diff --git a/modules/ROOT/page.adoc b/modules/ROOT/page.adoc deleted file mode 100644 index e69de29b..00000000 diff --git a/modules/ROOT/pages/Readme.adoc b/modules/ROOT/pages/Readme.adoc deleted file mode 100644 index 66c0117a..00000000 --- a/modules/ROOT/pages/Readme.adoc +++ /dev/null @@ -1,64 +0,0 @@ -== Neo4j Movies Example Application / Perl Edition - -=== Stack - -* http://metacpan.org/pod/REST::Neo4p[REST::Neo4p] - Neo4j Perl driver package -* http://mojolicio.us[Mojolicious] - Light but powerful Perl web framework -* Neo4j-Server -* Frontend: jquery, bootstrap, http://d3js.org/[d3.js] - -=== Endpoints: - -Get Movie - ----- -// JSON object for single movie with cast -curl http://localhost:8080/movie/The%20Matrix - -// list of JSON objects for movie search results -curl http://localhost:8080/search?q=matrix - -// JSON object for whole graph viz (nodes, links - arrays) -curl http://localhost:8080/graph ----- - -=== Setup - -From the source directory, run - ----- -perl Build.PL ----- - -which will create the ./Build script, and indicate any missing -modules. You can install these with - ----- -./Build --installdeps ----- - -=== Run locally: - -Start your local Neo4j Server (http://neo4j.com/download[Download & Install]), open the http://localhost:7474[Neo4j Browser]. -Then install the Movies data-set with `:play movies`, click the statement, and hit the triangular "Run" button. - -Copy the contents of -{github}/language-guides/assets -to ./public/assets - -Run tests in the source directory, if desired, with - ----- -./Build test ----- - -If all is well, start the app with Hypnotoad (included in the -Mojolicious distribution): - ----- -hypnotoad script/movie_neo4p ----- - -Go to http://localhost:8080 - -You can search for movies by title or and click on any entry. diff --git a/modules/ROOT/pages/_includes/aura_cta.adoc b/modules/ROOT/pages/_includes/aura_cta.adoc deleted file mode 100644 index faa1bf4f..00000000 --- a/modules/ROOT/pages/_includes/aura_cta.adoc +++ /dev/null @@ -1,11 +0,0 @@ -[.discrete.ad.right] -== Neo4j Aura -ifdef::cta-header[] -=== {cta-header} -endif::[] -ifndef::cta-header[] -=== The Fastest Path to Graph -endif::[] -Free forever, no credit card required. - -link:{aura_signup}[Start on AuraDB,role=button] diff --git a/modules/ROOT/pages/_includes/common-language-prereq.adoc b/modules/ROOT/pages/_includes/common-language-prereq.adoc deleted file mode 100644 index 591c9819..00000000 --- a/modules/ROOT/pages/_includes/common-language-prereq.adoc +++ /dev/null @@ -1,4 +0,0 @@ -.Prerequisites -[abstract] -You should be familiar with link:https://neo4j.com/docs/getting-started/current/graphdb-concepts/[graph database concepts] and the property graph model. -You should have link:{aura_signup}[created an Neo4j AuraDB cloud instance], or link:/download/[installed Neo4j locally] diff --git a/modules/ROOT/pages/_includes/community-driver-notice.adoc b/modules/ROOT/pages/_includes/community-driver-notice.adoc deleted file mode 100644 index d0719a47..00000000 --- a/modules/ROOT/pages/_includes/community-driver-notice.adoc +++ /dev/null @@ -1,12 +0,0 @@ -[#community-drivers] -== Neo4j Community Drivers - -Members of the each programming language community have invested a lot of time and love to develop each one of the community drivers for Neo4j, so if you use any one of them, please provide feedback to the authors. - -==== -[NOTE] -icon:users[size=2x] -The community drivers have been graciously contributed by the Neo4j community. -Many of them are fully featured and well-maintained, but some may not be. -Neo4j does not take any responsibility for their usability. -==== diff --git a/modules/ROOT/pages/_includes/desktop-create-and-start-DBMS.adoc b/modules/ROOT/pages/_includes/desktop-create-and-start-DBMS.adoc deleted file mode 100644 index 2b8bac7c..00000000 --- a/modules/ROOT/pages/_includes/desktop-create-and-start-DBMS.adoc +++ /dev/null @@ -1,44 +0,0 @@ - -In a Neo4j Desktop project, you can create one or more DBMSs. -Next, you will create a local DBMS in a project and start it. - -In the project where you want to add the DBMS, click the *Add* button, and then select *Local DBMS*: - -image::{img}generic-create_local_DBMS.png[role="popup-link"] - -This opens a dialog where you will specify the details of the DBMS. -You can use the default name for the DBMS, which is Graph DBMS, but you should name it something that helps to identify the use case for the DBMS. -Here we specify MyDBMS as the name: - -image::{img}generic-name_DBMS.png[role="popup-link"] - -You [underline]#must# specify a password for the DBMS so enter a password that you will remember: - -image::{img}generic-password_DBMS.png[role="popup-link"] - -Neo4j Desktop will create a DBMS with the default version for Neo4j Desktop. -However, you can select a different version. -Keep in mind that if there is a down arrow shown next to the version, this means that Neo4j Desktop will need to download resources for that particular version of the DBMS. To do this you [underline]#must# be connected to the Internet: - -image::{img}generic-version_DBMS.png[role="popup-link"] - -After you have specifed the details for the DBMS you want to create, you click the *Create* button: - -image::{img}generic-create_DBMS.png[role="popup-link"] - -And here is what you should see after the DBMS is successfully created: - -image::{img}generic-DBMS_created.png[role="popup-link"] - -Recall that you cannot have more than one DBMS started. -Provided you have no other DBMSs started in Neo4j Desktop, you can start your newly-created DBMS by hovering to the right of the DBMS name and clicking the *Start* button: - -image::{img}generic-start_DBMS.png[role="popup-link"] - -The DBMS will take a few seconds to start. After it is started, you should see something like this: - -image::{img}generic-DBMS_started.png[role="popup-link"] - -After the DBMS is started, you can access it with clients running on your system such as Neo4j Browser, Neo4j Bloom, etc. -In Neo4j Desktop, the DBMS is an Enterprise Server, but it can only be accessed locally. - diff --git a/modules/ROOT/pages/_includes/desktop-create-project.adoc b/modules/ROOT/pages/_includes/desktop-create-project.adoc deleted file mode 100644 index 4ae145f1..00000000 --- a/modules/ROOT/pages/_includes/desktop-create-project.adoc +++ /dev/null @@ -1,38 +0,0 @@ - -When you open Neo4j Desktop for the first time you will see a Neo4j Primer Project with the Movie Database already started as shown here: - -image::{img}generic-first_open_desktop.png[role="popup-link"] - -You can use this already-started DBMS if you want to begin learning about using Neo4j and Cypher. -For your development, you typically create one or more projects where a project can contain one or more DBMSs. - -Next, you will create a new project in Neo4j Desktop. -You can only have a single DBMS running, so you must first stop the DBMS that is started by clicking the *Stop* button: - -image::{img}generic-stop_movie_database.png[role="popup-link"] - -You should now see that there is no active DBMS: - -image::{img}generic-movie_database_stopped.png[role="popup-link"] - -Add a new project by clicking *New* at the top of the sidebar: - -image::{img}generic-create_project.png[role="popup-link"] - -This creates a project named *Project*: - -image::{img}generic-project_created.png[role="popup-link"] - -You want your projects to be named something that describes the goal of the project. -You can change the name of a project by hovering over the area to the right of the project name and selecting the *Edit* button: - -image::{img}generic-name_project.png[role="popup-link"] - -Then you type a name for the project and select the "Check" button to save the name. - -image::{img}generic-change_project_name.png[role="popup-link"] - -And here we see the project with the changed name: - -image::{img}generic-project_named.png[role="popup-link"] - diff --git a/modules/ROOT/pages/_includes/dotnet-driver.adoc b/modules/ROOT/pages/_includes/dotnet-driver.adoc deleted file mode 100644 index d59ff30e..00000000 --- a/modules/ROOT/pages/_includes/dotnet-driver.adoc +++ /dev/null @@ -1,49 +0,0 @@ - - - -The Neo4j .NET driver is *officially supported* by Neo4j and connects to the database using the binary protocol. -It aims to be minimal, while being idiomatic to .NET. - -Please note that the following example makes use of the `Neo4j.Driver.Simple` package that implements a blocking interface -around the 'main' driver. -This is to aid in clarity and simplicity for those just starting out with the Neo4j .Net driver. - -[source, subs=attributes] ----- -PM> Install-Package Neo4j.Driver.Simple-{dotnet-driver-version} ----- - -For real projects, the `Neo4j.Driver` package should be used instead. - -[source, subs=attributes] ----- -PM> Install-Package Neo4j.Driver-{dotnet-driver-version} ----- - -// TODO: Add this include back in -[source, c] ----- -include::https://raw.githubusercontent.com/neo4j/neo4j-dotnet-driver/{dotnet-driver-version}/Neo4j.Driver/Neo4j.Driver.Tests.Integration/Examples.cs[tag=hello-world,indent=0] ----- - - -include::./driver-warning.adoc[] - -[cols="3*"] -|=== -| Name -| icon:tag[] Version -| icon:user[] Authors - -| Neo4jDotNetDriver -| {dotnet-driver-version} -| The Neo4j Team, http://twitter.com/cskardon[Charlotte Skardon], https://twitter.com/IngvarKofoed[Martin Jensen] - -| http://www.nuget.org/packages/Neo4j.Driver[icon:gift[] NuGet Package] -| {examples}/movies-dotnet-bolt[icon:play-circle[] .NET Example], {examples}/movies-dotnetcore-bolt[icon:play-circle[] .NET core example] -| https://community.neo4j.com/c/drivers-stacks/NET[Neo4j Online Community^] - -| https://neo4j.com/docs/dotnet-manual/current/[icon:book[] Docs^] -| link:/docs/api/dotnet-driver/current/[icon:code[] API] -| http://github.com/neo4j/neo4j-dotnet-driver[icon:github[] Source] -|=== diff --git a/modules/ROOT/pages/_includes/driver-warning.adoc b/modules/ROOT/pages/_includes/driver-warning.adoc deleted file mode 100644 index 3d29bc3d..00000000 --- a/modules/ROOT/pages/_includes/driver-warning.adoc +++ /dev/null @@ -1,40 +0,0 @@ - -[#driver-configuration] -=== Driver Configuration - -From Neo4j version **4.0** and onwards, the default encryption setting is *off* by default and Neo4j will no longer generate self-signed certificates. -This applies to default installations, installations through Neo4j Desktop and Docker images. -You can https://neo4j.com/docs/migration-guide/current/upgrade-driver/#_configure_ssl_policy_for_bolt_server_and_https_server[verify the encryption level of your server^] by checking the `dbms.connector.bolt.enabled` key in `neo4j.conf`. - -// tag::table[] -.Table Scheme Usage -|=== -| Certificate Type | Neo4j Causal Cluster | Neo4j Standalone Server | Direct Connection to Cluster Member - -| *Unencrypted* -| `neo4j` -| `neo4j` -| `bolt` - -| *Encrypted with Full Certificate* -| `neo4j+s` -| `neo4j+s` -| `bolt+s` - - -| *Encrypted with Self-Signed Certificate* -| `neo4j+ssc` -| `neo4j+ssc` -| `bolt+ssc` - -| *https://neo4j.com/aura/[Neo4j AuraDB^]* -| `neo4j+s` -| N/A -| N/A - -|=== - - -Please review your https://neo4j.com/docs/operations-manual/current/security/ssl-framework/[SSL Framework settings^] when going into production. -If necessary, you can also https://medium.com/neo4j/getting-certificates-for-neo4j-with-letsencrypt-a8d05c415bbd[generate certificates for Neo4j with Letsencrypt^] -// end::table[] diff --git a/modules/ROOT/pages/_includes/drivers_example_list.adoc b/modules/ROOT/pages/_includes/drivers_example_list.adoc deleted file mode 100644 index 369cd739..00000000 --- a/modules/ROOT/pages/_includes/drivers_example_list.adoc +++ /dev/null @@ -1,27 +0,0 @@ -=== Existing Language / Driver Examples - -For our example application we've provided a implementation for the languages and drivers listed below. -You can find them in the http://github.com/neo4j-examples?query=movie[Neo4j Examples GitHub repository^]. -Most of them have thankfully been made available by the driver authors. - -==== For the Official Drivers - -* Java -** {examples}/neo4j-movies-java-bolt[Java Bolt Driver^] -** {examples}/spring-data-neo4j-intro-app[Spring Data Neo4j 5^] -* Python -** {examples}/movies-python-bolt[Python Bolt Driver^] -* Go -** {examples}/movies-golang-bolt[Golang Bolt Driver ^] -* Javascript -** {examples}/movies-javascript-bolt[Javascript Bolt Driver ^] - -==== For Community Drivers - -* Elixir: {examples}/movies-elixir-phoenix[phoenix^] by http://twitter.com/florin[Florin Pătraşcu^] -* .Net {examples}/movies-dotnet-neo4jclient[neo4jclient^] by http://twitter.com/pierrick22[Pierrick Gannon^] -* Ruby {examples}/movies-ruby-neo4j-core[Neo4j-Core^] by http://twitter.com/neo4jrb[Neo4j.rb Team^] -* Ruby {examples}/movies-ruby-neo4jrb[Neo4j.rb Rails^] by http://twitter.com/neo4jrb[Neo4j.rb Team^] -* Perl: {examples}/movies-perl-neo4p[Neo4p::REST^] by https://twitter.com/thinkinator[Mark Jensen^] -* PHP: {examples}/movies-php-neoclient[Neoclient^] by http://twitter.com/ikwattro[Chris Willemsen^] -* Python: {examples}/neo4j-movies-python-neomodel[Neomodel^] diff --git a/modules/ROOT/pages/_includes/go-driver.adoc b/modules/ROOT/pages/_includes/go-driver.adoc deleted file mode 100644 index c0074798..00000000 --- a/modules/ROOT/pages/_includes/go-driver.adoc +++ /dev/null @@ -1,42 +0,0 @@ - -The Neo4j Go driver is *officially supported* by Neo4j and connects to the database using the binary bolt protocol. -It aims to be minimal, while being idiomatic to Go. - -=== Module version -Make sure your application has been set up to use go modules (there should be a go.mod file in your application root). Add the driver with: - -[source, shell, subs="attributes, specialcharacters"] ----- -go get github.com/neo4j/neo4j-go-driver/v5 ----- - -If you need to pin a specific https://github.com/neo4j/neo4j-go-driver/tags[*5.x* version], you can run this instead: ----- -go get github.com/neo4j/neo4j-go-driver/v5@<5.x tag> ----- - -where `<5.x tag>` is one of the available tag (e.g.: `v5.2.0`). - -[source, go, subs="attributes"] ----- -include::https://raw.githubusercontent.com/neo4j/neo4j-go-driver/v{go-driver-version}/neo4j/test-integration/examples_test.go[tag=hello-world,indent=0] ----- - -include::./driver-warning.adoc[] - -[cols="3*"] -|=== -| Name -| icon:tag[] Version -| icon:user[] Authors - -| neo4j-driver -| {go-driver-version} -| The Neo4j Team - -| https://community.neo4j.com/c/drivers-stacks/go[Neo4j Online Community^] - -| https://neo4j.com/docs/go-manual/current/[icon:book[] Docs^] -| https://godoc.org/github.com/neo4j/neo4j-go-driver/neo4j[icon:code[] API] -| https://github.com/neo4j/neo4j-go-driver[icon:github[] Source] -|=== diff --git a/modules/ROOT/pages/_includes/java-driver.adoc b/modules/ROOT/pages/_includes/java-driver.adoc deleted file mode 100644 index af5624e8..00000000 --- a/modules/ROOT/pages/_includes/java-driver.adoc +++ /dev/null @@ -1,50 +0,0 @@ - -The Neo4j Java driver is *officially supported* by Neo4j and connects to the database using the binary protocol. -It aims to be minimal, while being idiomatic to Java. -We support Java 8 and 11 for the driver. - -When using Maven, add this to your _pom.xml_ file: - -[source, xml, subs="attributes, specialcharacters"] ----- -org.neo4j.driver -neo4j-java-driver -{java-driver-version} ----- - -For Gradle or Grails, this is how to add the dependency: - -[source, groovy, subs="attributes, specialcharacters"] ----- -compile 'org.neo4j.driver:neo4j-java-driver:{java-driver-version}' ----- - -For other build systems, see {maven-artifact-info}[information available at Maven Central]. - -[source,java, subs="attributes, specialcharacters"] ----- -include::https://github.com/neo4j/neo4j-java-driver/raw/{java-driver-version}/examples/src/main/java/org/neo4j/docs/driver/HelloWorldExample.java[tag=hello-world-import] - -include::https://github.com/neo4j/neo4j-java-driver/raw/{java-driver-version}/examples/src/main/java/org/neo4j/docs/driver/HelloWorldExample.java[tag=hello-world] ----- - -include::./driver-warning.adoc[] - -[cols="3*"] -|=== -| Name -| icon:tag[] Version -| icon:user[] Authors - -| neo4j-java-driver -| {java-driver-version} -| The Neo4j Team - -| {maven-artifact-info}[icon:gift[] Package] -| {examples}/movies-java-bolt[icon:play-circle[] Example] -| https://community.neo4j.com/c/drivers-stacks/java[Neo4j Online Community^] - -| https://neo4j.com/docs/java-manual/current/[icon:book[] Docs^] -| https://neo4j.com/docs/api/java-driver/current/[icon:code[] API] -| https://github.com/neo4j/neo4j-java-driver[icon:github[] Source] -|=== diff --git a/modules/ROOT/pages/_includes/javascript-driver.adoc b/modules/ROOT/pages/_includes/javascript-driver.adoc deleted file mode 100644 index f35c535c..00000000 --- a/modules/ROOT/pages/_includes/javascript-driver.adoc +++ /dev/null @@ -1,35 +0,0 @@ - -The Neo4j JavaScript driver is *officially supported* by Neo4j and connects to the database using the binary protocol. -It aims to be minimal, while being idiomatic to JavaScript, allowing you to subscribe to a stream of responses, errors and completion events. - -[source, shell, subs=attributes,specialcharacters] ----- -npm install neo4j-driver ----- - - -[source, javascript, subs=attributes] ----- -include::https://raw.githubusercontent.com/neo4j/neo4j-javascript-driver/4.0/test/examples.test.js[tag=language-guide-page,indent=0] ----- - -include::./driver-warning.adoc[] - -[cols="3*"] -|=== -| Name -| icon:tag[] Version -| icon:user[] Authors - -| neo4j-driver -| {javascript-driver-version} -| The Neo4j Team - -| https://www.npmjs.com/package/neo4j-driver[icon:gift[] Package] -| -| https://community.neo4j.com/c/drivers-stacks/javascript[Neo4j Online Community^] - -| https://neo4j.com/docs/javascript-manual/current/[icon:book[] Docs^] -| link:/docs/api/javascript-driver/current/[icon:code[] API] -| http://github.com/neo4j/neo4j-javascript-driver[icon:github[] Source] -|=== diff --git a/modules/ROOT/pages/_includes/python-driver.adoc b/modules/ROOT/pages/_includes/python-driver.adoc deleted file mode 100644 index 0d718426..00000000 --- a/modules/ROOT/pages/_includes/python-driver.adoc +++ /dev/null @@ -1,38 +0,0 @@ - -The Neo4j Python driver is *officially supported* by Neo4j and connects to the database using the binary protocol. -It aims to be minimal, while being idiomatic to Python. - -NOTE: Support for Python 2 was removed in the 2.0 release of the driver. - -[source, shell, subs=attributes,specialcharacters] ----- -pip install neo4j ----- - -[source, python, subs=attributes] ----- -include::https://github.com/neo4j/neo4j-python-driver/raw/{python-driver-version}/tests/integration/examples/test_hello_world_example.py[tag=hello-world-import] - -include::https://github.com/neo4j/neo4j-python-driver/raw/{python-driver-version}/tests/integration/examples/test_hello_world_example.py[tag=hello-world,indent=0] ----- - -include::./driver-warning.adoc[] - -[%headers,cols="3*"] -|=== -| Name -| icon:tag[] Version -| icon:user[] Authors - -| neo4j-driver -| {python-driver-version} -| The Neo4j Team - -| https://pypi.python.org/pypi/neo4j[icon:gift[] Package] -| -| https://community.neo4j.com/c/drivers-stacks/python[Neo4j Online Community^] - -| https://neo4j.com/docs/python-manual/current/[icon:book[] Docs^] -| link:/docs/api/python-driver/current/[icon:code[] API] -| https://github.com/neo4j/neo4j-python-driver[icon:github[] Source] -|=== diff --git a/modules/ROOT/pages/_includes/sample_project.adoc b/modules/ROOT/pages/_includes/sample_project.adoc deleted file mode 100644 index 7bbb5dba..00000000 --- a/modules/ROOT/pages/_includes/sample_project.adoc +++ /dev/null @@ -1,8 +0,0 @@ -=== The Example Project - -The Neo4j example project is a small, one page webapp for the movies database built into the Neo4j tutorial. -The front-end page is the same for all drivers: movie search, movie details, and a graph visualization of actors and movies. -Each backend implementation shows you how to connect to Neo4j from each of the different languages and drivers. - -You can learn more about our small, consistent example project across many different language drivers link:/developer/example-project[here^]. -You will find the implementations for all drivers as https://github.com/neo4j-examples?q=movies[individual GitHub repositories^], which you can clone and deploy directly. diff --git a/modules/ROOT/pages/_includes/using_aura.adoc b/modules/ROOT/pages/_includes/using_aura.adoc deleted file mode 100644 index c2341a32..00000000 --- a/modules/ROOT/pages/_includes/using_aura.adoc +++ /dev/null @@ -1,12 +0,0 @@ - -=== Deploying the Recommendations Database with AuraDB - -This section demonstrates how to deploy the Recommendations database to AuraDB - -* Download the link:https://github.com/neo4j-graph-examples/recommendations/tree/main/data[Dump File] -* Create an account on link:https://neo4j.com/cloud/aura/[AuraDB] -* Upload the dump and start the database - -++++ - -++++ diff --git a/modules/ROOT/pages/about-graphacademy.adoc b/modules/ROOT/pages/about-graphacademy.adoc deleted file mode 100644 index a05cbf4c..00000000 --- a/modules/ROOT/pages/about-graphacademy.adoc +++ /dev/null @@ -1,82 +0,0 @@ -//obsolete -= icon:graduation-cap[] Learn through Neo4j GraphAcademy -:level: Beginner -:page-level: Beginner -:author: Neo4j -:category: documentation -:tags: resources, graphacademy, training, certification, meetup - -The Neo4j GraphAcademy is the place for all of our interactive and guided training materials. -From self-paced online training courses to in-person events with Neo4j experts, you can learn all of the core functionality that you need to become familiar with Neo4j! - -There are a variety of opportunities to learn Neo4j in the way that works best for you. -You can go straight to our https://graphacademy.neo4j.com/[GraphAcademy^] page to see everything we offer. - -[#neo4j-training] -== Neo4j Training - -Whether you would like self-paced or guided types of learning, our training courses are a fantastic way to learn key concepts and practice hands-on exercises. -We offer a few different kinds of training to best suit your needs and learning goals. - -Whether you are new and want to see what graph is all about or experienced and want to dive deeper on a particular topic, the goal for our training is to help you learn it. - -=== Self-paced Online Training - -Our GraphAcademy includes free, self-paced online training for several areas of the Neo4j graph ecosystem. - -*Beginner Learning Path:* - -* https://graphacademy.neo4j.com/courses/neo4j-fundamentals/[Neo4j Fundamentals^] -* https://graphacademy.neo4j.com/courses/cypher-fundamentals/[Cypher Fundamentals^] -* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^] -* https://graphacademy.neo4j.com/courses/importing-data/[Importing CSV Data into Neo4j^] - -*Cypher Learning Path:* - -* https://graphacademy.neo4j.com/courses/cypher-intermediate-queries/[Cypher Intermediate Queries^] - -*Developer Learning Path:* - -* https://graphacademy.neo4j.com/courses/app-nodejs/[Building Neo4j Applications with Node.js^] -* https://graphacademy.neo4j.com/courses/app-python/[Building Neo4j Applications with Python^] -* https://graphacademy.neo4j.com/courses/app-java/[Building Neo4j Applications with Java^] -* https://graphacademy.neo4j.com/courses/app-dotnet/[Building Neo4j Applications with .NET^] -* https://graphacademy.neo4j.com/courses/app-go/[Building Neo4j Applications with Go^] - -*Data Scientist Learning Path:* - -* https://graphacademy.neo4j.com/courses/gds-product-introduction/[Introduction to Neo4j Graph Data Science^] -* https://graphacademy.neo4j.com/courses/graph-data-science-fundamentals/[Neo4j Graph Data Science Fundamentals^] - -https://graphacademy.neo4j.com/[Click here to enroll in a free online training course^] - -=== Online Meetups and Webinars - -For those who are unable to travel or who want to learn more about the broader graph world, Neo4j also hosts online meetups and webinars. -These events cover a variety of topics and include partner products, community projects, and technical solutions for users at all levels. - -* https://neo4j.com/developer/online-meetup[Twitch, YouTube and Meetup Live Streams] -* https://neo4j.com/webinars/[Neo4j Webinars^] - -=== Instructor-led Training - -Neo4j also provides both virtual and classroom training all over the world throughout the year. -These courses are instructor-led and are designed for users to get maximum value by hearing the content from someone at Neo4j and interacting directly with the instructor for questions, help, and better understanding. - -https://neo4j.com/events/world/training/[Instructor-led public training events^] - -Companies can also mailto:training@neo4j.com[request] private, custom training events for internal staff to solve specific problems or understand concepts and architecture specific to their use case. - -[#neo4j-certification] -== Neo4j Certified Professional - -image::{img}Neo4j_certified_professional.jpeg[role="popup-link",float="right",width=150] - -We also have a Neo4j Developer Certification that proves you know the foundations of the graph database and Cypher and is a great addition to your résume or CV. - -The exam tests your knowledge of developer-focused concepts, including the graph model, Cypher, and more. -It is free of charge and can be retaken. - -To help you get prepared, you can check out the details on the https://neo4j.com/graphacademy/neo4j-certification/[certification page^] of GraphAcademy and read https://medium.com/neo4j/neo4j-certification-how-to-pass-like-a-pro-eed6daa7c6f7[Jennifer's blog post^] for study tips. - -Upon passing the exam, you will receive a certificate and if you are a member of the https://community.neo4j.com[Neo4j Community^], a Neo4j Certified Professional Badge. diff --git a/modules/ROOT/pages/admin_courses.adoc b/modules/ROOT/pages/admin_courses.adoc deleted file mode 100644 index 070edd51..00000000 --- a/modules/ROOT/pages/admin_courses.adoc +++ /dev/null @@ -1,26 +0,0 @@ -//obsolete -= Administrator Courses -:category: administration -:tags: cypher, queries, graph-queries, resources, documentation, admin, training -:page-pagination: previous - -== Administrator Courses - -Neo4j has a number of courses to teach administrators how to use Neo4j. - -Here is the suggested learning path for Neo4j Administrators: - -image::{img}AdministratorLearningPath.png[role="popup-link"] - - -=== GraphAcademy Courses - -We recommend that you first take our introductory administration course: - -https://neo4j.com/graphacademy/online-training/basic-neo4j-admin-40/[Basic Neo4j 4.0 Administration^] - -Then follow with these courses: - -https://neo4j.com/graphacademy/online-training/introduction-to-neo4j-40/[Introduction to Neo4j 4.0^] -https://neo4j.com/graphacademy/online-training/graph-data-modeling/[Graph Data Modeling for Neo4j ^] -https://neo4j.com/graphacademy/online-training/cypher-query-tuning-40/[Cypher Query Tuning in Neo4j 4.0^] \ No newline at end of file diff --git a/modules/ROOT/pages/apache-hadoop.adoc b/modules/ROOT/pages/apache-hadoop.adoc deleted file mode 100644 index ffad62c1..00000000 --- a/modules/ROOT/pages/apache-hadoop.adoc +++ /dev/null @@ -1,59 +0,0 @@ -= Neo4j and Apache Hadoop -:level: Intermediate -:page-level: Intermediate -:author: Neo4j -:category: integrations -:tags: integrations, tools, hadoop -:description: Hadoop is well established as large scale data processing platform. It can easily pre-process huge datasets and information streams to extract and project the high quality data vectors that enrich your graph model with relevant new information. Here we want to demonstrate some approaches that used Hadoop jobs to prepare data for ingestion into Neo4j. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -You should have a sound understanding of both Hadoop and Neo4j, each data model, infrastructure requirements, data processing paradigm and APIs to leverage them effectively together. - -[role=expertise {level}] -{level} - -[#neo4j-hadoop] -== General Observations - -Hadoop is used everywhere to process large amounts of data. -Graph Databases on the other hand are all combining highly connected, high quality data from a variety of sources. -Using Hadoop to efficiently pre-process, filter and aggregate raw information to be suitable for Neo4j imports is a reasonable approach. - -Real world, log-, sensor-, transaction- and event data is noisy. -Most of the data frames don't add new information but are repetetive. -For enriching a good graph model with variant information you want to filter out the noise and project the raw data into a format that can be easily ingested in your graph. - -// this sentence is cut off (commented out) -// We look at a few examples that - -//// -todo mention GraphBuilder ?? -reliable, scale out hdfs -pre-processing, filter, aggregating of raw data -filter out noise -graph data is quality data not mass data -//// - -[NOTE] -In the past, there were some approaches that used Hadoop to quickly generate Neo4j datastores directly. -While this approach is performant, it is also tightly coupled to the store-format of a certain Neo4j version as it has to duplicate the functionality of writing to split-up store-files. -With the parallel neo4j-import tool and APIs provided in Neo4j, such a solution is no longer needed. -The import facilities scale across a large number of CPUs to maximize import performance. - -//// -seems like more todos - -== Pre-processing Graph Data with Hadoop - -* Dave: BitCoin -* Dave: DocGraph - -* TODO: Streaming / Event-Stream Filtering, Conversion, Aggregation - -// == Concurrent Graph Data Ingestion -//// diff --git a/modules/ROOT/pages/apache-spark.adoc b/modules/ROOT/pages/apache-spark.adoc deleted file mode 100644 index e81ee445..00000000 --- a/modules/ROOT/pages/apache-spark.adoc +++ /dev/null @@ -1,185 +0,0 @@ -= Neo4j and Apache Spark -:level: Intermediate -:page-level: Intermediate -:author: Neo4j -:category: integrations -:tags: integrations, spark, tools, deprecated -:description: This page is deprecated in favor of the Neo4j Connector for Apache Spark - -[NOTE] -**This approach is deprecated** in favor of the link:/docs/spark/current/[Neo4j Connector for Apache Spark]. This page is being maintained -for reference, but is not current or supported. Please consult the Neo4j Connector for Apache Spark for the latest supported connector. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -You should have a sound understanding of both Apache Spark and Neo4j, each data model, data processing paradigm and APIs to leverage them effectively together. - -[role=expertise {level}] -{level} - -[#neo4j-spark] -== General Observations - -Apache Spark is a clustered, in-memory data processing solution that scales processing of large datasets easily across many machines. It also comes with GraphX and GraphFrames two frameworks for running graph compute operations on your data. - -You can integrate with Spark in a variety of ways. -Either to pre-process (aggregate, filter, convert) your raw data to be imported into Neo4j. - -Spark can also serve as external Graph Compute solution, where you - -1. export data of selected subgraphs from Neo4j to Spark, -2. compute the analytic aspects, and -3. write the results back to Neo4j -4. to be used in your Neo4j operations and Cypher queries. - -[NOTE] -Neo4j itself is capable of running graph processing on medium to large graphs quickly. -For instance the https://github.com/maxdemarzi/graph_processing[graph-processing] project demonstrates that we can run PageRank (5 iterations) on the dbpedia dataset (10M nodes, 125M relationships) in 20 seconds as a Neo4j server extension or http://neo4j-contrib.github.io/neo4j-apoc-procedures/#_pagerank_algorithm[user defined procedure]. -Spark might be better suited for larger datasets or more intensive compute operations. - -[#spark-connector] -== Neo4j-Spark-Connector - -The https://github.com/neo4j-contrib/neo4j-spark-connector[Neo4j Spark Connector] uses the binary Bolt protocol to transfer data from and to a Neo4j server. - -[NOTE] -**The information on this page refers to the old (2.4.5 release) of the spark connector**. For more up to date information, an easier and more modern API, -consult the link:/docs/spark/current/[Neo4j Connector for Apache Spark]. - -It offers Spark-2.0 APIs for *RDD, DataFrame, GraphX and GraphFrames*, so you're free to chose how you want to use and process your Neo4j graph data in Apache Spark. - -Configure Neo4j-URL, -user and -password via `spark.neo4j.bolt.*` Spark Config options. - -The general usage is: - -1. create `org.neo4j.spark.Neo4j(sc)` -2. set `cypher(query,[params])`,`nodes(query,[params])`,`rels(query,[params])` as direct query, or + - `pattern("Label1",Seq("REL"),"Label2")` or `pattern( ("Label1","prop1"),("REL","prop"),("Label2","prop2") )` -3. optionally define `partitions(n)`, `batch(size)`, `rows(count)` for parallelism -4. choose which datatype to return - * `loadRowRdd`, `loadNodeRdds`, `loadRelRdd`, `loadRdd[T]` - * `loadDataFrame`,`loadDataFrame(schema)` - * `loadGraph[VD,ED]` - * `loadGraphFrame[VD,ED]` - -Here is a basic example for loading a `RDD[Row]`. - -[source,scala] ----- -org.neo4j.spark.Neo4j(sc).cypher("MATCH (n:Person) RETURN n.name").partitions(5).batch(10000).loadRowRdd ----- - -[source,shell] ----- -$SPARK_HOME/bin/spark-shell --conf spark.neo4j.bolt.password= \ ---packages neo4j-contrib:neo4j-spark-connector:2.0.0-M2,graphframes:graphframes:0.2.0-spark2.0-s_2.11 ----- - -[source,scala] ----- -import org.neo4j.spark._ - -val neo = Neo4j(sc) - -val rdd = neo.cypher("MATCH (n:Person) RETURN id(n) as id ").loadRowRdd -rdd.count - -// inferred schema -rdd.first.schema.fieldNames -// => ["id"] -rdd.first.schema("id") -// => StructField(id,LongType,true) - -neo.cypher("MATCH (n:Person) RETURN id(n)").loadRdd[Long].mean -// => res30: Double = 236696.5 - -neo.cypher("MATCH (n:Person) WHERE n.id <= {maxId} RETURN n.id").param("maxId", 10).loadRowRdd.count -// => res34: Long = 10 ----- - -Similar operations are available for *DataFrames* and *GraphX*. -The GraphX integration also allows to write data back to Neo4j with a `save` operation. - -To use *GraphFrames* you have to declare it as package. -Then you can load a GraphFrame with graph data from Neo4j and run graph algorithms or pattern matchin on it (the latter will be slower than in Neo4j). - -[source,scala] ----- -import org.neo4j.spark._ - -val neo = Neo4j(sc) - -import org.graphframes._ - -val graphFrame = neo.pattern(("Person","id"),("KNOWS",null), ("Person","id")).partitions(3).rows(1000).loadGraphFrame - -graphFrame.vertices.count -// => 100 -graphFrame.edges.count -// => 1000 - -val pageRankFrame = graphFrame.pageRank.maxIter(5).run() -val ranked = pageRankFrame.vertices -ranked.printSchema() - -val top3 = ranked.orderBy(ranked.col("pagerank").desc).take(3) -// => top3: Array[org.apache.spark.sql.Row] -// => Array([236716,70,0.62285...], [236653,7,0.62285...], [236658,12,0.62285]) ----- - -More examples and details can be found in the docs of the GitHub repository. - -* https://github.com/neo4j-contrib/neo4j-spark-connector[GitHub repository] -* https://spark-packages.org/package/neo4j-contrib/neo4j-spark-connector[Spark-Packages] -* http://neo4j.com/blog/neo4j-3-0-apache-spark-connector/[Blog Post] - -[#mazerunner] -== Neo4j-Mazerunner - -An interest in analytical graph processing led http://twitter.com/kennybastani[Kenny Bastani^] to work on an integration solution. -It allows to export dedicated datasets, e.g. node or relationship-lists to Spark. - -It supports these algorithms: - -* PageRank -* Closeness Centrality -* Betweenness Centrality -* Triangle Counting -* Connected Components -* Strongly Connected Components - -After running graph processing algorithms the results are written back concurrently and transactionally to Neo4j. - -One focus of this approach is on data safety, that's why it uses a persistent queue (RabbitMQ) to communicate data between Neo4j and Spark. - -The infrastructure is set up using Docker containers, there are dedicated containers for Spark, RabbitMQ, HDFS and Neo4j with the Mazerunner Extension. - -More details can be found on the project's http://github.com/neo4j-contrib/neo4j-mazerunner[GitHub page]. - -// TODO Kenny: should we discuss the implementation of the graph algorithms and the Pregel Program ? -// TODO Kenny: Anything else to add ? - -* http://github.com/neo4j-contrib/neo4j-mazerunner[GitHub: Neo4j-Mazerunner] -* http://www.kennybastani.com/2014/11/using-apache-spark-and-neo4j-for-big.html[Blog Post: Introduction] -* http://kennybastani.com/2015/01/categorical-pagerank-neo4j-spark.html[Blog Post: Categorical Page Rank] -* http://www.kennybastani.com/search/label/Mazerunner[Blog Series: Mazerunner] -* http://www.kennybastani.com/2015/03/spark-neo4j-tutorial-docker.html[Presentation: Combining Neo4j and Apache Spark using Docker] - -[#spark-data] -== Spark for Data Preprocessing - -One example of pre-processing raw data (Chicago Crime dataset) into a format that's well suited for import into Neo4j, was demonstrated by http://twitter.com/markhneedham[Mark Needham]. -He combined a number of functions into a Spark-job that takes the existing data, cleans and aggregates it and outputs fragments which are recombined later to larger files. - -The approach is detailed in his blog post: http://www.markhneedham.com/blog/2015/04/14/spark-generating-csv-files-to-import-into-neo4j/["Spark: Generating CSV Files to import into Neo4j"]. - -// Another example is Dave Fauth's bitcoin processing, where the raw bitcoin blockchain data is pre-processed in Spark to generate a format suitable for Neo4j's `neo4j-import` bulk import tool. TODO more details & blog post link(s) - -// todo show job fragements ??? -// todo Mark: anything else to add ? -// * TODO == Spark Streaming -// * TODO == Direct Spark Connector diff --git a/modules/ROOT/pages/aura-bloom.adoc b/modules/ROOT/pages/aura-bloom.adoc deleted file mode 100644 index f3d71d9a..00000000 --- a/modules/ROOT/pages/aura-bloom.adoc +++ /dev/null @@ -1,152 +0,0 @@ -= Use Neo4j Bloom with AuraDB Database -:level: Beginner -:page-level: Beginner -:author: Jennifer Reif -:neo4j-versions: 3.5 -:category: cloud -:tags: aura, bloom, dbaas, visualization -:description: We will learn how to connect Neo4j Bloom with a Neo4j cloud AuraDB instance. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -We should have created and started a Neo4j AuraDB database instance. -If we have not already, we can follow the instructions in the link:https://aura.support.neo4j.com/hc/en-us/articles/360037562253-Working-with-Neo4j-Aura[getting started guide^] to step through the process. - -[role=expertise {level}] -{level} - -**** -[NOTE] -For a step-by-step walkthrough of Bloom functionality with screenshots, visit the link:/developer/neo4j-bloom/[Bloom guide]. -Throughout this guide, we will also link to relevant sections of that guide for more detailed information on how to execute the steps, if needed. -**** - -**** -[NOTE, role="strong-bloom"] -Bloom is now available directly from AuraDB. *AuraDB supports Bloom version 1.3 and later* -**** - -++++ - -++++ - -[#aura-movie-graph] -== Load the Movie Graph - -If you have worked with Neo4j previously, you may already be familiar with the movie graph used in other tutorials and examples. -This graph is a small data set of movie and person entities and the relationships between them for people who acted in or directed the films. - -Neo4j has already set up a script and guide to help users import this data into Neo4j. -To access it, we can open a Neo4j Browser window. - -In the Neo4j AuraDB console (console.neo4j.io), choose the blue button at the right of the instance for `Open with` and choose `Neo4j Browser`. - -image::{img}aura_console_browser_1.jpg[role="popup-link"] - -Once we have Neo4j Browser open, we can click in the command line input at the top, type in `:play movie-graph`, then click the `play` button to the right of the input. - -image::{img}browser_command_line.jpg[role="popup-link"] - -A slideshow will appear beneath the command line, and we can use the next button to see the next page of the Browser guide. - -image::{img}load_movie_graph.jpg[role="popup-link"] - -Click on the Cypher statement (in grey dotted lines on the right). -This will copy it to the command line above, and then we can click the `play` button again to run that statement. -It will return a piece of the graph and display it in another result pane that appears below the command line. - -image::{img}movie_graph_return.jpg[role="popup-link"] - -**** -[NOTE] -Colors for your results may differ slightly, as each session can choose different color combinations. -**** - -We will also need to add indexes for the Person and Movie labels, which we will do by running the following two Cypher statements: - -`CREATE INDEX ON :Person(name);` - -`CREATE INDEX ON :Movie(title);` - -Copy each one into the command line, running each one by clicking the `play` button. - -image::{img}add_indexes.jpg[role="popup-link"] - -[#bloom-explore] -== Explore the data with Neo4j Bloom - -Let's explore the movie data with Neo4j Bloom! - -First, we launch our Bloom application by clicking again on the `Open with` button on the Neo4j AuraDB console, selecting `Neo4j Bloom` this time. - -image::{img}aura_console_browser_1.jpg[role="popup-link"] - -We will need to provide the username and password for our AuraDB instance before we can continue. - -image::{img}aura_bloom_login.jpg[role="popup-link"] - -It will then ask us to link:/developer/neo4j-bloom/#bloom-perspective[choose a perspective], which we will allow Bloom to do for us by clicking on the `Create Perspective`, and then the `Generate Perspective` button. -This will create a new perspective that we will then choose - select by clicking on the `Use Perspective` button. - -To view some data, we can type in `Movie Person` in the link:/developer/neo4j-bloom/#bloom-search[search bar] and click on the first suggestion that appears underneath it. - -image::{img}aurabloom_movieperson_1.jpg[role="popup-link"] - -That will show us all the `Movie` nodes who have `Person` nodes connected to them. - -image::{img}aurabloom_movie_results_1.jpg[role="popup-link"] - -We can zoom in (using the `+` and `-` icons in the lower right) to view more details about all of the nodes, and we can move about the graph by clicking in an empty space in the scene and dragging it around. - -This is quite a bit more data than we probably want to start with, so let us query something with a bit narrower scope. -We can link:/developer/neo4j-bloom/#clearing-scene[clear the scene] by right-mouse clicking on an empty space in the scene and choosing the `Clear Scene` option in the menu. - -Next, we can write a query to look for a specific movie and the people who are connected to it. -To do this, type `The Polar Express Person` into the search bar and click on the first suggestion that appears beneath it. - -image::{img}aurabloom_polarexpress_1.jpg[role="popup-link"] - -This will display the results of the query in the scene and allow us to explore a bit more. - -image::{img}aurabloom_polarexp_results_1.jpg[role="popup-link"] - -We can see that there are two people connected to this movie in our dataset - `Tom Hanks` and `Robert Zemeckis`. -We can find out if these two `Person` nodes have any other connections by link:/developer/neo4j-bloom/#expand-nodes[expanding those nodes]. -For this, click on one of the persons where a white border appears around the node. -Then right-mouse click and choose the `Expand`, and then `All` options from the menu. - -image::{img}aurabloom_expand_node_1.jpg[role="popup-link"] - -Once complete for both `Robert Zemeckis` and `Tom Hanks`, we can see that both people were also involved in the `Cast Away` movie. -Both times, Robert directed the movies and Tom Hanks acted in them (relationship types `ACTED_IN` and `DIRECTED`). - -image::{img}aurabloom_expanded_graph_1.jpg[role="popup-link"] - -**** -[NOTE] -To learn about more of Neo4j Bloom's capabilities, visit the link:/developer/neo4j-bloom/[Bloom guide] or the link:/docs/bloom-user-guide/current/[Bloom documentation^]. -**** - -[#aura-help] -== Help and Questions - -Helpful guides and support are available on the https://aura.support.neo4j.com/hc/en-us[AuraDB support^] pages. - -You can also ask questions and connect with other people launching Neo4j AuraDB at the -https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^]. - -If you have questions or problems with Bloom, you can post in the dedicated https://community.neo4j.com/c/neo4j-graph-platform/neo4j-bloom/91[Bloom topic on the Community Site^]. - -[#resources] -== Resources - -* Guide: Explore more functionality in link:/developer/neo4j-bloom/[Neo4j Bloom] -* Documentation: Learn more about link:/docs/bloom-user-guide/current/[Neo4j Bloom^] diff --git a/modules/ROOT/pages/aura-cloud-dbaas.adoc b/modules/ROOT/pages/aura-cloud-dbaas.adoc deleted file mode 100644 index 86f3116b..00000000 --- a/modules/ROOT/pages/aura-cloud-dbaas.adoc +++ /dev/null @@ -1,100 +0,0 @@ -= Neo4j AuraDB -:author: Jennifer Reif -:neo4j-versions: 4.0 -:category: cloud -:tags: aura, auradb, database-as-a-service, dbaas, graph-platform -:page-ad-overline-link: https://neo4j.com/cloud/aura/?ref=developer-guides -:page-ad-overline: Neo4j Aura -:page-ad-title: The Fastest Way to get Started with Graphs -:page-ad-description: Free forever, no credit card required. -:page-ad-link: https://neo4j.com/cloud/aura/?ref=developer-guides -:page-ad-underline-role: button -:page-ad-underline: Start on AuraDB - -[#neo4j-dbaas] -Neo4j AuraDB is a fully-managed cloud database developed by the same people that built Neo4j. - -This guides and tutorials below will help you get started: - -[#get-started-aura] -== Get Started with Neo4j AuraDB - -video::1Ee242FDFcc[youtube] - -Is it your first time using Neo4j AuraDB? -You've come to the right place! -The tutorial below will guide you through signing up, creating a database, and importing your data. - -https://aura.support.neo4j.com/hc/en-us/articles/360037562253-Working-with-Neo4j-Aura[Get started with Neo4j AuraDB^, role="button feature-box_button"] - -// link:/developer/create-database[Create database] -// link:/developer/change-password[Change password] - -[#connecting-aura] -== Connect to your AuraDB database - -If you log in to your AuraDB database using the AuraDB console, for any running database, you can simply open Neo4j Browser for that database using the *Open* button on the console. - -Provided the database is running, you can also connect to it from a Web browser by entering this URL: - ----- -https://dbid.databases.neo4j.io/browser/ ----- - -Where you specify the *dbid* for your database. Neo4j Browser will connect to the database after you provide the username/password for the database. -The default user name is `neo4j` and the password is what was generated or specified when the database was created. - -These tutorials explain how to connect to your AuraDB database from other Neo4j tools. - -link:/developer/aura-connect-neo4j-desktop/[Connect from Neo4j Desktop] + -link:/developer/aura-connect-cypher-shell/[Connect from Cypher Shell] + -link:/developer/aura-connect-driver/[Connect from your application] - -[#importing-data] -== Importing data - -AuraDB considers the protection of users' data to be of paramount importance. -There are therefore some constraints to keep in mind when using your favourite import tools to load data into an AuraDB database. -The guide below covers these in detail. - -link:/developer/aura-data-import/[Importing data into Neo4j AuraDB] - -// These tutorials show how to import data into an AuraDB database. - -// * link:/developer/import-existing-database[Import an existing database] - -// * Import from Web APIs - -[#deploying-applications] -== Deploying applications to AuraDB - -These tutorials explain how to build full stack applications with AuraDB. - -link:/developer/aura-grandstack/[Deploying a GRANDstack application] - -[#visualize-aura] -== Visualizing your data in AuraDB - -You can use Neo4j's Bloom visualization tool to explore graph data stored in Neo4j AuraDB. -Connect to an AuraDB database with Neo4j Bloom and run near-natural language searches to query your data and return the results in a visual way. - -link:/developer/aura-bloom/[Visualizing Neo4j AuraDB data with Bloom] - -[#managing-aura] -== Manage your AuraDB database - -You've created your AuraDB database, imported your data, and are now running your application against it. -Now you want to know what's happening inside the database. -The tutorial below explains the available options for monitoring your database. - -link:/developer/aura-monitoring/[Monitoring your AuraDB database] - -[#dbaas-resources] -== Questions? - -Additional guides and help are available on the https://aura.support.neo4j.com/hc/en-us[AuraDB support^] page. - -https://status.neo4j.io/[Neo4j AuraDB Status Page^, role="button feature-box_button"] - -You can also ask questions and connect with other people launching Neo4j AuraDB at the -https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^]. diff --git a/modules/ROOT/pages/aura-connect-cypher-shell.adoc b/modules/ROOT/pages/aura-connect-cypher-shell.adoc deleted file mode 100644 index 32fbb163..00000000 --- a/modules/ROOT/pages/aura-connect-cypher-shell.adoc +++ /dev/null @@ -1,239 +0,0 @@ -= Connecting to Neo4j AuraDB with Cypher Shell -:level: Intermediate -:page-level: Intermediate -:author: Mark Needham -:neo4j-versions: 3.5 -:category: cloud -:tags: aura, dbaas, cypher, tools -:description: In this page, we will learn how to connect to our Neo4j AuraDB database using the Cypher Shell command line tool. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -We should have created and started a Neo4j AuraDB database instance. -If we have not already, we can follow the instructions in the link:https://aura.support.neo4j.com/hc/en-us/articles/360037562253-Working-with-Neo4j-Aura[getting started guide] to step through the process. -We should also have https://neo4j.com/docs/operations-manual/current/tools/cypher-shell/[installed the Cypher Shell^] command line tool. - -[role=expertise {level}] -{level} - -[#aura-db-credentials] -== Get database credentials - -If we open our https://console.neo4j.io/#databases[Neo4j AuraDB Console Dashboard^], we'll see a list of our databases. - -image::{img}dbaas_database_created_4.png[role="popup-link"] - -We'll need to use the Connection URI, so let's copy that onto our clipboard: - -image::{img}dbaas_database_created_4_highlight_browser.png[role="popup-link"] - -We're now ready to connect to the Cypher Shell. - -[#aura-cypher-shell] -== Connecting to Cypher Shell - -If we have Neo4j installed locally we can find the Cypher Shell in the `bin` directory of the database. - -In the following examples we'll use the standalone version of the tool, which we can download from the https://neo4j.com/download-center/#cyphershell[Neo4j Download Center^]. -Once we've downloaded the zip file, we'll run the following commands: - -[source,bash] ----- -unzip cypher-shell.zip -cd cypher-shell ----- - -Let's learn what arguments we need to pass to the tool to connect to our database. - -[source, bash] ----- -./cypher-shell --help ----- - -If we run that command we'll see the following output, which has been edited for brevity: - -[source,text] ----- -usage: cypher-shell [-h] [-a ADDRESS] [-u USERNAME] [-p PASSWORD] [--encryption {true,false,default}] [-d DATABASE] [--format {auto,verbose,plain}] [-P PARAM] [--debug] [--non-interactive] [--sample-rows SAMPLE-ROWS] [--wrap {true,false}] [-v] [--driver-version] [-f FILE] - [--fail-fast | --fail-at-end] [cypher] - -A command line shell where you can execute Cypher against an instance of Neo4j. By default the shell is interactive but you can use it for scripting by passing cypher directly on the command line or by piping a file with cypher statements (requires Powershell on Windows). - -example of piping a file: - cat some-cypher.txt | cypher-shell - -positional arguments: - cypher an optional string of cypher to execute and then exit - -optional arguments: - -h, --help show this help message and exit - --fail-fast exit and report failure on first error when reading from file (this is the default behavior) - --fail-at-end exit and report failures at end of input when reading from file - --format {auto,verbose,plain} - desired output format, verbose displays results in tabular format and prints statistics, plain displays data with minimal formatting (default: auto) - -P PARAM, --param PARAM - Add a parameter to this session. Example: `-P "number => 3"`. This argument can be specified multiple times. - --debug print additional debug information (default: false) - --non-interactive force non-interactive mode, only useful if auto-detection fails (like on Windows) (default: false) - --sample-rows SAMPLE-ROWS - number of rows sampled to compute table widths (only for format=VERBOSE) (default: 1000) - --wrap {true,false} wrap table column values if column is too narrow (only for format=VERBOSE) (default: true) - -v, --version print version of cypher-shell and exit (default: false) - --driver-version print version of the Neo4j Driver used and exit (default: false) - -f FILE, --file FILE Pass a file with cypher statements to be executed. After the statements have been executed cypher-shell will be shutdown - -connection arguments: - -a ADDRESS, --address ADDRESS - address and port to connect to (default: neo4j://localhost:7687) - -u USERNAME, --username USERNAME - username to connect as. Can also be specified using environment variable NEO4J_USERNAME (default: ) - -p PASSWORD, --password PASSWORD - password to connect with. Can also be specified using environment variable NEO4J_PASSWORD (default: ) - --encryption {true,false,default} - whether the connection to Neo4j should be encrypted. This must be consistent with Neo4j's configuration. If choosing 'default' the encryption setting is deduced from the specified address. For example the 'neo4j+ssc' protocol would use encryption. - (default: default) - -d DATABASE, --database DATABASE - database to connect to. Can also be specified using environment variable NEO4J_DATABASE (default: ) ----- - -We need to pass in our database URI to the `-a` argument. -We can also optionally pass in user to the `-u` argument, and password to the `-p` argument, but we will be prompted for those values if we don't provide them. - -If we run the following command: - -[source,bash] ----- -./cypher-shell -a neo4j+s://d1c4a329.databases.neo4j.io ----- - -We'll then receive the following prompt: - -[source,bash] ----- -username: neo4j -password: ********************************************** ----- - -Once we've provided those details, we'll see the following message: - -[source,bash] ----- -username: neo4j -password: ******************************************* -Connected to Neo4j 4.1.0 at neo4j+s://d1c4a329.databases.neo4j.io:7687 as user neo4j. -Type :help for a list of available commands or :exit to exit the shell. -Note that Cypher queries must end with a semicolon. -neo4j@neo4j> - ----- - -We can now run queries against our Cloud database. -For example, we can run the following procedure to return the logged in user: - -[source,cypher] ----- -neo4j> CALL dbms.showCurrentUser(); ----- - -[opts="header",cols="1,1,1"] -|=== -| userName | roles | flags -|"neo4j" | ["admin"] | [] -|=== - -[#import-cypher-shell] -== Importing data using the Cypher Shell - -We could also pipe a file containing Cypher commands to the Cypher Shell command. -This feature is useful for doing data import. - -We have a file `import.cypher` that contains some of the Cypher statements from the link:/developer/graph-model-refactoring/[Graph Model Refactoring^] developer guide. -We can view the contents of that file by executing the following command from the terminal: - -[source,bash] ----- -cat import.cypher -CREATE CONSTRAINT ON (airport:Airport) -ASSERT airport.code IS UNIQUE; - -LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j-contrib/training/master/modeling/data/flights_1k.csv" AS row -MERGE (origin:Airport {code: row.Origin}) -MERGE (destination:Airport {code: row.Dest}) -MERGE (origin)-[connection:CONNECTED_TO { - airline: row.UniqueCarrier, - flightNumber: row.FlightNum, - date: date({year: toInteger(row.Year), month: toInteger(row.Month), day: toInteger(row.DayofMonth)}), - cancelled: row.Cancelled, - diverted: row.Diverted}]->(destination) -ON CREATE SET connection.departure = localtime(apoc.text.lpad(row.CRSDepTime, 4, "0")), - connection.arrival = localtime(apoc.text.lpad(row.CRSArrTime, 4, "0")); - -MATCH (:Airport)-[connectedTo:CONNECTED_TO]->(:Airport) -CALL apoc.refactor.normalizeAsBoolean(connectedTo, "diverted", ["1"], ["0"]) -RETURN count(*) ----- - -Now we're going to import this data into our Neo4j Cloud database, via the Cypher Shell command. -We'll do this by executing the following: - -[source,bash] ----- -export NEO4J_PASSWORD="Your password" -cat import.cypher | ./cypher-shell -a neo4j+s://d1c4a329.databases.neo4j.io -u neo4j --format verbose ----- - -[NOTE] -==== -If we pipe a file into the Cypher Shell command, we won't be prompted for our username and password, it will use default values if they aren't provided. -==== - -If we run those commands, we'll see the following output: - -[source,bash] ----- -0 rows available after 1 ms, consumed after another 0 ms -0 rows available after 336 ms, consumed after another 0 ms -Added 62 nodes, Created 1000 relationships, Set 7062 properties, Added 62 labels ----- - -We can now execute a read query to see what data we've imported into our database: - -[source, bash] ----- -./cypher-shell -a neo4j+s://d1c4a329.databases.neo4j.io -u neo4j "MATCH path = ()-->() RETURN path LIMIT 10" ----- - -If we execute this command, we'll see the following output: - -[opts="header",cols="1"] -|=== -| path -| (:Airport {code: "IAD"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 10:00, cancelled: "0", departure: 07:35, airline: "WN", flightNumber: "3231"}]->(:Airport {code: "TPA"}) -| (:Airport {code: "IAD"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 22:25, cancelled: "0", departure: 19:55, airline: "WN", flightNumber: "335"}]->(:Airport {code: "TPA"}) -| (:Airport {code: "IND"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 15:10, cancelled: "0", departure: 12:55, airline: "WN", flightNumber: "4"}]->(:Airport {code: "TPA"}) -| (:Airport {code: "IND"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 09:55, cancelled: "0", departure: 07:45, airline: "WN", flightNumber: "1144"}]->(:Airport {code: "PHX"}) -| (:Airport {code: "IND"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 16:25, cancelled: "0", departure: 14:25, airline: "WN", flightNumber: "675"}]->(:Airport {code: "PHX"}) -| (:Airport {code: "IND"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 10:10, cancelled: "0", departure: 10:20, airline: "WN", flightNumber: "2272"}]->(:Airport {code: "MDW"}) -| (:Airport {code: "IND"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 16:55, cancelled: "0", departure: 17:00, airline: "WN", flightNumber: "1827"}]->(:Airport {code: "MDW"}) -| (:Airport {code: "IND"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 07:10, cancelled: "0", departure: 07:15, airline: "WN", flightNumber: "1016"}]->(:Airport {code: "MDW"}) -| (:Airport {code: "IND"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 14:25, cancelled: "0", departure: 14:30, airline: "WN", flightNumber: "829"}]->(:Airport {code: "MDW"}) -| (:Airport {code: "IND"})-[:CONNECTED_TO {date: 2008-01-03, diverted: "0", arrival: 17:25, cancelled: "0", departure: 15:10, airline: "WN", flightNumber: "1333"}]->(:Airport {code: "MCO"}) -|=== - -[#cypher-shell-resources] -=== Resources - -* link:https://neo4j.com/docs/operations-manual/current/tools/cypher-shell/[Cypher Shell Documentation^] -* link:https://neo4j.com/download-center/#cyphershell[Cypher Shell in Download Center] - -[#aura-help] -== Help and Questions - -Helpful guides and support are available on the link:https://aura.support.neo4j.com/hc/en-us[AuraDB support^] pages. - -You can also ask questions and connect with other people launching Neo4j AuraDB at the -https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^]. diff --git a/modules/ROOT/pages/aura-connect-driver.adoc b/modules/ROOT/pages/aura-connect-driver.adoc deleted file mode 100644 index cab0ebe1..00000000 --- a/modules/ROOT/pages/aura-connect-driver.adoc +++ /dev/null @@ -1,68 +0,0 @@ -= Connect to Neo4j AuraDB with Neo4j Driver -:level: Beginner -:page-level: Beginner -:author: Mark Needham -:neo4j-versions: 3.5 -:category: cloud -:tags: aura, dbaas, cypher, tools -:description: In this page, we will learn how to connect to our Neo4j AuraDB database using the Neo4j Drivers. - - -.Goals -[abstract] -:description: - -.Prerequisites -[abstract] -We should have created and started a Neo4j AuraDB database instance. -If we have not already, we can follow the instructions in the link:https://aura.support.neo4j.com/hc/en-us/articles/360037562253-Working-with-Neo4j-Aura[getting started guide^] to step through the process. - -[role=expertise] -{level} - -[#aura-db-credentials] -== Get database credentials - -When using any of the language drivers, before we try to connect to our Neo4j AuraDB Database, we'll need three pieces of information: - -* Neo4j URL -* Username -* Password - -If we open our https://console.neo4j.io/#databases[Neo4j AuraDB Console Dashboard^], we'll see a list of our databases. - -image::{img}dbaas_database_created_4.png[role="popup-link"] - -We'll need to use the Neo4j connection URL, so let's copy that onto our clipboard: - -image::{img}dbaas_database_created_4_highlight_browser.png[role="popup-link"] - -The default username is `neo4j` and the default password is generated on database creation. - -For the database described above we'd have the following credentials: - -* Bolt URL - `neo4j+s://178c6768.databases.neo4j.io` -* Username - `neo4j` -* Password - `our-super-secret-password` - -[#aura-connect-driver] -== Connect with driver - -Now that we've got our credentials, we're ready to connect using a driver. - -The link:/developer/language-guides/[Drivers and Languages Guides^] page gives an overview of Neo4j drivers and explains the Bolt protocol used to interact with the database. -There are also guides for individual language drivers, including officially supported ones for the following languages: - -* link:/developer/dotnet/[.NET] -* link:/developer/java/[Java] -* link:/developer/javascript/[JavaScript] -* link:/developer/go/[Go] -* link:/developer/python/[Python] - -[#aura-help] -== Help and Questions - -Helpful guides and support are available on the link:https://aura.support.neo4j.com/hc/en-us[AuraDB support^] pages. - -You can also ask questions and connect with other people launching Neo4j AuraDB at the -https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^]. diff --git a/modules/ROOT/pages/aura-connect-neo4j-desktop.adoc b/modules/ROOT/pages/aura-connect-neo4j-desktop.adoc deleted file mode 100644 index bb92e384..00000000 --- a/modules/ROOT/pages/aura-connect-neo4j-desktop.adoc +++ /dev/null @@ -1,82 +0,0 @@ -= Connect to Neo4j AuraDB with Neo4j Desktop -:level: Beginner -:page-level: Beginner -:author: Jennifer Reif -:neo4j-versions: 3.5 -:category: cloud -:tags: aura, dbaas, desktop, remote-connection -:description: In this page, we will learn how to connect the Neo4j Desktop application to an instance in Neo4j AuraDB. This gives us the opportunity to have a single portal for interacting with all instances of Neo4j, whether local databases or databases located in various clouds. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -We should have created and started a Neo4j AuraDB database instance. -If we have not already, we can follow the instructions in the https://aura.support.neo4j.com/hc/en-us/articles/360037562253-Working-with-Neo4j-Aura[getting started guide^] to step through the process. -We should also have downloaded and installed the Neo4j Desktop application. -If you have not already, you can link:/download/[download it^] and follow the installation instructions that appear once you click to download. - -[role=expertise {level}] -{level} - -[#aura-desktop] -== Desktop Remote Instance - -When we create a new graph in Neo4j Desktop, we have the option to create a local graph or a remote graph. Because the Neo4j AuraDB instance is hosted remotely, we will be connecting to a remote instance. We can do this with a few steps. - - -1. Navigate to the https://console.neo4j.io[Neo4j AuraDB Console^] and confirm your instance is running. Take note of the URL (or use the copy button next to it to copy it to your clipboard), as we will need to use it later. -+ -image::{img}1desktopConnect_cloud_confirm_running.jpg[role="popup-link"] - -2. Choose the Projects tab in Neo4j Desktop [1], then choose or create a project [2], and click `Add Database` [3] in the right pane. -+ -image::{img}2desktopConnect_create_graph.jpg[role="popup-link"] - -3. Choose the option to connect to a Remote Graph -+ -image::{img}3desktopConnect_remote_graph.jpg[role="popup-link"] - -4. Enter a name for the database (can match name in cloud or can be different) and copy/paste the URL from the Neo4j AuraDB console (see Step 1) to the second input. Once complete, click `Next`. -+ -image::{img}4desktopConnect_name_url.jpg[role="popup-link"] - -5. Now we need to choose an authentication method. -Neo4j AuraDB instances require username and password authentication, so click the `Username/password` button. -+ -image::{img}5desktopConnect5_auth.jpg[role="popup-link"] - - -6. On this screen, we enter our credentials. -By default, the username for your Neo4j AuraDB database will be `neo4j` and unless you have changed it, the password will be the one generated for you when you created your AuraDB database. -If you have created another user for your AuraDB database, you can use this instead. -AuraDB databases require an encrypted connection, so ensure the box is checked for `Use encrypted connection` before clicking `Next`. -+ -image::{img}6desktopConnect_enter_user_pass.jpg[role="popup-link"] - -7. If the connection succeeds, we will see our database name and url. To activate the connection, click the `Connect` button in lower right. -Once activated, we can disconnect with the same button, open Neo4j Browser with the `Open` button to the left or open a link:/developer/graph-app-development/[Graph App] using the dropdown menu next to the Open button. -+ -image::{img}7desktopConnect_connected_remote.jpg[role="popup-link"] - -Neo4j Desktop only allows 1 connection at a time to a database (local or remote). - -**** -[NOTE] -**Note:** Deactivating in Neo4j Desktop will not shut down or stop the remote instance - only temporarily close the connection to it in Neo4j Desktop. -**** - -[#aura-desktop-interact] -== Interacting with the database - -Just as with other databases in Neo4j Desktop, you can https://install.graphapp.io[install graphapps^] for monitoring and other functionality. To do this, we can follow the same process to install the graph application that you need and open it from Neo4j Desktop or a web browser with the running and activated Neo4j AuraDB instance. - -[#aura-help] -== Help and Questions - -Helpful guides and support are available on the https://aura.support.neo4j.com/hc/en-us[AuraDB support^] pages. - -You can also ask questions and connect with other people launching Neo4j AuraDB at the -https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^]. diff --git a/modules/ROOT/pages/aura-data-import.adoc b/modules/ROOT/pages/aura-data-import.adoc deleted file mode 100644 index ad372499..00000000 --- a/modules/ROOT/pages/aura-data-import.adoc +++ /dev/null @@ -1,74 +0,0 @@ -= Data Import with Neo4j AuraDB -:level: Beginner -:page-level: Beginner -:author: Mark Needham -:neo4j-versions: 3.5 -:category: cloud -:tags: aura, dbaas, import, etl -:description: In this guide, we will see how Neo4j AuraDB imports data differently from other Neo4j instances and how to navigate those differences. - -.Goals -[abstract] -{description} - -[role=expertise {level}] -{level} - -[#aura-import] -== Why is it different? - -Because Neo4j AuraDB is a database instance that is located and maintained in the cloud, we must take care to ensure data integrity is maintained and access is carefully granted to avoid malicious tampering. - -To mediate these risks, Neo4j AuraDB has increased security and access to database-as-a-service instances. -While this means that malicious users cannot harm the data or infiltrate the system, it also means that certain tasks and processes may not work exactly the same as with other local or remote Neo4j databases. - -We will cover what those differences are, why they exist, and how to still get the data into the instance. - -[#import-differences-aura] -== Import differences with Neo4j AuraDB - -Let's go through the differences, one at a time. - -=== Import directory is inaccessible. - -Why? Restricting access to internal Neo4j folders avoids non-authorized users from accessing and tampering with the folder and contents. This prevents any user from dropping malicious files into the folder and potentially corrupting the database. - -What does it mean? Users will only be able to use APOC load procedures or LOAD CSV on publicly accessible data. Andrew Jefferson has written a couple of helpful link:https://medium.com/@aejefferson/methods-for-loading-data-into-a-remote-neo4j-instance-part-1-abea3328dedf[blog posts^] in more detail on this subject, including a scenario to link:https://medium.com/@aejefferson/how-to-use-cloud-storage-to-securely-load-data-into-neo4j-d97b72b2ad8f[securely load data^] into a remote Neo4j instance. - -=== Cannot use neo4j-admin import tool for importing data. - -Why? Similar to the import folder accessibility above, disk access has been locked down to avoid malicious activity and data/instance corruption by unauthorized users. - -To use this tool, we can still run it locally to import onto a local instance, then push the imported data to cloud by link:https://console.neo4j.io/#import-instructions[uploading our database^]. - -=== Importing data with a custom procedure is not possible at this time. - -Why? As with both of the above examples, disk and folder access for internal Neo4j folders has been restricted to avoid malicious activity. This means that the plugins folder is not visible where we cannot drop custom plugin files in as extensions. - -Instead, we can use all of the other supported and common import methods for Neo4j, including link:/docs/labs/apoc/current/import/[APOC^], link:https://aura.support.neo4j.com/hc/en-us/articles/360037063474-How-do-I-load-data-from-a-CSV-[LOAD CSV^], and link:https://console.neo4j.io/#import-instructions[push to cloud import^]. - -=== Neo4j's ETL Tool works a bit differently - -The Neo4j ETL tool still operates in a cloud environment, but we will need to use the 1.4.1 release. -This release changes the way the ETL tool imports the data. -To find out more, check out the link:https://aura.support.neo4j.com/hc/en-us/articles/360037559973-Neo4j-ETL-now-ready-for-the-cloud[article^] on what changed and how to use it. - -=== APOC cannot import using local files - -As discussed in most of the points above, Neo4j AuraDB will not access local storage for protection reasons. -We can still use APOC to access publicly accessible files or to load secure files. -For more information, check out Andrew Jefferson's blog posts on link:https://medium.com/@aejefferson/methods-for-loading-data-into-a-remote-neo4j-instance-part-1-abea3328dedf[loading public data^] and link:https://medium.com/@aejefferson/how-to-use-cloud-storage-to-securely-load-data-into-neo4j-d97b72b2ad8f[loading secure files^] into remote instances. - -=== Storing API credentials in the config file not available - -In other types of installations, users could place API credentials in the `neo4j.conf` file and reference them in Cypher statements like environment variables. -With Neo4j AuraDB, however, those internal folders and files have been hidden, so this functionality must be handled in other ways. -Whether we store these credentials in a secure file on the cloud or store them as parameters in Neo4j with Cypher, we must use these best practices to avoid non-authorized users accessing private credentials or information. - -[#aura-help] -== Help and Questions - -Helpful guides and support are available on the link:https://aura.support.neo4j.com/hc/en-us[AuraDB support^] pages. - -You can also ask questions and connect with other people launching Neo4j AuraDB at the -https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^]. \ No newline at end of file diff --git a/modules/ROOT/pages/aura-grandstack.adoc b/modules/ROOT/pages/aura-grandstack.adoc deleted file mode 100644 index b19298cb..00000000 --- a/modules/ROOT/pages/aura-grandstack.adoc +++ /dev/null @@ -1,321 +0,0 @@ - -= Deploying a GRANDstack application to AuraDB -:level: Intermediate -:page-level: Intermediate -:author: Mark Needham -:neo4j-versions: 3.5 -:category: cloud -:tags: aura, dbaas, grandstack, drivers, applications, labs -:description: In this page, we will learn how to build and deploy a GRANDstack application with Neo4j AuraDB. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -We should have created and started a Neo4j AuraDB database instance. -If we have not already, we can follow the instructions in the link:https://aura.support.neo4j.com/hc/en-us/articles/360037562253-Working-with-Neo4j-Aura[getting started guide] to step through the process. -We should also be familiar with the https://grandstack.io/[GRANDstack^]. - -[role=expertise {level}] -{level} - -Now that we've created our Neo4j AuraDB database, we're going to learn how to deploy a GRANDstack application that queries data stored in that database. - -[#our-dataset] -== The Football Transfers Graph - -We're going to deploy an application built on top of a graph containing football transfers scraped from https://www.transfermarkt.co.uk/[transfermarkt.co.uk]. -See https://medium.com/neo4j/football-transfers-graph-e8ba7347169e[Football Transfers Graph^] to learn more about the dataset. - -== Connect to Neo4j AuraDB with Neo4j Desktop - -The first thing we need to do, in link:/developer/neo4j-desktop/[Neo4j Desktop^], is create a remote connection to our AuraDB database. -See the link:/developer/aura-connect-neo4j-desktop/[Connect to Neo4j AuraDB with Neo4j Desktop^] guide for step by step instructions explaining how to do this. - -[#neo4j-cloud-tool] -== Neo4j Cloud Tool - -We're going to use the Neo4j Cloud Tool to import our data, so let's see how to set up that application. -We can install that link:neo4j://graphapps/install?url=https://neo.jfrog.io/neo/api/npm/npm/neo4j-cloud-ui[directly via this link], or if that doesn't work we can paste the following URL into the `Graph App URL` field on the Graph Applications menu: - -[source, text] ----- -https://neo.jfrog.io/neo/api/npm/npm/neo4j-cloud-ui ----- - -image::{img}grandstack-install-graphapp-manual.png[role="popup-link"] - -Once we do that we'll see the following prompt: - -image::{img}grandstack-install-graphapp.png[role="popup-link"] - -We can now launch the Neo4j Cloud Tool, which will take us to the following screen: - -image::{img}grandstack-neo4j-cloud-tool-launched.png[role="popup-link"] - - -[#importing-database] -== Importing the dataset - -We're going to use the Neo4j Cloud tool to import this dataset. - -Click on the `Check existing database` button. -This will check whether our AuraDB database contains existing data. -Assuming an empty database, we'll see the following screen: - -image::{img}grandstack-neo4j-cloud-tool-check-existing.png[role="popup-link"] - -Clicking on the `Next` button will take us to the following screen: - -image::{img}grandstack-neo4j-cloud-tool-select-database-dump.png[role="popup-link"] - -From here we can import databases or a dump file. -We'll be using the latter functionality to import a dump containing the football transfers database. - -The dump file can be downloaded from https://aura-datasets.s3.amazonaws.com/football-transfers.dump[aura-datasets.s3.amazonaws.com/football-transfers.dump]. -Once we've downloaded that file we'll select it as the dump file to import. -After that we'll click on the `Import Dump file` button. -This will take a few minutes to run, but eventually we'll see output similar to the following: - -image::{img}grandstack-neo4j-cloud-tool-done.png[role="popup-link"] - -Click on the `View data in Neo4j Browser` button to see what we've imported. -The diagram below shows a sample of the imported graph: - -image::{img}grandstack-neo4j-browser.png[role="popup-link"] - -[#our-application] -== Our Application - -Let's have a look at the GRANDstack application that we're going to deploy. -The application is a football transfers one built on top of the graph that we imported in the previous step. -See https://blog.grandstack.io/football-transfers-graph-app-with-the-grandstack-starter-kit-b8dfa62c322e[Football Transfers Graph App with the GRANDstack Starter Kit^] to learn more about the application itself. - -The following command will clone the football transfers project locally: - -[source,bash] ----- -git clone git@github.com:mneedham/football-transfers-app.git -cd football-transfers-app ----- - -The application has two parts: - -* API - this code powers a GraphQL API on top of our Neo4j database, and lives in the `api` directory. -* UI - the `ui` directory contains a React application that renders data extracted from the GraphQL API. - -The diagram below shows the architecture of our application and how it interacts with Neo4j: - -image::{img}grandstack-architecture.png[role="popup-link"] - -== Now CLI - -We're going to deploy the application using ZEIT, a cloud platform for websites and serverless APIS. - -First we'll need to create an account on that platform via https://zeit.co/signup[zeit.co/signup^]. - -image::{img}zeit_signup.png[role="popup-link"] - -Once we've done that we can install the Now CLI on our machine using instructions from https://zeit.co/download[zeit.co/download^]: - -[source,bash] ----- -npm i -g now ----- - -Now let's login to ZEIT using the Now CLI tool: - -[source,bash] ----- -now login ----- - -If we run that, we'll see the following output: - -[source,bash] ----- -> Enter your email: xxx@xxx.com -> We sent an email to xxx. Please follow the steps provided - inside it and make sure the security code matches xxx. ----- - -Once we've entered our credentials we'll receive an email which will contain a link that we need to click to verify that we want to login. -The console will then be updated with the following message: - -[source,bash] ----- -✔ Email confirmed -> Congratulations! You are now logged in. In order to deploy something, run `now`. ----- - -== Define credentials - -Now we're ready to tell ZEIT about our database credentials. -We'll need to provide the following details: - -* Bolt URL -* Username -* Password - -If we open our https://console.neo4j.io/#databases[Neo4j AuraDB Console Dashboard^], we'll see a list of our databases. - -image::{img}grandstack-database.png[role="popup-link"] - -We'll need to use the Bolt URL, so let's copy that onto our clipboard: - -image::{img}grandstack-database-highlight-bolt.png[role="popup-link"] - -The default username is `neo4j` and the default password is generated on database creation. - -image::{img}grandstack-database-password.png[role="popup-link"] - -For the database described above we'd have the following credentials: - -* Bolt URL - `bolt+routing://648d934e.databases.neo4j.io` -* Username - `neo4j` -* Password - `9UvUS6UnVAnr7_ziNaKg--e7ekd-5x4AEL7yseEFsl8` - -We can run the following commands to add these credentials as ZEIT secrets. - -[NOTE] -==== -Don't forget to change the credentials below to match those of your own AuraDB database. -Copy and pasting the credentials below won't work as that database has long since been destroyed! -==== - -[source,bash] ----- -now secret add NEO4J_URI bolt+routing://648d934e.databases.neo4j.io -now secret add NEO4J_USER neo4j -now secret add NEO4J_PASSWORD 9UvUS6UnVAnr7_ziNaKg--e7ekd-5x4AEL7yseEFsl8 ----- - -== Deploying GRANDstack application - -We're now ready to deploy our application, which we can do by executing the following command: - -[source,bash] ----- -now ----- - -Running this command will result in the following output: - -[source,bash] ----- -> Deploying ~/projects/football-transfers-app under mneedham -> Using project grand-stack-starter -> Synced 21 files [652ms] -> Upload [====================] 99% 0.0s> NOTE: This is the first deployment in the grand-stack-starter project. It will be promoted to production. -> NOTE: To deploy to production in the future, run `now --prod`. -> https://grand-stack-starter-ou2l4008p.now.sh [4s] -> Ready! Deployment complete [2m] -- https://grand-stack-starter-seven-wheat.now.sh -- https://grand-stack-starter.mneedham.now.sh [in clipboard] ----- - -We can then navigate to the provided URL, which in this case is `https://grand-stack-starter-seven-wheat.now.sh/`. -Once this page has loaded, we'll see a list of the top transfers: - -image::{img}grandstack-deployed-transfers.png[role="popup-link"] - -We can navigate to other screens via the left menu: - -image::{img}grandstack-deployed-menu.png[role="popup-link"] - -My favourite one is `Country Money Flow`, so let's have a look at that: - -image::{img}grandstack-deployed-country-money-flow.png[role="popup-link"] - -We've now deployed our first GRANDstack application. -If any of the steps don't make sense or didn't work, please let us know in the https://community.neo4j.com/c/drivers-stacks/graphql-grandstack[GraphQL and GRANDstack topic^] on the https://community.neo4j.com/[community site^]. - -[#aura-help] -== Help and Questions - -Helpful guides and support are available on the link:https://aura.support.neo4j.com/hc/en-us[AuraDB support^] pages. - -You can also ask questions and connect with other people launching Neo4j AuraDB at the -https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic^] on the community site. - -// The most interesting part of the API is our GraphQL schema, which lives in https://github.com/mneedham/football-transfers-app/blob/master/api/src/schema.graphql[`api/src/schema.graphql`^]. -// Below is a small part of the schema: -// -// [source,graphql] -// ---- -// type Club { -// _id: Long! -// id: String! -// image: String -// name: String! -// in_league: League @relation(name: "IN_LEAGUE", direction: "OUT") -// transfers_from_club: [Transfer] @relation(name: "FROM_CLUB", direction: "IN") -// transfers_to_club: [Transfer] @relation(name: "TO_CLUB", direction: "IN") -// } -// ---- -// -// This fragment defines a `Club` type that will return nodes that have the `Club` label. -// It maps some basic properties: `id`, `image`, and `name`, and also allows us to navigate to some other types, including: -// -// -// * the `League` that a club plays in using the `in_league` property. -// This property traverses the `IN_LEAGUE` relationship type going `OUT` from the club. -// * the `Transfers` going from a club using the `transfers_from_club` property -// This property traverses the `FROM_CLUB` relationship type coming `IN` to the club. -// * the `Transfers` going to a club using the `transfers_to_club` property -// This property traverses the `TO_CLUB` relationship type coming `IN` to the club -// -// === UI -// -// The `ui` directory contains a React application that renders data extracted from the GraphQL API. -// -// Let's take a look at https://github.com/mneedham/football-transfers-app/blob/master/ui/src/TopTransfers.js#L54[ui/src/TopTransfers.js^], which contains a component that renders the most expensive transfers: -// In the middle of the file we find the following code: -// -// [source,js] -// ---- -// const QUERY = gql` -// query topTransfers( -// $orderBy: [_TransferOrdering] -// $first: Int -// $offset: Int -// $filter: _TransferFilter -// ) { -// Transfer( -// first: $first -// orderBy: $orderBy -// offset: $offset -// filter: $filter -// ) { -// date { formatted } -// value -// id -// of_player { name image } -// from_club { name image } -// to_club { name image -// } -// } -// } -// `; -// ---- -// -// This fragment defines a GraphQL query that returns various properties related to `Transfers`. -// We call that query in the `Query` component further down the file: -// -// [source,js] -// ---- -// -// ---- -// -// This component executes the GraphQL query and returns a collection of results in the `data` variable. -// We then iterate over that collection, rendering one row per transfer. diff --git a/modules/ROOT/pages/aura-monitoring.adoc b/modules/ROOT/pages/aura-monitoring.adoc deleted file mode 100644 index f1336813..00000000 --- a/modules/ROOT/pages/aura-monitoring.adoc +++ /dev/null @@ -1,114 +0,0 @@ -= Monitoring a Neo4j AuraDB Database -:level: Intermediate -:page-level: Intermediate -:author: Mark Needham -:neo4j-versions: 3.5 -:category: cloud -:tags: aura, dbaas, monitoring, operations -:description: In this page, we will learn how to monitor our Neo4j AuraDB database. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -We should have created and started a Neo4j AuraDB database instance. -If we have not already, we can follow the instructions in the link:https://aura.support.neo4j.com/hc/en-us/articles/360037562253-Working-with-Neo4j-Aura[getting started guide] to step through the process. - -[role=expertise {level}] -{level} - -Now that we've created our Neo4j AuraDB database, we're going to learn how to monitor it. - -[#aura-status] -== Status Page - -Like every proper cloud service, Neo4j AuraDB also has a status page, that gives you a quick overview on the availability and operability of the overall service. - -https://status.neo4j.io/[Neo4j AuraDB Status Page^, role="button feature-box_button"] - -[#aura-metrics] -== Built-in metrics - -If we open our https://console.neo4j.io/#databases[Neo4j AuraDB Console Dashboard^], we'll see a list of our databases. - - -image::{img}dbaas_database_created.png[role="popup-link"] - -We'll click on the name of our database, which will take us to the database overview page: - - -image::{img}dbaas_database_created_highlight_name.png[role="popup-link"] - -Once we've clicked this button, we'll see the following screen, which gives us a high level overview of some important metrics: - -image::{img}dbaas_database_overview.png[role="popup-link"] - -These metrics should be sufficient for understanding what's happening in the AuraDB database, but if you think that there's something missing don't hesitate to https://aura.support.neo4j.com/hc/en-us/requests/new[submit a support ticket^] and our team will be happy to help. - -// == Halin -// -// Halin is Neo4j's monitoring and management tool. -// It can be https://install.graphapp.io[installed as a Neo4j Desktop Graph App^] and there is a https://halin.graphapp.io/[hosted version^] as well. -// -// We can learn more about the Halin tool on its https://neo4j.com/labs/halin/[Neo4j Labs product page^]. - -// We'll use the hosted version in this tutorial. - -// If we navigate to https://halin.graphapp.io/[halin.graphapp.io^], we'll see the following screen requesting our database credentials: -// -// image::{img}dbaas_monitoring_halin_login.png[role="popup-link"] -// -// The default username is `neo4j` and the default password is generated on database creation. -// In the host field we need to enter the host of our database, which is the Bolt URI minus the protocol information. -// -// image::{img}dbaas_database_created_highlight_browser.png[role="popup-link"] -// -// In the example above, our Bolt URI is `bolt+routing://178c6768.databases.neo4j.io`, and our host would therefore be `178c6768.databases.neo4j.io`. -// -// Once we've filled in those details we'll see the following overview screen: -// -// image::{img}dbaas_monitoring_halin_overview.png[role="popup-link"] -// -// This is the best screen for getting an overall summary of what's happening with your database. -// -// === Power Users -// -// The overview screen only touches the surface of what Halin has to offer. -// For power users there are many other features, which are covered in detail in David Allen's online meetup talk: -// -// ++++ -// -// ++++ -// -// Let's explore one of these power user features - viewing the queries running on cluster members. -// We can interrogate individual cluster members by clicking on the `Cluster members` tab, which is the 2nd one down: -// -// image::{img}dbaas_monitoring_halin_select_members.png[role="popup-link"] -// -// We can then click on the `Queries` tab to see which queries are currently running on that cluster member: -// -// image::{img}dbaas_monitoring_halin_queries.png[role="popup-link"] -// -// In this screenshot we can see that we've got a long running query, on line 1, that we probably want to kill. -// We can do that by clicking on the `X` button: -// -// image::{img}dbaas_monitoring_halin_kill_query.png[role="popup-link"] -// -// Once we've done this a request will be sent to the database to terminate that query, and it will be removed from the query view. - -// [monitoring-resources] -// === Resources -// -// * link:https://neo4j.com/labs/halin/[Halin – Neo4j Monitoring Tool^] -// * link:https://halin.graphapp.io[Hosted version of Halin^] -// * link:https://install.graphapp.io[Graph Apps Gallery^] - -[#aura-help] -== Help and Questions - -Helpful guides and support are available on the link:https://aura.support.neo4j.com/hc/en-us[AuraDB support^] pages. - -You can also ask questions and connect with other people launching Neo4j AuraDB at the -https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^]. diff --git a/modules/ROOT/pages/browser-guide-list.adoc b/modules/ROOT/pages/browser-guide-list.adoc deleted file mode 100644 index 14cbd11f..00000000 --- a/modules/ROOT/pages/browser-guide-list.adoc +++ /dev/null @@ -1,49 +0,0 @@ -= Browser Guide Reference List -:level: Beginner -:page-level: Beginner -:author: Jennifer Reif -:category: documentation -:tags: resources, browser, guides, custom-guide, graphgists -:page-newsletter: true -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/appendix/example-data/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -Neo4j Browser provides many interactive guides for learning concepts and tools with Neo4j. -From data import to graph algorithms to working with RDF data, there is likely an interactive browser guide out there to walk through it. - -If there isn't one that fits your needs, you can create one and publish it to the community or internally at your company. -For more information on how to do that, check out our guide for link:/developer/guide-create-neo4j-browser-guide/[creating a custom Browser guide]. - -[#built-in] -== Neo4j Browser built-in guides - -Some guides ship with Neo4j Browser out-of-the-box, no matter what system or installation we are working on. -Below is a list of guides with descriptions for what is provided. - -[cols="2*",frame=all] -|=== -|Guide Command |Description -|`:play intro` |Navigating Neo4j Browser -|`:play concepts` |Property graph model concepts -|`:play cypher` |Cypher basics - create, match, patterns -|`:play graph-data-science` |Compute centrality and similarity scores, detect communities -|`:play movie-graph` |Queries and recommendations with Cypher - movie use case -|`:play northwind-graph` |Translate and import relational data to graph -|`:play start` |Main guide (loads when Browser opens) - concepts, write-code, system info -|`:play write-code` |Shows both guides for movie graph and northwind graph -|=== - -[#public-guides] -== Public Guides - -Other guides are created by Neo4j staff and community members to help others learn Neo4j or how to use the tools in its ecosystem. -We have some amazing work in this area, and we always welcome contributions! - -You can check out the currently published list on the https://portal.graphgist.org/graph_guides[GraphGist Portal (Graph Guides section)^]. -Latest updates to the content and any newly-added guides will also show there! - -== Resources - -* Tutorial: link:/developer/guide-create-neo4j-browser-guide/[Create your own Browser guide] diff --git a/modules/ROOT/pages/c.adoc b/modules/ROOT/pages/c.adoc deleted file mode 100644 index 888069b1..00000000 --- a/modules/ROOT/pages/c.adoc +++ /dev/null @@ -1,46 +0,0 @@ -= Neo4j Community Driver for C Developers -:level: Intermediate -:page-level: Intermediate -:author: Neo4j -:programming-language: c -:category: drivers -:tags: c, app-development, applications -:description: If you are a C or pass:[C++] developer, this guide provides an overview of options for connecting from C and pass:[C++] to Neo4j. - -.Goals -[abstract] -{description} - -include::./_includes/common-language-prereq.adoc[] - -[role=expertise {level}] -{level} - -include::./_includes/community-driver-notice.adoc[] - -[#neo4j-Cdriver] -== Neo4j C Driver - -Despite several attempts, Neo4j's HTTP was not well supported in the C community. -This changed with the advent of the binary protocol in Neo4j 3.0. -Chris Leishman wrote a very comprehensive and full featured c-client library for the binary protocol. - -[#libneo4j] -== libneo4j-client - -Libneo4j-client is a full featured c-library for interacting with Neo4j's binary protocol. -It supports all wire commands, as well as the security features like auth and TLS. - -A very performant shell client is provided together with the library. - -[NOTE] -This library is not only meant to be used from C/pass:[C++] programs directly, but also as convenient, low-level integration library for other languages like PHP, Ruby and Python. - -[cols="1,4"] -|=== -| icon:user[] Author | http://twitter.com/cleishm[Chris Leishman] -// | icon:play-circle[] Example Project | {examples}/movies-c -| icon:gift[] Package | http://cleishm.github.io/libneo4j-client/#getting-started -| icon:github[] Source | https://github.com/cleishm/libneo4j-client -| icon:book[] Docs | https://cleishm.github.com/libneo4j-client -|=== diff --git a/modules/ROOT/pages/cassandra.adoc b/modules/ROOT/pages/cassandra.adoc deleted file mode 100644 index 3de4e44d..00000000 --- a/modules/ROOT/pages/cassandra.adoc +++ /dev/null @@ -1,167 +0,0 @@ -= Neo4j and Cassandra -:level: Intermediate -:page-level: Intermediate -:author: Neo4j -:category: integrations -:tags: integrations, cassandra, tools -:description: The goal of this guide is to export data from Cassandra, convert to a property graph model and insert into Neo4j. To accomplish this we will use the Neo4j Cassandra data import tool, a prototype command line application that enables translation from a column-oriented data model to a property graph data model. - -.Goals -[abstract] -{description} - -[NOTE] -The Neo4j Cassandra data import tool is in its infancy and currently has many limitations. -It is currently a simple prototype meant to support a limited data model. We'd appreciate any feedback you might have, please raise an issue on the link:https://github.com/neo4j-contrib/neo4j-cassandra-connector/issues[GitHub project]. - -.Prerequisites -[abstract] -You should have an understanding of Cassandra, Neo4j and be familiar with column-oriented and property graph data models. -You will need to have Cassandra, Neo4j and Python 3.x installed. - -[role=expertise {level}] -{level} - -[#neo4j-cassandra] -== Overview - -image::{img}20160203161027/datamodel.png[title='Translating from a column-oriented data model to a property graph.'] - -The main goal of the link:https://github.com/neo4j-contrib/neo4j-cassandra-connector[Cassandra Neo4j data import tool] is to provide a direct way to map a Cassandra schema to Neo4j and import result sets that come from Cassandra columns to Neo4j property graph model, generating a meaningful representation of nodes and relationships. This translation is specified by populating a YAML file based on the Cassandra schema to specify how the data should be mapped from a column-oriented data model to a property graph. The tool exports data from Cassandra using the Cassandra Python driver into CSV format as an intermediate step. LOAD CSV cypher statements are then generated based on the data model mapping specified for loading the data into Neo4j. The following sections will guide you through this process and also provide some mapping examples. - -[NOTE] -At this point, only Python 3.x is supported - -[#install-cassandra] -== Install - -* Both link:http://cassandra.apache.org/download/[Cassandra] and link:http://neo4j.com/download[Neo4j] should be installed and running. -* Clone link:https://github.com/neo4j-contrib/neo4j-cassandra-connector[this GitHub repository]: ```git clone https://github.com/neo4j-contrib/neo4j-cassandra-connector.git``` -* Install project dependencies: `pip install -r requirements.txt` - -[#populate-cassandra] -== Populating an initial Cassandra Database - -We will use a sample database of musicians and songs: - -* A sample database is included that works with this example. Simply go to `db_gen` directory, start Cassandra shell `cqlsh` and invoke the command `SOURCE '/playlist.cql'`. You can also provide the absolute path of the file. This will populate your Cassandra database with a sample Tracks and Artists database. - -[#inspect-schema] -== Inspect the Cassandra schema - -After populating your initial database, you must generate a file to properly map a Cassandra Schema to a graph. Do the following: - -* Into the project directory, navigate to the subfolder __connector/__ -* Run the script `connector.py`. Invoke it with `python connector.py parse -k playlist`. -* Some output files will be generated. At this stage, take a look into the generated `schema.yaml` file. It contains a YAML representation of the Cassandra schema with placeholders for specifying how to convert this Cassandra schema into a Neo4j property graph data model. - -The next step consists of populating the placeholders in this file with mapping information. Check out the next section for more information. - -[#data-model-config] -== Configure data model mappings - -In order to import data into Neo4j the mapping from Cassandra schema to Neo4j property graph must be specified. This is done by populating the placeholders in the generated schema.yaml file. - -__schema.yaml__ file for the sample database: - -[source,sql] ----- -CREATE TABLE playlist.artists_by_first_letter: - first_letter text: {} - artist text: {} - PRIMARY KEY (first_letter {}, artist {}) -CREATE TABLE playlist.track_by_id: - track_id uuid PRIMARY KEY: {} - artist text: {} - genre text: {} - music_file text: {} - track text: {} - track_length_in_seconds int: {} -NEO4J CREDENTIALS (url {}, user {}, password {}) ----- - -[NOTE] -* Every table will be translated as a Node in Neo4j. -* The keyspace from Cassandra will be translated as a label for every generated node in Neo4j. - -Note the __{}__. It's possible to fill them up with the following options: - -* _p_, for regular node property (fill with __\{p\}__), -* __r__ for relationship (fill with __\{r\}__), -* __u__ for unique constraint field (fill with __\{u\}__) - -For example: - -[source,sql] ----- -CREATE TABLE playlist.artists_by_first_letter: - first_letter text: {p} - artist text: {r} - PRIMARY KEY (first_letter {p}, artist {u}) -CREATE TABLE playlist.track_by_id: - track_id uuid PRIMARY KEY: {u} - artist text: {r} - genre text: {p} - music_file text: {p} - track text: {p} - track_length_in_seconds int: {p} ----- - -This will create a propery graph with nodes for the artists and tracks, with a relationship connecting the artist to the track. - -There's also one last line at the end of the file, that requires Neo4j address and credentials: - -[source,sql] ----- -NEO4J CREDENTIALS (url {"http://localhost:7474/db/data"}, user {"neo4j"}, password {"neo4jpasswd"}) ----- - -If you have turned off authentication, you can leave __user__ and __password__ fields empty: - -[source,sql] ----- -NEO4J CREDENTIALS (url {"http://localhost:7474/db/data"}, user {}, password {}) ----- - -An example of filled YAML file can be found on __connector/schema.yaml.example__. - -=== Important points to consider when mapping: - -For this first version, we do not have a strong error handling. So please be aware of the following aspects: - -* If you populate a field as a relationship between two nodes, please map the field with __r__ in both table. In the example above, note that __artist__ is mapped as __r__ in both tables, __playlist.track_by_artist__ and __playlist.track_by_id__. In this initial version keys must have the same name to indicate a relationship. - -* Regarding unique constraints: be sure that you will not have more than one node with the property that you selected for creating this constraint. __u__ is going to work **only** for lines that have been marked with __PRIMARY KEY__. For example: `PRIMARY KEY (first_letter \{p\}, artist \{u\})` This example denotes that __artist__ is selected to be a constraint. We cannot have more than one node with the same artist. - -* To avoid performance issues, try to promote fields to constraints if you notice that it would reduce the number of reduced nodes (of course considering the meaningfulness of the modelling). - -[#import-to-neo4j] -== Import to Neo4j - -After populating the empty brackets, save the file and run the script `connector.py`, now specifying the tables you wish to export from Cassandra: - -[source,shell] ----- -python connector.py export -k playlist -t track_by_id,artists_by_first_letter ----- - -The schema YAML file name (if different than `schema.yaml`) can also be specifed as a command line argument. For example: - -[source,shell] ----- -python connector.py export -k playlist -t track_by_id,artists_by_first_letter -f my_schema_file.yaml ----- - -image::{img}20160203161028/neo4j_cassandra.png[title='Neo4j Cassandra data import tool'] - -[#mapping-cassandra] -== Mapping data into Cassandra to Neo4j - -The YAML file will be parsed into Cypher queries. A file called **cypher_** will be generated in your directory. It contains the Cypher queries that will generate Nodes and Relationship into a graph structure. After generated, the queries are automatically executed by http://py2neo.org/2.0/[Py2Neo] using the Neo4j connection parameters specified in `schema.yaml`. - -Using the sample Artists and Tracks dataset, we have __Track__ nodes and __Artist__ nodes, connected by artist fields. We also wanted to make a constraint on artist by its name - we could not have two different nodes with similar artist names. - -image::{img}20160204123739/graph_data_model.png[title='Property graph data from sample playlist database'] - -[NOTE] -The Neo4j Cassandra data import tool is in its infancy and currently has many limitations. It is currently a simple prototype meant to support a limited data model. We'd appreciate any feedback you might have, please raise an issue on the link:https://github.com/neo4j-contrib/neo4j-cassandra-connector/issues[GitHub project]. diff --git a/modules/ROOT/pages/cla.adoc b/modules/ROOT/pages/cla.adoc deleted file mode 100644 index 4d94254f..00000000 --- a/modules/ROOT/pages/cla.adoc +++ /dev/null @@ -1,65 +0,0 @@ -= Contributor License Agreement -:author: Neo4j -:category: development -:tags: community, contributions, cla - -[#cla-summary] -== Summary - -We require all source code that is hosted on the Neo4j infrastructure to be contributed through the http://dist.neo4j.org/neo4j-cla.pdf[Neo4j Contributor License Agreement^] (CLA). - -The purpose of the Neo4j Contributor License Agreement is to protect the integrity of the code base, which, in turn, protects the community around that code base: the founding entity (Neo4j, Inc.), the Neo4j developer community, and Neo4j users. - -This kind of contributor agreement is common amongst free software and open source projects (it is, in fact, very similar to the widely-signed http://www.oracle.com/technetwork/community/oca-486395.html[Oracle Contributor Agreement^]). - -Please see the below or send an email to cla@neo4j.com if you have any other questions about the intent of the CLA. If you have a legal question, please ask a lawyer. - -If you work at *IBM*, please use this https://dev.assets.neo4j.com/wp-content/uploads/neo4j-cla_IBM_20231101_v2.1.pdf[CLA]. - -[#common-questions] -== Common questions - -=== Am I losing the rights to my own code? - -No, the http://dist.neo4j.org/neo4j-cla.pdf[Neo4j CLA^] only asks you to _share_ your rights, not relinquish them. - -Unlike some contribution agreements that require you to transfer copyrights to another organization, the CLA does not take away your rights to your contributed intellectual property. When you agree to the CLA, you grant us joint ownership in copyright, and a patent license for your contributions. You retain all rights, title, and interest in your contributions and may use them for any purpose you wish. Other than revoking our rights, you can still do whatever you want with your code. - -=== What can you do with my contribution? - -We may exercise all rights that a copyright holder has, as well as the rights you grant in the http://dist.neo4j.org/neo4j-cla.pdf[Neo4j CLA^] to use any patents you have in your contributions. As the CLA provides for joint copyright ownership, you may exercise the same rights as we in your contributions. - -=== What are the community benefits of this? - -It allows us to sponsor the Neo4j projects and provide an infrastructure for the community, while making sure that we can include this in software that we ship to our customers without any nasty surprises. Without this ability, we as a small company would be hard pressed to release all our code as free software. - -Moreover, the CLA lets us protect community members (both developers and users) from hostile intellectual property litigation should the need arise. This is in line with how other free software stewards like the http://www.fsf.org[Free Software Foundation -- FSF^] defend projects (except with the FSF, there's no shared copyright but instead you completely sign it over to the FSF). The contributor agreement also includes a "free software covenant," or a promise that a contribution will remain available as free software. - -At the end of the day, you still retain all rights to your contribution and we can stand confident that we can protect the Neo4j community and the Neo4j, Inc. customers. - -=== Can we discuss some items in the CLA? - -Absolutely! Please give us feedback! But let's keep the legalese off the mailing lists. Please mail your feedback directly to cla@neo4j.com and we'll get back to you. - -=== I still don't like this CLA - -That's fine. You can still host it anywhere else, of course. Please do! We're only talking here about the rules for the infrastructure that we provide. - -[#sign-cla] -== How to sign - -When you've read through the CLA, please send a mail to cla@neo4j.com. -Include the following information: - -* Your full name. -* Your e-mail address. -* Your GitHub username. -* An attached copy of the https://dist.neo4j.org/neo4j-cla.pdf[Neo4j CLA^]. -* That you agree to its terms. - -For example: - ----- -Hi. My name is John Doe (john@doe.com, johndoe). -I agree to the terms in the attached Neo4j Contributor License Agreement. ----- diff --git a/modules/ROOT/pages/clojure.adoc b/modules/ROOT/pages/clojure.adoc deleted file mode 100644 index 99deb83d..00000000 --- a/modules/ROOT/pages/clojure.adoc +++ /dev/null @@ -1,61 +0,0 @@ -= Neo4j Community Driver for Clojure Developers -:level: Intermediate -:page-level: Intermediate -:author: Neo4j -:programming-language: clojure -:category: drivers -:tags: clojure, app-development, applications -:description: If you are a Clojure developer, this guide provides an overview of options for connecting to Neo4j. While this guide is not comprehensive it will introduce the different drivers and link to the relevant resources. - -.Goals -[abstract] -{description} - -include::./_includes/common-language-prereq.adoc[] - -[role=expertise {level}] -{level} - -include::./_includes/community-driver-notice.adoc[] - -[#neo4j-clojure] -== Neo4j Clojure Drivers - -image::{img}clojure.png[float=right] - -Clojure drivers either support Neo4j embedded on the JVM or use Neo4j Server via its HTTP or Bolt APIs. - -Members of the Clojure community have invested a lot of time and love to develop all these drivers, so if you use them please provide feedback to the authors. - -include::./_includes/sample_project.adoc[] - -[#clj-lib] -=== Neo4j-clj - -Clojure bindings for Bolt / the Java Neo4j driver, complete with Joplin support for managing database migrations. - -[cols="1,4"] -|=== -| icon:user[] Authors | Christian Betz -| icon:play-circle[] Example Project | https://github.com/gorillalabs/neo4j-clj/blob/master/example/src/example/core.clj -| icon:article[] Article | https://medium.com/neo4j/neo4j-clj-a-new-neo4j-library-for-clojure-2df1a2a45783 -| icon:github[] Source | https://github.com/gorillalabs/neo4j-clj -| icon:book[] Docs | https://raw.githack.com/gorillalabs/neo4j-clj/master/docs/uberdoc.html -| icon:video[] Video | https://www.youtube.com/watch?v=vIgoemc2kNM -|=== - -// https://clojars.org/gorillalabs/neo4j-clj - -[#neocons-lib] -=== Neocons - -An idiomatic, feature-rich Clojure client which supports the Http and Bolt APIs. - -[cols="1,4"] -|=== -| icon:user[] Authors | Michael Klishin, Rohit Aggarwal -| icon:play-circle[] Example Project | {examples}/movies-clojure-neocons -| icon:gift[] Package | https://clojars.org/clojurewerkz/neocons -| icon:github[] Source | https://github.com/michaelklishin/neocons -| icon:book[] Docs | http://clojureneo4j.info/ -|=== diff --git a/modules/ROOT/pages/conferences.adoc b/modules/ROOT/pages/conferences.adoc deleted file mode 100644 index 4d1ce579..00000000 --- a/modules/ROOT/pages/conferences.adoc +++ /dev/null @@ -1,55 +0,0 @@ -= Meet Neo4j at JFokus -:slug: conference -:section: Documentation and Resources -:category: documentation -:tags: confernce, meetup, talk, events, developer, training - -image::https://pbs.twimg.com/profile_banners/47313251/1633359329/1500x500[alt="jfokus",width="500px",float="center",link="https://www.jfokus.se"] - -== Jfokus -Neo4j is attending https://www.jfokus.se[Jfokus^] + -Founded in 2008, Jfokus is an exciting conference for developers, architects, and IT leaders, featuring a lineup of sessions on Java (front-end and back-end), IoT, DevOps, AI, Cloud, future trends, and more. - -📅 2-4 May + -🗺️ Stockholm - Sweden - -=== Presentation - -Wedndesday, 4 May - 11:10 + -https://www.jfokus.se/talks/973[Building secure GraphQL APIs without the Boilerplate^] - -**Abstract:** + -While GraphQL APIs are one of the hottest trends in API development, they are no magic bullet. You still have to write a lot of resolver-code, adjust your API to changing schemas, figure out database performance and handle security properly. - -Backing a GraphQL API with a graph database makes several of these things easier. Queries and mutations can be generated from the schema, GraphQL queries are directly translated into join-free database queries and with built in security directives you can integrate with auth providers and database security. - -In this talk I want to walk you through building a GraphQL API for a conference system from scratch, discussing and demonstrating these aspects live. - -**About https://twitter.com/mesirii[Michael Hunger^]:** + - -image::https://pbs.twimg.com/profile_images/792577726230237184/8ZSDZEvI_400x400.jpg[alt="Michael Hunger",width="150px",float="left",align="text-right",link="https://twitter.com/JMHReif"] -Michael Hunger has been passionate about software development for more than 25 years. For the last 10 years, he has been working on the open source Neo4j graph database filling many roles, most recently leading the Neo4j Labs efforts. - -As caretaker of the Neo4j community and ecosystem he especially loves to work with graph-related projects, users, and contributors. - -As a developer Michael enjoys many aspects of programming languages, learning new things every day, participating in exciting and ambitious open source projects and contributing and writing software related books and articles. Michael spoke at numerous conferences and helped organized several of them. His efforts got him accepted to the JavaChampions program. - -  + -  + - -== Neo4j AuraDB - -image::https://github.com/neo4j-documentation/developer-guides/raw/publish/modules/ROOT/images/Neo4jauraDB.png[width="500px",float="center",alt="Neo4j AuraDB",link="https://dev.neo4j.com/discover-aura"] - -**The Fastest Path to Graph** + -Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers. - -=== https://dev.neo4j.com/discover-aura[Start free^] - -++++ - -++++ - -Built to leverage relationships in data, AuraDB enables lightning-fast queries for real-time analytics and insights. AuraDB is reliable, secure, and fully automated, enabling you to focus on building graph applications without worrying about database administration. - -https://dev.neo4j.com/discover-aura[Start free^] diff --git a/modules/ROOT/pages/contribute.adoc b/modules/ROOT/pages/contribute.adoc deleted file mode 100644 index 304eaef3..00000000 --- a/modules/ROOT/pages/contribute.adoc +++ /dev/null @@ -1,123 +0,0 @@ -= Contributing to Neo4j -:author: Neo4j -:category: development -:tags: community, contributions, share - -[#neo4j-contribute] -Looking for a place to contribute to the Neo4j ecosystem? -This is a great place to start. -Here you will find guides from Neo4j’s community of contributors. - -[#finding-help] -== Help Others in the Community - -Want to help others? - -There are a number of great resources the Neo4j community uses to get quick help from graph database experts. -Don't hesitate to ask when you’re stuck and looking for help. Also, if you're familiar with a particular topic area, please jump in and lend a hand for your fellow graphistas. - -The primary home for Neo4j community discussion is our https://community.neo4j.com[Neo4j Forum^]. - -We also have some other places the community asks questions. - -* http://stackoverflow.com/questions/tagged/neo4j[Neo4j on StackOverflow^] -* http://twitter.com/neo4j[Social: Neo4j on Twitter^] -* https://discord.gg/neo4j[Discord Chat^] -* http://github.com/neo4j/neo4j/issues[Raise Issues: GitHub Issues^] - -[#ask-question-tips] -== Tips for Asking Good Questions - -If you have technical questions, we would love to help you find the answers to them! Please help us help you by following the guidelines below. - -*Write a title that summarizes the specific problem* + -The title may be the first and the last thing that potential viewers see of your post. -Make it count. Make it describe the problem, not your current state of mind. -ALL CAPS and lots of question and exclamation marks are an indication that you wear your underwear on your head ??? not that you have an urgent problem !!! - -*Choose the correct "category" and add the relevant "tags"* + -Both of these serve to narrow down the problem area. -A database creation error on the "Neo4j Desktop" is quite possible, it is almost impossible if installed with a Yum package. -For installation questions specifying the OS is definitely useful. And so on. - -*Explain First* + -What do you want to accomplish? What is the problem? -What have you tried (you have tried something, right...right?)? -Can you reproduce the situation? Which steps need to be followed to get there? - -*Code Second* + -For some weird reason, Verdana 12pt does horrible things to code. -There are a ton of ways to add readable code in your posts, the easiest is to select it and use the preformatted text icon. -Reading hundreds of lines of code and Cypher-queries is the favorite hobby of most of the visitors of the forum. -Just in case it is one of the others that is trying to help you, provide the code that demonstrates the issue and no more. - -*Proofread* + -Are you ready to press "Create"? Take a deep breath. Exhale. -Go once more over what you've just written. Does it look like the kind of question you could answer? - -*I'm not sure my English is good enough...can I get help?* + -Just do the best you can! While technical questions should be in English, there are Local Groups on the forum. -Not only are those good for finding out about local activities, but there might be somebody else there that knows (for example) South-West England English and can help you translate it into six o'clock news English! - -*Be Patient - Be Friendly - Be Polite - Help someone else in turn* + -Somebody is going to spend some of the single most valuable resources for a human on your question. Time. -Giving an indication that you understand the value of that is the least you can do. - -[#speaking] -== Sharing your Love of Graphs - -No matter where you live in the world, there are plenty of ways to share your love of graphs -and help others understand the importance of relationships. - -With thousands of technology and business *conferences* occuring every year around the world, -you can present to your peers and become a recognized expert in your field. Neo4j, via the -link:/speaker-program/[*Neo4j Speaker Program*], can also help and reimburse -travel expenses for community members speaking at many of the top conferences. - -There are also hundreds of face-to-face https://www.meetup.com/topics/neo4j/[*Neo4j meetup groups*] in communities -around the world. Many of these meetup groups regularly look for speakers to share their stories. - -The https://www.meetup.com/Neo4j-Online-Meetup/[*Neo4j Online Meetup*] is a way for you to meet the global community online and share your story. - -We select talks for the Online Meetup and stories for our link:/tag/twin4j/[*Developer Newsletter*] from https://community.neo4j.com/[*Neo4j Community Forum*]. To submit your story, post it in the `Projects & Collaboration` (if including a link to github or website) or `Community Content & Blogs` (if linking to a blog post, -slideshow, video, or article) categories. - -[#contributing] -== Other Ways to Contribute - -The Neo4j project is an open source effort to bring fast, complex data storage and processing to life. -Every form of help is highly appreciated by the community. -Note that you can contribute to Neo4j also by providing documentation or giving feedback on the current documentation. -Basically, in all the places where you can get help, there is also room for contributions. - -* link:/developer/contributing-code/[Contributing Code^] -* link:/developer/cla/[Contributors License Agreement^] -* GitHub http://github.com/neo4j/neo4j/issues[Issues^] and http://github.com/neo4j/neo4j/pulls[Pull Requests^] -* See the https://github.com/neo4j/neo4j/graphs/contributors[list of contributors^] - -[#develop-neo4j] -== Tools for Developing Neo4j - -We are happy users of the following tools that we use daily to develop Neo4j. - -* IDE: https://www.jetbrains.com/idea/[IntelliJ Idea by JetBrains^] - *Thank you so much for the https://www.jetbrains.com/idea/buy/choose_edition.jsp?license=OPEN_SOURCE[OSS license^]!* and Eclipse -* Build System: https://www.jetbrains.com/teamcity/[TeamCity by JetBrains^] - *Thank you so much for the https://www.jetbrains.com/teamcity/buy/choose_edition.jsp?license=OPEN_SOURCE[OSS license^]!* -* Profiler: Yourkit, VisualVM, jvmtop, Java Mission Control, Flight Recorder -* Version Control: Git & http://github.com/neo4j[GitHub^] -* Issue Management: Trello and http://github.com/neo4j/neo4j/issues[GitHub issues^] -* Team Communication: Neo4j Community Site, Discord, Google Hangouts, and Zoom -* Pair Programming: Team Viewer, join.me -* Documents: Google Docs -* Manual, GraphGists, Presentations: http://asciidoctor.org[AsciiDoctor^] -* Programming Language: Java, Build-Tool - Maven -* Libraries - JVM: Scala, Parboiled, Google Collections, JMH, Jetty, Jersey, Jackson, Apache Commons, JUnit -* Libs & Tools: Javascript: D3.js, Angular.js, Grunt, Bower - -[#integration-dev] -== Tools, Libraries, and Drivers - -Neo4j is supported by a rich ecosystem of libraries, tools, drivers and guides provided by partners, users and community contributors. -We want to give an overview about what is available and link to the original sources. -We try to focus on the freely available solutions here and provide links to commercial options where appropriate. - -Read more about it in our link:/developer/integration/[Integrations section]. \ No newline at end of file diff --git a/modules/ROOT/pages/contributing-code.adoc b/modules/ROOT/pages/contributing-code.adoc deleted file mode 100644 index bfbaeb11..00000000 --- a/modules/ROOT/pages/contributing-code.adoc +++ /dev/null @@ -1,144 +0,0 @@ -= Contributing Code -:author: Neo4j -:category: development -:tags: community, contributions, code - -[#code-contrib] -== Intro - -The Neo4j community is a free software and open source community centered around software and components for the Neo4j Graph Database. -It is sponsored by http://neo4j.com/company/[Neo Technology], which provides infrastructure (different kinds of hosting, documentation, etc.) as well as people to manage it. -The Neo4j community is an open community, in so far as it welcomes any member that accepts the basic criteria of contribution. - -Contribution can be in many forms (documentation, discussions, bug reports). -This document outlines the rules of governance for a contributor of code. - -[#governance] -== Governance Fundamentals - -In a nutshell, you need to be aware of the following fundamentals if you wish to contribute code: - -* All software published by the Neo4j project must have been contributed under the Neo4j Code link:../cla[Contributors License Agreement^]. -* Neo4j is a free software and open source community. - As a contributor, you are free to place your work under any license that has been approved by either the http://www.fsf.org/[Free Software Foundation^] or the http://opensource.org[Open Source Initiative^]. - You still retain copyright, so in addition to that license you can of course release your work under any other license (for example a fully proprietary license), just not on the Neo4j infrastructure. -* The Neo4j software is split into components. - A Git repository holds either a single or multiple components. -* The source code should follow the Neo4j Code Style and "fit in" with the Neo4j infrastructure as much as is reasonable for the specific component. - -[#contrib-roles] -== Contributor Roles - -Every individual that contributes code does so in the context of a role (a single individual can have multiple roles). -The role defines their responsibilities and privileges: - -* A _patch submitter_ is a person who wishes to contribute a patch to an existing component. - See xref:workflow[] below. -* A _committer_ can contribute code directly to one or more components. -* A _component maintainer_ is in charge of a specific component. - They can: -** commit code in their component's repository, -** manage tickets for the repository, -** grant push rights to the repository. -* A _Neo4j admin_ manages the Neo4j infrastructure. - They: -** define new components and assign component maintainership, -** drive, mentor and coach Neo4j component development. - -[#workflow] -== Contribution Workflow - -Code contributions to Neo4j are normally done via Github Pull Requests, following the workflow shown below. -Please check the xref:pr-checklist[] before sending off a pull request as well. - -. Fork the appropriate GitHub repository. -. Create a new branch for your specific feature or fix. -. xref:code-tests[Write unit tests for your code, even for small contributions]. -. xref:code-styles[Write code]. -. Write appropriate Javadocs and entries in the Neo4j Manual. -. xref:commit-messages[Commit changes]. -. xref:pr-checklist[Send pull request]. - -[#pr-checklist] -== Pull Request Checklist - -. xref:sign-cla[Sign the CLA]. -. xref:use-rebase[Ensure that you have not added any merge commits]. -. xref:use-rebase[Rebase against the latest master]. -. xref:code-tests[Run all relevant tests]. -. Send the request! - -[#code-tests] -== Unit Tests - -You have a much higher chance of getting your changes accepted if you supply us with small, readable unit tests covering the code you've written. -Also, make sure your code doesn't break any existing tests. -_Note that there may be downstream components that need to be tested as well,_ depending on what you change. - -To run tests, use Maven rather than your IDE to ensure others can replicate your test run. -The command for running Neo4j tests in any given component is `mvn clean test`. - -The general structure of a unit test looks like this: -[source,java] --------------------------------------------- -@Test -public void myTest() -{ - // Given - [ Setup code here, setting the stage and - parameters that are relevant ] - - // When - [ The code that is being tested, preferably - just one line, like calling a method ] - - // Then - [ Assertions on the result ] -} --------------------------------------------- - -[#code-styles] -== Code Style - -The Neo4j Code style is maintained at http://neo4j.github.io/. - -[#commit-messages] -== Commit Messages - -Please take some care in providing good commit messages; an excellent summary of good practices can be found at http://chris.beams.io/posts/git-commit/. -Please also consider the following guidelines: - -* Use _english_. This includes proper punctuation and correct spelling. - Commit messages are supposed to convey some information at a glance -- they're not a chat room. -* Remember that a commit is a _changeset_, which describes a cohesive set of changes across potentially many files. - Try to group every commit as a logical change. - Explain what it changes. - If you have to redo work, you might want to clean up your commit log before doing a pull request. -* If you fix a bug or an issue that's related to a ticket, then refer to the ticket in the message. - For example, _"Added this and then changed that. This fixes #14."_ - Just mentioning #xxx in the commit will connect it to the GitHub issue with that number, see https://github.com/blog/831-issues-2-0-the-next-generation[GitHub issues^]. - Any of these synonyms will also work: - ** fixes #xxx - ** fixed #xxx - ** fix #xxx - ** closes #xxx - ** close #xxx - ** closed #xxx. -* Remember to convey _intent_. - Don't be too brief but don't provide too much detail, either. - That's what `git diff` is for. - -[#sign-cla] -== Signing the CLA - -One crucial aspect of contributing is the link:../cla[Contributors License Agreement]. -In short: make sure to sign the CLA, or the Neo4j project won't be able to accept your contribution. - -[#use-rebase] -== Don't merge, use rebase instead! - -Because we would like each contribution to be contained in a single commit, merge commits are not allowed inside a pull request. -Merges are messy, and should only be done when necessary, e.g. when merging a branch into master to remember where the code came from. - -If you want to update your development branch to incorporate the latest changes from master, use `git rebase`. -For details on how to use rebase, see Git manual on rebase: http://git-scm.com/book/en/Git-Branching-Rebasing[the Git Manual^]. diff --git a/modules/ROOT/pages/createConstraints.cypher b/modules/ROOT/pages/createConstraints.cypher deleted file mode 100644 index bb60c64e..00000000 --- a/modules/ROOT/pages/createConstraints.cypher +++ /dev/null @@ -1,6 +0,0 @@ -CREATE INDEX on :Ticket(id); -CREATE INDEX on :Client(id); -CREATE INDEX on :Product(description); -CREATE INDEX on :TicketItem(netAmount); -CREATE INDEX on :TicketItem(units); -CREATE INDEX on :TicketItem(product); diff --git a/modules/ROOT/pages/data-import.adoc b/modules/ROOT/pages/data-import.adoc deleted file mode 100644 index a97deb32..00000000 --- a/modules/ROOT/pages/data-import.adoc +++ /dev/null @@ -1,68 +0,0 @@ -= Data Import -:author: Jennifer Reif -:category: import-export -:tags: data-import, graph-import, import-csv, json-api, northwind-graph, example-data -:page-pagination: next -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-import/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -[#about-import] -These guides and tutorials are designed to help you understand how to import various types of data into Neo4j. -From JSON to APIs to another database, you can retrieve data from nearly any source and use it to populate your graph. - -[#import-csv] -== Importing CSV files - -One of the most common formats of data is in rows and columns on flat files. -This spreadsheet format is used for a variety of imports and exports to/from relational databases, so it is easy to retrieve existing data this way. - -You can also use this format of data for Neo4j! -The `LOAD CSV` command in Cypher allows us to specify a filepath, headers or not, different value delimiters, and the Cypher statements for how we want to model that tabular data in a graph. - -We will walk through the details of how to take any CSV file and import the data into Neo4j easily and quickly. - -link:/developer/guide-import-csv/[Import CSV from any URL] + -link:/developer/desktop-csv-import/[Neo4j Desktop: Import CSV Files] - -[#import-api] -== Importing API data - -There are now many data sources that use an API to expose data via a URL - many of these in JSON format. -You can also import this type of data into Neo4j using the APOC standard extension library and executing the commands in the Neo4j Browser command line or in a script. - -The `apoc.load.json` command allows us to specify a URL path and any necessary parameters, followed by Cypher statements to model that tree-like data in a graph. - -This guide shows us how to retrieve data from a JSON-based REST API and import it into Neo4j. - -link:/developer/guide-import-json-rest-api/[Importing API Data] - -[#import-relational-graph] -== Import: RDBMS to Graph - -Many existing systems store data in relational or tabular types of formats. -Knowing how to translate and migrate this data into graph data for analyzing the relationships can seem complex. - -There are a variety of tools for migrating data from relational formats into graphs. -In this guide, we want to discuss all of the options and why you can or should choose some over others for your use case. - -link:/developer/relational-to-graph-import/[Importing Relational Data to a Graph] - -[#import-northwind] -== Tutorial: Importing Data - Northwind - -This guide will use a common relational data set (Northwind) and walk us through how to transform and import data from a relational database to Neo4j graph database. - -You will learn what steps are needed to retrieve the data from the relational data store and import the same data as a graph in Neo4j, as well as how to take the relational data model and convert it to graph in the process. - -link:/developer/guide-importing-data-and-etl/[Tutorial: Import Northwind Data] - -[#import-examples] -== Example Data Sets - -We want to provide some of our favorite data sets for you to practice your skills and learn more about importing and querying data in Neo4j! -This guide shows a variety of open-source data sets that you can access and easily import. -Happy learning! - -link:/developer/example-data/[Example Data Sets] diff --git a/modules/ROOT/pages/data-modeling.adoc b/modules/ROOT/pages/data-modeling.adoc deleted file mode 100644 index 8c73460f..00000000 --- a/modules/ROOT/pages/data-modeling.adoc +++ /dev/null @@ -1,65 +0,0 @@ -= Graph Data Modeling -:author: Jennifer Reif -:category: modeling -:tags: graph-modeling, data-model, schema, create-model, translate-model, model-performance, model-example -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-modeling/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -[#graph-modeling] -These guides and tutorials are designed to give you the tools you need to design and implement an efficient and flexible graph database technology through a good graph data model. - -Best practices and tips gathered from Neo4j's tenure of building and recommending graph technologies will provide you with the confidence to build graph-based solutions with rich data models. -The focus of this section is to provide you with the necessary guidelines and tools to help you model your domain as a graph. - -image::{img}graph_data_modeling.jpg[role="popup-link"] - -[#create-graph-model] -== How to Create a Graph Data Model - -To start, this guide will introduce the basic process of designing a graph data model and walk you through the first steps to create a graph data model, building upon the foundations of the property graph data model. - -It will help you determine the questions you need to ask and share design considerations, best practices learned from experts through the years, and tips for building a more flexible and clean data model to structure your data model for the best results. - -xref:guide-data-modeling.adoc[Data Modeling Guide] + -xref:modeling-designs.adoc[Modeling Designs] - -[#rdbms-graph-schema] -== Translating an RDBMS Schema to Graph - -Want to relate your existing knowledge of relational data models to the graph data model? -Or do you already have a relational model you want to convert to graph? -This guide will help you translate that existing ERD skill and design to a graph data model. - -From typical process steps to conversion mappings, we will walk through how the process can differ and what tables and columns look like as a graph. - -link:/developer/relational-to-graph-modeling/[Modeling: RDBMS to Graph] - -[#optimize-graph-model] -== Optimizing Graph Data Models - -Finally, your data model may be working, but you find that performance or other aspects are not giving you the quality you desired. -Believe it or not, your graph data model can affect queries and performance of your use case. - -Learn how to improve your graph solution and maximize the capabilities of what is existing with recommendations for optimization techniques and ideas. - -link:/developer/modeling-tips/[Graph Modeling Tips] - -[#graphgist-models] -== Live Graph Models - GraphGists - -Looking for graph data model examples or ideas or want to share some of your own? -Learn about our Neo4j GraphGists, where the Neo4j community can share examples of their solutions! -Based on your use case or industry, you can find some projects that could aid your design process. - -Visit our link:/graphgists/[GraphGists page^] and explore the many examples the Neo4j user community created! - -link:/developer/graphgist/[Interactive Graph Models] - -[#Online-training] -== Online training - -There is a free online training course that covers graph data modeling: - -* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^] diff --git a/modules/ROOT/pages/data_science_courses.adoc b/modules/ROOT/pages/data_science_courses.adoc deleted file mode 100644 index 8b93fd2d..00000000 --- a/modules/ROOT/pages/data_science_courses.adoc +++ /dev/null @@ -1,34 +0,0 @@ -//obsolete -= Data Science Courses -:category: administration -:tags: cypher, queries, graph-queries, resources, documentation, graph algorithms, data science, training -:page-pagination: previous - -== Data Science Courses - -Neo4j has a number of courses to teach data scientists how to use Neo4j and the Neo4j Graph Data Science Library. - -Here is the suggested learning path for Data Scientists: - -image::{img}DataScientistLearningPath.png[role="popup-link"] - - -=== GraphAcademy Courses - -We recommend that you take these courses to get you started with Neo4j Graph Data Science: - -*Beginner Learning Path:* - -* https://graphacademy.neo4j.com/courses/neo4j-fundamentals/[Neo4j Fundamentals^] -* https://graphacademy.neo4j.com/courses/cypher-fundamentals/[Cypher Fundamentals^] -* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^] -* https://graphacademy.neo4j.com/courses/importing-data/[Importing CSV Data into Neo4j^] - -*Cypher Learning Path:* - -* https://graphacademy.neo4j.com/courses/cypher-intermediate-queries/[Cypher Intermediate Queries^] - -*Data Scientist Learning Path:* - -* https://graphacademy.neo4j.com/courses/gds-product-introduction/[Introduction to Neo4j Graph Data Science^] -* https://graphacademy.neo4j.com/courses/graph-data-science-fundamentals/[Neo4j Graph Data Science Fundamentals^] diff --git a/modules/ROOT/pages/desktop-csv-import.adoc b/modules/ROOT/pages/desktop-csv-import.adoc deleted file mode 100644 index 5c3c8102..00000000 --- a/modules/ROOT/pages/desktop-csv-import.adoc +++ /dev/null @@ -1,599 +0,0 @@ -= How-To: Import CSV Data with Neo4j Desktop -:level: Intermediate -:page-level: Intermediate -:author: Jennifer Reif -:category: import-export -:tags: data-import, import-csv, graph-import, desktop, load-csv, cypher -:description: You will learn how to import data from CSV files to a Neo4j graph database using the Neo4j Desktop application. This tutorial is designed for you to follow along and step through the process. -:page-pagination: -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/appendix/tutorials/guide-import-desktop-csv/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -This guide builds upon the basic concepts discussed in earlier guides. -You should be familiar with the following: - -* link:/developer/graph-database/[What is a Graph Database?] -* link:/developer/neo4j-desktop/[Neo4j Desktop] -* link:/developer/cypher-query-language/[Cypher] -* xref:guide-data-modeling.adoc[Property Graph Data Model] - -You should also have downloaded link:https://neo4j.com/download/[Neo4j Desktop^] and be familiar with navigating the application, as well as creating and starting a database. - - -[role=expertise {level}] -{level} - -[#about-desktop-import] -== Neo4j Desktop Import - -Neo4j Desktop provides a user-friendly interface for starting and creating Neo4j instances, adding or removing plugins, changing configurations, and other functionality. -It also includes some shortcuts and easy access for importing files (such as CSVs) into Neo4j. - -In this guide, you will work with a zipped folder containing three CSV files and import the data to a graph with Neo4j Desktop. -The CSV files contain data for products, orders, and order line items. -You will look at the data in the files later in this guide. - -[#start-db] -== 1. Creating and starting the Neo4j instance - -If you already know how to create a project, create a Neo4j instance (DBMS), and start the DBMS, you can skip to Step 2. - -include::./_includes/desktop-create-project.adoc[] - -include::./_includes/desktop-create-and-start-DBMS.adoc[] - -[#csv-location] -== 2. Putting CSV files in the import folder - -First, download this https://s3.amazonaws.com/dev.assets.neo4j.com/wp-content/uploads/desktop-csv-import.zip[zip file^]. -Uncompress/unzip this file which should yield three CSV files for products, orders, and order details. -You will put these files in the *import* folder that Neo4j expects for imports. - -Open a finder window on your system by hovering over the three dots to the right started DBMS and selecting *Open folder*, and then *Import*: - -image::{img}generic-open_import_folder.png[role="popup-link"] - -Copy or move the three CSV files into this *import* folder on your system: - -image::{img}developer-desktop-csv-import-files_for_import.png[role="popup-link"] - -Now that your files are in the *import* folder, you can import the data into the database managed by the DBMS. -You will use the current table and column format in the CSV files and translate it into nodes and relationships. -This can be done a few different ways, but you will use Cypher's `LOAD CSV` command in this guide. - -[#loadcsv-desktop] -== LOAD CSV - -`LOAD CSV` is a built-in command in Cypher that allows you to read CSV files and append regular Cypher statements to create or update the data as a graph. -You can also use `LOAD CSV` without creating the graph to output samples, counts, or distributions. -This helps to detect incorrect header column counts, delimiters, quotes, escapes, or spelling of header names before the data is written and stored. - -To enter and run Cypher statements on a started DBMS, you can: - -. Use Neo4j Browser: -.. Click the *Open* button for the started DBMS -.. Type or copy Cypher statements into the edit pane. -.. Execute the Cypher with the `play` button on the right. -. Use cypher-shell: -.. Click the drop-down menu to the right of the *Open* button and select *Terminal*. - -image::{img}generic-open_terminal.png[role="popup-link",float=center,width=500] - -[start=b] -.. Enter `bin/cypher-shell`. -.. Enter *neo4j* for the user. -.. Enter the password you specified for the DBMS. -.. All Cypher statements must end with ";" -.. Use `:exit` to quit. - -In Step 2, you downloaded the *.zip* file and copied its CSV files to the *import* folder for the DBMS. -Before you insert anything into your graph database, you should inspect the data in the files a bit. -To do this, you can use the `LOAD CSV` statement. -If you opened the files previously, you may have noticed that two of the files have headers and one does not (*products.csv*). -Let us see how to inspect each type of file. - -First, you can check how many lines are in the CSV files to ensure they didn't get corrupted or cut off from a potential export process. -For the files with headers, you simply add the `WITH HEADERS` clause after `LOAD CSV`, so that it excludes the header row in the count and only counts the rows of data. - -You should execute this Cypher: - -[source, cypher] ----- -//count data rows in products.csv (no headers) -LOAD CSV FROM 'file:///products.csv' AS row -RETURN count(row); ----- - -[source, cypher] ----- -//count data rows in orders.csv (headers) -LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row -RETURN count(row); ----- - -[source, cypher] ----- -//count data rows in order-details.csv (headers) -LOAD CSV WITH HEADERS FROM 'file:///order-details.csv' AS row -RETURN count(row); ----- - -Running these statements should return the following counts: - -* 77 rows for *products.csv* -* 830 rows for *orders.csv* -* 2155 rows for *order-details.csv* - -[#inspect-files] -== View Data with `LOAD CSV` - -Next, you can take a look at what the data looks like in the CSV files and how `LOAD CSV` sees it. -The only line you need to change from the Cypher above is the `RETURN` statement. -Since all of these files have several rows, you will use `LIMIT` to only get a sample. - -[source, cypher] ----- -//view data rows in products.csv -LOAD CSV FROM 'file:///products.csv' AS row -RETURN row -LIMIT 3; ----- - -Your results should look something like this: - -[format="dsv", options="header"] -|=== -row -["1", "Chai", "18"] -["2", "Chang", "19"] -["3", "Aniseed Syrup", "10"] -|=== - -[source, cypher] ----- -//count data rows in orders.csv (headers) -LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row -RETURN row -LIMIT 5; ----- - -Your results should look something like this: - -[options="header",cols="m"] -|=== -|row - -|{ - "orderID": "10248", - "orderDate": "1996-07-04 00:00:00.000", - "shipCountry": "France" -} -|{ - "orderID": "10249", - "orderDate": "1996-07-05 00:00:00.000", - "shipCountry": "Germany" -} -|{ - "orderID": "10250", - "orderDate": "1996-07-08 00:00:00.000", - "shipCountry": "Brazil" -} -|{ - "orderID": "10251", - "orderDate": "1996-07-08 00:00:00.000", - "shipCountry": "France" -} -|{ - "orderID": "10252", - "orderDate": "1996-07-09 00:00:00.000", - "shipCountry": "Belgium" -} -|=== - -[source, cypher] ----- -//count data rows in order-details.csv (headers) -LOAD CSV WITH HEADERS FROM 'file:///order-details.csv' AS row -RETURN row -LIMIT 8; ----- - -Your results should look something like this: - -[options="header",cols="m"] -|=== -|row - -|{ - "quantity": "12", - "productID": "11", - "orderID": "10248" - } -|{ - "quantity": "10", - "productID": "42", - "orderID": "10248" - } -|{ - "quantity": "5", - "productID": "72", - "orderID": "10248" - } -|{ - "quantity": "9", - "productID": "14", - "orderID": "10249" - } -|{ - "quantity": "40", - "productID": "51", - "orderID": "10249" - } -|{ - "quantity": "10", - "productID": "41", - "orderID": "10250" - } -|{ - "quantity": "35", - "productID": "51", - "orderID": "10250" - } -|{ - "quantity": "15", - "productID": "65", - "orderID": "10250" - } -|=== - - -Notice that the *orders.csv* and the *order-details.csv* return in a different format from the *products.csv*. -This is because those files have headers, so the column names are returned with the values for those rows. -Since the *products.csv* does not have column names, then `LOAD CSV` just returns the plain data row from the file. - -[#filtering-load] -== Filtering what you load with `LOAD CSV` - -After inspecting the data, you may only want to view or load a subset of the data in the CSV file. -You can filter what you view (or load) as follows: - -[source, cypher] ----- -//count data rows in orders.csv (headers) -LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row -WITH row WHERE row.shipCountry = 'Germany' -RETURN row -LIMIT 5; ----- - -Your results should look something like this: - -[options="header",cols="m"] -|=== -|row - -|{ - "orderID": "10249", - "orderDate": "1996-07-05 00:00:00.000", - "shipCountry": "Germany" - } -|{ - "orderID": "10260", - "orderDate": "1996-07-19 00:00:00.000", - "shipCountry": "Germany" - } -|{ - "orderID": "10267", - "orderDate": "1996-07-29 00:00:00.000", - "shipCountry": "Germany" - } -|{ - "orderID": "10273", - "orderDate": "1996-08-05 00:00:00.000", - "shipCountry": "Germany" - } -|{ - "orderID": "10277", - "orderDate": "1996-08-09 00:00:00.000", - "shipCountry": "Germany" - } -|=== - -[#data-types] -== Data types - -The `LOAD CSV` command reads all values as a string. -No matter how the value appears in a file, it will be loaded as a string with `LOAD CSV`. -So, before you import, you want to ensure you convert any values that are non-string. - -There are a variety of conversion functions in Cypher. -The ones you will use for this exercise are as follows: - -* *toInteger():* converts a value to an integer. -* *toFloat():* converts a value to a float (in this case, for monetary amounts). -* *datetime():* converts a value to a datetime. - -We will look at the values in each CSV file to determine what needs to be converted. - -.Products.csv -The values in the products.csv files are for product ID, product name, and unit cost. -Product ID looks like an integer value that increases with each row, so you can convert this to an integer using the `toInteger()` function in Cypher. -Product name can remain a string since it consists of characters. -The final column is the product unit cost. -Though the sample values from your inspection are all whole numbers, we know that monetary amounts often have decimal place values, so we will convert these values to floats using the `toFloat()` function. - -You can see the Cypher to handle all of these conversions below; however, you are still not loading the values into Neo4j yet. -You will be just viewing the CSV files with converted values. - -[source, cypher] ----- -LOAD CSV FROM 'file:///products.csv' AS row -WITH toInteger(row[0]) AS productId, row[1] AS productName, toFloat(row[2]) AS unitCost -RETURN productId, productName, unitCost -LIMIT 3; ----- - -Your results should look something like this: - -[format="dsv",options="header"] -|=== -productId:productName:unitCost -1:"Chai":18.0 -2:"Chang":19.0 -3:"Aniseed Syrup":10.0 -|=== - -Note that we are using collection positions (row[0], row[1], row[2]) to refer to the columns in the row and improve readability by using aliases to reference them in the return. -In a file that has no headers, this is how to reference values in each position. - -.Orders.csv -The values in the orders.csv (per the column names) are for orderID, orderDate, and shipCountry. -Again, you can evaluate the values and determine any conversions to apply. - -OrderID looks like an integer, so you can convert that using the `toInteger()` function. -The orderDate column is certainly in a date format and will require us to format it using the `datetime()` function. -Finally, the shipCountry values are characters, so you can leave that as a string. - -Just as you did with the last CSV files, let us look at the results of these conversions without importing the data. - -[source, cypher] ----- -LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row -WITH toInteger(row.orderID) AS orderId, datetime(replace(row.orderDate,' ','T')) AS orderDate, row.shipCountry AS country -RETURN orderId, orderDate, country -LIMIT 5; ----- - -Your results should look something like this: - -[format="dsv",options="header",separator=";"] -|=== -orderId;orderDate;country -10248;"1996-07-04T00:00:00Z";"France" -10249;"1996-07-05T00:00:00Z";"Germany" -10250;"1996-07-08T00:00:00Z";"Brazil" -10251;"1996-07-08T00:00:00Z";"France" -10252;"1996-07-09T00:00:00Z";"Belgium" -|=== - -There was one tricky thing with this CSV in the `orderDate` column. -Neo4j's datetime uses the https://en.wikipedia.org/wiki/ISO_8601[ISO 8601^] format which uses the delimiter `T` between the date and time values. -The CSV file does not have the 'T' joining the date and time values but has a space between them instead. -You used the `replace()` function to change the space to the character 'T' and get the string into the expected format. -Then, you wrapped the `datetime()` function around that to convert the changed string to a datetime value. - -.Order-details.csv -The values in the order-details.csv (from column names) are for productID, orderID, and quantity. -Let us look at which ones need to be converted. - -product ID is also from the products.csv file, where you converted that value to an integer. -You will do the same here to ensure you match formats. -The order ID field contains values from the orders.csv file, so you will match your previous conversion and translate this field to an integer, as well. -The quantity field in this file is a numeric value. -You can convert this to an integer with the `toInteger()` function you have been using. - -The results of these conversions are in the code below. -Remember that you still are not loading any data yet. - -[source, cypher] ----- -LOAD CSV WITH HEADERS FROM 'file:///order-details.csv' AS row -WITH toInteger(row.productID) AS productId, toInteger(row.orderID) AS orderId, toInteger(row.quantity) AS quantityOrdered -RETURN productId, orderId, quantityOrdered -LIMIT 8; ----- - -Your results should look something like this: - -[format="dsv",options="header"] -|=== -productId:orderId:quantityOrdered -11:10248:12 -42:10248:10 -72:10248:5 -14:10249:9 -51:10249:40 -41:10250:10 -51:10250:35 -65:10250:15 -|=== - -[#loading-data] -== Loading the data! - -Now that you have determined that the CSV file data looks OK, and you have verified how `LOAD CSV` sees the data and converted any non-string values, you are almost ready to create the data in our graph database! -To do that, you will use Cypher statements alongside the `LOAD CSV` commands you used above. -The `LOAD CSV` will read the files, and the Cypher statements will create the data in your database. - -=== Graph data model - -An important step you need before writing Cypher statements, though, is to determine what the graph structure should look like once you import your file data. -After all, importing the data in the existing table and column data will not provide the value you want to achieve from a graph. -To utilize the graph database fully, you need a graph data model. - -Though there are a variety ways to organize the products and orders in your files, we will save that for another guide and use the below version of the model for this exercise. - -image::{img}developer-desktop-csv-import-data_model.svg[role="popup-link",width=700] - -We have two nodes - one for a product and one for an order. -Each of those nodes have properties from the CSV files. -For the `Product`, we have ID, name, and unit cost. -For the `Order`, we have ID, date/time, and country where it is going. - -The order-details.csv file defines the relationship between those two nodes. -That file has the product ID, the order ID it belongs to, and the quantity of the product on the order. -So, in the data model, this becomes the `CONTAINS` relationship between `Product` and `Order` nodes. -We also include a property of `quantityOrdered` on the relationship because the product quantity value only exists when a product is related to an order. - -Now that you know the types of nodes and relationships you will have and the properties involved, you can construct the Cypher statements to create the data for this model. - -=== Avoiding duplicates and increasing performance - -One final thing you need to think about before you create data in the graph is ensuring values are unique and performance is efficient. -To handle this, you can use constraints. -Just as with other databases, constraints ensure data integrity criteria is not violated, while simultaneously indexing the property with the constraint for faster query performance. - -There are cases for applying indexes to a database before any data and with existing data. -In this exercise, you will add two constraints before you create any data - one for `productId` and one for `orderId`. -This will ensure that, when you create a new node of each of those types or a relationship to connect them, you know the entities are unique and indexed. - -Below is the Cypher for adding indexes. - -[source, cypher] ----- -CREATE CONSTRAINT UniqueProduct ON (p:Product) ASSERT p.id IS UNIQUE; -CREATE CONSTRAINT UniqueOrder ON (o:Order) ASSERT o.id IS UNIQUE; ----- - -[#write-statements] -== Cypher - -Now you are ready to write the Cypher for creating the data in the graph! -You could use `CREATE` statements where you are sure that you will not have duplicate rows in your CSV file and use `MATCH` to find existing data for updates. -However, since it is hard to completely scrub all data and import perfectly clean data from any source, you will use `MERGE` statements to check if the data exists before inserting. -If the node or relationship exists, Cypher will match and return (without any writes), but if it does not exist, Cypher will insert it. -Using `MERGE` can have some performance overhead, but often it is the better approach to maintain high data integrity. - -[NOTE] --- -*Why both constraints and MERGE?* -Using constraints is different from using MERGE. -Statements that create data in violation of the constraint will error, while statements that use `MERGE` will simply return existing values (no errors). -If you use both, you avoid terminating your load statements due to constraint violations, and you also ensure you don't accidentally create duplicates in adhoc queries. --- - -.Products -To start, you will load the products into the graph. -You start with your `LOAD CSV` statement from above, then you add the Cypher to create the data from the CSV into your graph model. -You will use the `MERGE` statement to check if the `Product` already exists before you create it. -The properties will be set to the converted values you handled earlier in this guide. - -[source, cypher] ----- -LOAD CSV FROM 'file:///products.csv' AS row -WITH toInteger(row[0]) AS productId, row[1] AS productName, toFloat(row[2]) AS unitCost -MERGE (p:Product {productId: productId}) - SET p.productName = productName, p.unitCost = unitCost -RETURN count(p); ----- - -If you run that statement, it will return the number of product nodes (`count(p)`) that were created in the database. -You can cross-check that number with the number of rows in the CSV file from earlier (77 rows in products.csv). -You can also run a validation query to return a sample of nodes and review that the properties look accurate. - -[source, cypher] ----- -//validate products loaded correctly -MATCH (p:Product) -RETURN p LIMIT 20; ----- - -Here are the results in Neo4j Browser: - -image::{img}developer-desktop-csv-import-verify_products.svg[role="popup-link"] - -.Orders -Next, you will load the orders. -Again, since you want to verify you do not create duplicate `Order` nodes, you can use the `MERGE` statement. -Just as with products, you start with the `LOAD CSV` command, then add Cypher statements and include your data conversions. - -[source, cypher] ----- -LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row -WITH toInteger(row.orderID) AS orderId, datetime(replace(row.orderDate,' ','T')) AS orderDate, row.shipCountry AS country -MERGE (o:Order {orderId: orderId}) - SET o.orderDateTime = orderDate, o.shipCountry = country -RETURN count(o); ----- - -You can also run a validation query, as before, to verify the graph data looks correct. - -[source, cypher] ----- -//validate orders loaded correctly -MATCH (o:Order) -RETURN o LIMIT 20; ----- - -Here are the results in Neo4j Browser: - -image::{img}developer-desktop-csv-import-verify_orders.svg[role="popup-link"] - -.Order-details -Last, but not least, you will create the relationship between the products and the orders. -Since you expect all of your products and all of your orders to already exist in the graph (that data should have been loaded with the last two files), then you start with `MATCH` to find the existing `Product` and `Order` nodes. -Then, the `MERGE` statement will add the new relationship or match an existing one. - -As you found when you ran a count on the order-details file above, there are 2,155 rows in the CSV. -While this is not a huge number for file imports, you will have Cypher periodically commit the data to the database to reduce the memory overhead of the transaction state. -For this, you can add the `:auto USING PERIODIC COMMIT` clause before the `LOAD CSV` command. -The default value for periodic commit is 1,000, but for this exercise, you will ask Cypher to commit every *500 rows*. -You could decrease this number if you have a lot of memory already allocated to other tasks, or if it is limited. - -[source, cypher] ----- -:auto USING PERIODIC COMMIT 500 -LOAD CSV WITH HEADERS FROM 'file:///order-details.csv' AS row -WITH toInteger(row.productID) AS productId, toInteger(row.orderID) AS orderId, toInteger(row.quantity) AS quantityOrdered -MATCH (p:Product {productId: productId}) -MATCH (o:Order {orderId: orderId}) -MERGE (o)-[rel:CONTAINS {quantityOrdered: quantityOrdered}]->(p) -RETURN count(rel); ----- - -Just as you did above, you can validate the data with the query below. - -[source, cypher] ----- -MATCH (o:Order)-[rel:CONTAINS]->(p:Product) -RETURN p, rel, o LIMIT 50; ----- - -Here are the results in Neo4j Browser: - -image::{img}developer-desktop-csv-import-verify_details.svg[role="popup-link"] - -[#import-wrapup] -== Wrapping up - -Congratulations! -You have successfully loaded 3 CSV files into a Neo4j graph database using Neo4j Desktop! - -The `LOAD CSV` functionality, coupled with Cypher, is exceptionally useful for getting data from files into a graph structure. -The best way to advance your skills in this area is to load a variety of files for various data sets and models. -Practice makes perfect! - -.Increasing the challenge -If you work through this exercise again at a later time, feel free to increase the challenge by coming up with your own data model for these files or try to load some other CSV files to a graph. - -If you have any questions or need assistance using `LOAD CSV`, reach out to us on the https://community.neo4j.com/[Community Site^]! - -To learn more about `LOAD CSV` and its uses, check out our guide on link:/developer/guide-import-csv/[LOAD CSV]. diff --git a/modules/ROOT/pages/developer_courses.adoc b/modules/ROOT/pages/developer_courses.adoc deleted file mode 100644 index 88f71d6f..00000000 --- a/modules/ROOT/pages/developer_courses.adoc +++ /dev/null @@ -1,31 +0,0 @@ -//obsolete -= Developer Courses -:category: developer -:tags: cypher, queries, graph-queries, resources, documentation, cypher-training, cypher-help, training -:page-pagination: previous - -== Courses - -Neo4j has a number of courses to teach you how to use Neo4j. - -Here is the suggested learning path for Neo4j developers: - -*1. Beginner Learning Path:* - -* https://graphacademy.neo4j.com/courses/neo4j-fundamentals/[Neo4j Fundamentals^] -* https://graphacademy.neo4j.com/courses/cypher-fundamentals/[Cypher Fundamentals^] -* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^] -* https://graphacademy.neo4j.com/courses/importing-data/[Importing CSV Data into Neo4j^] - -*2. Cypher Learning Path:* - -* https://graphacademy.neo4j.com/courses/cypher-intermediate-queries/[Cypher Intermediate Queries^] - -*3. Developer Learning Path:* - -* https://graphacademy.neo4j.com/courses/app-nodejs/[Building Neo4j Applications with Node.js^] -* https://graphacademy.neo4j.com/courses/app-python/[Building Neo4j Applications with Python^] -* https://graphacademy.neo4j.com/courses/app-java/[Building Neo4j Applications with Java^] -* https://graphacademy.neo4j.com/courses/app-dotnet/[Building Neo4j Applications with .NET^] -* https://graphacademy.neo4j.com/courses/app-go/[Building Neo4j Applications with Go^] - diff --git a/modules/ROOT/pages/discord.adoc b/modules/ROOT/pages/discord.adoc deleted file mode 100644 index 586c2415..00000000 --- a/modules/ROOT/pages/discord.adoc +++ /dev/null @@ -1,88 +0,0 @@ -= Neo4j Discord Chat -:members: 8800 -:author: Neo4j -:category: documentation -:tags: resources, discord, community - -[#join-discord] -== Join our Discord chat - -image::{img}20210324131149/discord-logo-wordmark-color.png[link=https://discord.gg/neo4j,float=right,width=300,window="neo4j-discord"] - -The Neo4j Discord is a friendly chat atmosphere for lively discussion, collaboration or -comaraderie, throughout the week and also during online events. - -For help, the latest news or to share work you've created, please visit our https://community.neo4j.com[Neo4j Forums^] instead! - - -[#getting-support] -== Getting Support - -If you are a customer, please raise a ticket at http://support.neo4j.com[our support portal^] or contact our http://neo4j.com/contact-us[professional services] directly. - -For more involved questions, please raise them in an appropriate category on the https://community.neo4j.com[Neo4j Forums^]. -The forums allow other users to search for questions and answers, sharing knowledge throughout the community! - -[#chat-guidelines] -== Chat Guidelines - -Please don't cross post between channels if not asked so explicitely. - -Don't use any @here, or @channel mentions as many of the channels have a large number of members. - -Please be respectful of varying users and beliefs and avoid content that could be perceived as offensive by others. - - -[#channel-overview] -== Channel Overview - -We've organized the channels into a few different groups. Most channels are for typing. Each -group of channels also has an audio/video channel, for stepping into a room to have a quick conversation. - -[cols="3*<.` option. --- - -Let us go ahead and create our Neo4j container by running the command below. -An explanation of each option is in the following paragraphs. - -[source,bash] ----- -docker run \ - --name testneo4j \ - -p7474:7474 -p7687:7687 \ - -d \ - -v $HOME/neo4j/data:/data \ - -v $HOME/neo4j/logs:/logs \ - -v $HOME/neo4j/import:/var/lib/neo4j/import \ - -v $HOME/neo4j/plugins:/plugins \ - --env NEO4J_AUTH=neo4j/password \ - neo4j:latest ----- - -The `docker run` command creates and starts a container. -On the next line, `--name testneo4j` defines the name we want to use for the container as `testneo4j`. -This avoids us having to reference the container by its generic id, making our container easier to reference and to remember. - -Using the `-p` option with ports 7474 and 7687 allows us to expose and listen for traffic on both the HTTP and Bolt ports. -Having the HTTP port means we can connect to our database with Neo4j Browser, and the Bolt port means efficient and type-safe communication requests between other layers and the database. - -Next, we have `-d`. -This detaches the container to run in the background, meaning we can access the container separately and see into all of its processes. - -The next several lines start with the `-v` option. -These lines define volumes we want to bind in our local directory structure so we can access certain files locally. - -* The first one is for our `/data` directory, which stores the system information and graph data. -* The second `-v` option is for the `/logs` directory. -Outputting the Neo4j logs to a place outside the container ensures we can troubleshoot any errors in Neo4j, even if the container crashes. -* The third line with the `-v` option binds the import directory, so we can copy CSV or other flat files into that directory for importing into Neo4j. -Load scripts for importing that data can also be placed in this folder for us to execute. -* The next `-v` option line sets up our plugins directory. -If we want to include any custom extensions or add the Neo4j APOC or graph algorithms library, exposing this directory simplifies the process of copying the jars for Neo4j to access. -We can also add some plugins with environment variables (`--env` option) as an alternative to mounting the directory. -Either way, some plugins will require modifying Neo4j configuration, which can also be done with the `--env` options. - -On the next line with the `--env` parameter, we initiate our Neo4j instance with a username and password. -Neo4j automatically sets up basic authentication with the `neo4j` username as a foundation for security. -Since it will initiate authentication and require a password change when first connecting, we can handle all of that in this parameter. - -Finally, the last line of the command above references the Docker image we want to pull from DockerHub (`neo4j`), as well as any specified version (in this case, just the `latest` edition). - -[NOTE] --- -Using Docker on Windows will also need a couple of additional configurations because the default `0.0.0.0` address that is resolved with the above command does not translate to `localhost` in Windows. -We can add the following environment variables to our command above to set the advertised addresses: - -[source,bash] ----- - --env NEO4J_dbms_connector_https_advertised__address="localhost:7473" \ - --env NEO4J_dbms_connector_http_advertised__address="localhost:7474" \ - --env NEO4J_dbms_connector_bolt_advertised__address="localhost:7687" \ ----- --- - -When we run this command, it will create and start the container. -We can see this because it generates a container id like in the output below. -Even though it creates a container id, you can reference the container using the `name` we set up in the command - `testneo4j`. - -.Output -[source,bash] ----- -jenniferreif@Jennifer-Reif-MBP docker % docker run \ - --name testneo4j \ - -p7474:7474 -p7687:7687 \ - -d \ - -v $HOME/neo4j/data:/data \ - -v $HOME/neo4j/logs:/logs \ - -v $HOME/neo4j/import:/var/lib/neo4j/import \ - -v $HOME/neo4j/plugins:/plugins \ - --env NEO4J_AUTH=neo4j/password \ - neo4j:latest -b5213186fe621962d0df798ad1e8397ff02f5e70a9a8e1cd8575f7706b7c7e77 ----- - -[#container-status] -== Verifying Execution and Stopping the Container -Once we execute the command above, Neo4j should be running in our Docker container! -You can verify this by running `docker ps`. - -[NOTE] --- -If you do not see your container in the list when you run `docker ps`, you can run `docker ps -a` instead to see if the container crashed and any associated exit codes. --- - -.Output -[source,bash] ----- -jenniferreif@Jennifer-Reif-MBP docker % docker ps -CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES -b5213186fe62 neo4j:latest "/sbin/tini -g -- /d…" 2 minutes ago Up 2 minutes 0.0.0.0:7474->7474/tcp, 7473/tcp, 0.0.0.0:7687->7687/tcp testneo4j ----- - -The above output shows the results of the `docker ps` command, showing the container id, image:version, command, created duration, current status, exposed ports, and the container name. - -Since the container is currently running, we can stop the container (without destroying it) using the `docker stop testneo4j` command. -To start it again, we can execute `docker start testneo4j`. -Output of both those commands is shown below. -We have added `docker ps` commands in between the start and stop, so we can see the status of the container before and after each command. - -.Output -[source,bash] ----- -jenniferreif@Jennifer-Reif-MBP docker % docker stop testneo4j -testneo4j -jenniferreif@Jennifer-Reif-MBP docker % docker ps -CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES - -jenniferreif@Jennifer-Reif-MBP docker % docker start testneo4j -testneo4j -jenniferreif@Jennifer-Reif-MBP docker % docker ps -CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES -b5213186fe62 neo4j:latest "/sbin/tini -g -- /d…" 7 minutes ago Up 2 seconds 0.0.0.0:7474->7474/tcp, 7473/tcp, 0.0.0.0:7687->7687/tcp testneo4j ----- - -If we did not create the container properly, and we want to start over, we will need to destroy the container before executing the docker run again with the same container name. -Running the same run command that we did above will notify us that we cannot create another container with the same name as an existing container. -This is shown in the output below. - -.Output -[source,bash] ----- -jenniferreif@Jennifer-Reif-MBP docker % docker run \ - --name testneo4j \ - -p7474:7474 -p7687:7687 \ - -d \ - -v $HOME/neo4j/data:/data \ - -v $HOME/neo4j/logs:/logs \ - -v $HOME/neo4j/import:/var/lib/neo4j/import \ - -v $HOME/neo4j/plugins:/plugins \ - --env NEO4J_AUTH=neo4j/password \ - neo4j:latest -docker: Error response from daemon: Conflict. The container name "/testneo4j" is already in use by container "b5213186fe621962d0df798ad1e8397ff02f5e70a9a8e1cd8575f7706b7c7e77". You have to remove (or rename) that container to be able to reuse that name. -See 'docker run --help'. ----- - -In order to avoid this, we can destroy the old container (after stopping it) using the `docker rm testneo4j` command. -Once we run this, we can use the same docker run command from earlier to create our container again. -Note: `docker ps` has also been run to verify that the container is up after creating. - -.Output -[source,bash] ----- -jenniferreif@Jennifer-Reif-MBP docker % docker rm testneo4j -testneo4j -jenniferreif@Jennifer-Reif-MBP docker % docker run \ - --name testneo4j \ - -p7474:7474 -p7687:7687 \ - -d \ - -v $HOME/neo4j/data:/data \ - -v $HOME/neo4j/logs:/logs \ - -v $HOME/neo4j/import:/var/lib/neo4j/import \ - -v $HOME/neo4j/plugins:/plugins \ - --env NEO4J_AUTH=neo4j/password \ - neo4j:latest -c851156b2d84ab523d5f233eab717a938c10e09b4cb57ffa1611b402ea09c074 -jenniferreif@Jennifer-Reif-MBP docker % docker ps -CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES -c851156b2d84 neo4j:latest "/sbin/tini -g -- /d…" 3 seconds ago Up 2 seconds 0.0.0.0:7474->7474/tcp, 7473/tcp, 0.0.0.0:7687->7687/tcp testneo4j ----- - -[#commands-exec] -== Executing Other Functionality in Neo4j Containers -Once you are comfortable with creating, starting, and stopping the Docker container, you can start exploring other Neo4j functionality. -Much of the other Neo4j processes for importing data, adding plugins, and interacting via Neo4j Browser work the same way as with any other Neo4j installation with the proper directory volumes mounted. - -=== Cypher and Cypher Shell -To run any Cypher against our database within the container, we can use either Neo4j Browser or the Cypher shell tool. - -.Neo4j Browser -If already familiar with Neo4j Browser, it works the same as with other Neo4j instances. -First, ensure the database is running, then open a browser window and enter the url `localhost:7474`. - -.Cypher Shell -If we want to run Cypher directly in our container, we need to first access our container. -We will need to use the command below in order to run any commands in a running container. -In this case, we are telling docker to run bash within our container, allowing us to interact with our container using Linux bash commands. -For a full list of options, check out https://docs.docker.com/engine/reference/commandline/exec/[Docker's info^] on the `exec` command. - -[source,bash] ----- -docker exec -it testneo4j bash ----- - -After the above command is run, we can now access Cypher shell by running the `cypher-shell` command, which is shown below. -Notice that we also need to specify the username (`-u neo4j`) and password (`-p password`) in order to access the database, using the authentication values we set up when we created the container. - -[source,bash] ----- -cypher-shell -u neo4j -p password ----- - -We can use the returning prompt to write and run various Cypher statements against our data. -The image below shows the command and prompt to access Cypher shell, as well as a query to see how many nodes are in the database (at this point, 0). -The final command exits Cypher shell using `:exit` and returns to our bash prompt. - -.Commands and Output -image:{img}docker_cypher_shell.jpg[role="popup-link"] - -=== Overriding Default Config -If you do need to modify any of the preset configuration values, you can do so in a couple of different ways. -However, the recommended approach is to use environment variables, unless situations require otherwise. - -*Set environment variables for altering configurations* + -Defaults are set for many Neo4j configurations, such as pagecache and memory (512M each default). -To change any configurations, we can use the `--env` parameter in our `docker run` command to set different values for the settings we want to change. -*Note:* dot characters (`.`) become underscores (`\_`) and underscores become double underscores (`__`). -[source,bash] --- -docker run \ - ... \ - --env NEO4J_dbms_memory_pagecache_size=1G \ - neo4j:latest --- - -For other ways to customize configuration for certain needs, you can take a look at our {opsmanual}/docker/configuration/[documentation^]. - -=== Authentication -As we have discussed and shown above, Neo4j (by default) requires authentication and requires us to login with `neo4j/neo4j` at the first connection and set a new password. - -Just as we did above, we can set the password for the Docker container directly by specifying `--env NEO4J_AUTH=neo4j/` in the run directive. -We could also disable authentication entirely by specifying `--env NEO4J_AUTH=none` instead. - -Another way is to run Neo4j as a non-root user by altering the `docker run` command with a different option. -Instead of the `--env`, we can use the `--user` option and pass in the user's id and group for access. -We can see an example of this below, where it passes in the current user and group as the authentication. - -[source,bash] ----- -docker run \ - ... \ - --user="$(id -u):$(id -g)" \ - neo4j:latest ----- - -[#wrap-up] -== Wrapping Up -Congratulations! -You have successfully created and started a Neo4j graph database in a Docker container! - -If you have any questions or need assistance using Neo4j with Docker, reach out to us on the https://community.neo4j.com/[Community Site^]! - -To learn more about running Neo4j with Docker, check out our {opsmanual}/docker/[documentation^]. diff --git a/modules/ROOT/pages/docker.adoc b/modules/ROOT/pages/docker.adoc deleted file mode 100644 index 657c7772..00000000 --- a/modules/ROOT/pages/docker.adoc +++ /dev/null @@ -1,84 +0,0 @@ -= Neo4j with Docker -:level: Intermediate -:page-level: Intermediate -:docker-hub-link: https://hub.docker.com/_/neo4j/ -:author: Neo4j -:category: environment -:tags: administration, docker, docker-image, deployment -:page-deprecated-title: the Neo4j Operations Manual -:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/docker/ - -// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -image::{img}neo4j-docker-image-now-in-beta-235x300.jpg[float="right"] - -[#neo4j-docker] -== Running Neo4j with Docker - -.TL;DR -[source,bash] ----- -docker run -p7474:7474 -p7687:7687 -e NEO4J_AUTH=neo4j/s3cr3t neo4j -# then open http://localhost:7474 to connect with Neo4j Browser ----- - -==== - -[[what-is-docker]] -*What is Docker* - -https://docker.com[Docker^] is a lightweight virtualization mechanism to run single applications or processes in a containerized environment on a Linux host system. -It is designed to handle a small piece of functionality in each container and scale according to needs. -Docker containers can be used as infrastructure layers, data containers, or configuration providers. - -The containers are built from images that can be vendor-provided or user-defined. -To build a Docker image, you create a specification file (`Dockerfile`) to define the minimum-required, dependent layers for the application or service to run. -The steps in the Dockerfile describe the operations for adding the necessary filesystem content for each layer. -You can run as many Docker instances on your host as your resources allow because each container is isolated from any others. -==== - -[#docker-image] -== The official Neo4j Docker Image - -Neo4j provides and maintains official {docker-hub-link}[Neo4j Docker images^] on DockerHub for both Neo4j Community and Enterprise link:/subscriptions/#editions[editions^]. -Releases for current and previous versions of the image are also provided. -A list of the previous versions is available under the {docker-hub-link}[tags section^] of the DockerHub page. - -[#run-neo4j-docker] -== How to use the Neo4j Docker Image - -There are several ways to leverage Docker for your Neo4j development and deployment. -You can create throw-away Neo4j instances of many different versions for testing and running your applications. -You can also pre-seed containers with datasets, extensions, and configurations for interaction and processing. - -The step-by-step instructions on starting Docker containers for Neo4j are given in our link:/developer/docker-run-neo4j/[how-to guide]. -There is also documentation in our link:/docs/operations-manual/current/docker/[operations manual^] on running Neo4j with Docker and how to configure it, run clusters, and handle security. - -[NOTE] --- -By default, the docker image does not have certificates installed. -This means that you will need to disable encryption when connecting with a driver. --- - -[#neo4j-docker-eval] -== Evaluating Neo4j on Docker - -We also use Neo4j on Docker internally for some of our tools and functionality. -From building solutions to live demos, deploying Neo4j with Docker is a valuable capability. -Probably our best-known examples of Neo4j deployed with Docker containers are the Neo4j Sandboxes. -These sandboxes are Neo4j instances in Docker containers running on a shared cloud server. -Each sandbox is independent and separated from the others, allowing users to spin up contained environments for trying out and testing Neo4j! - -In each sandbox use case, we specify certain configurations, data sets, and extensions/plugins to include, and each user's queries and exploration is specific to that assigned container. -Once the life of a Neo4j Sandbox is complete (maximum of 10 days), the container is shut down. -If you want to see how Neo4j works in a Docker container, go ahead and create a link:/sandbox/?ref=developer-docker[Neo4j Sandbox^]. -Note that we do have some configuration presets to restrict certain access and limit functionality. - -[#docker-resources] -== Resources - -* https://hub.docker.com/_/neo4j[Official Neo4j Image on Docker Hub] -* link:/developer/docker-run-neo4j/[How-To: Run Neo4j with Docker] -* link:/docs/operations-manual/current/docker/[Manual: Neo4j on Docker reference^] -* {docker-hub-link}[DockerHub: Neo4j Docker images^] -* https://github.com/neo4j-contrib/docker-neo4j[Github: Neo4j Docker repository^] diff --git a/modules/ROOT/pages/dotnet.adoc b/modules/ROOT/pages/dotnet.adoc deleted file mode 100644 index becaae73..00000000 --- a/modules/ROOT/pages/dotnet.adoc +++ /dev/null @@ -1,90 +0,0 @@ -= Using Neo4j from .NET -:level: Intermediate -:page-level: Intermediate -:author: Neo4j -:programming-language: dotnet -:category: drivers -:tags: dotnet, project, official-driver, app-development, applications -:description: If you are a .NET developer, this guide provides an overview of options for connecting to Neo4j. While this guide is not comprehensive it will introduce the different drivers and link to the relevant resources. -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/neo4j-dotnet/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -include::./_includes/common-language-prereq.adoc[] - -[role=expertise {level}] -{level} - -[#neo4j-dotnet] -== Neo4j for .NET Developers - -image::{img}dotnet-logo.png[float=right,width=300] - -Neo4j provides drivers which allow you to make a connection to the database and develop applications -which create, read, update, and delete information from the graph. - - -[.discrete.ad] -== link:https://graphacademy.neo4j.com/?ref=guides[Learn with GraphAcademy^] - -image::https://graphacademy.neo4j.com/courses/app-dotnet/badge/[float=left] - -[.discrete] -=== link:https://graphacademy.neo4j.com/courses/app-dotnet/?ref=guides[Building Neo4j Applications with .NET^] - -In this free course, we walk through the steps to integrate Neo4j into your .NET projects. -You will learn about the Neo4j .NET Driver, how sessions and transactions work, how to query Neo4j and process results. - -link:https://graphacademy.neo4j.com/courses/app-dotnet/?ref=guides[Learn more^,role=button] - -[#dotnet-driver] -== Neo4jDotNetDriver - -include::./_includes/dotnet-driver.adoc[] - -//// -[role=side-nav] -* https://wintellectnow.com/Videos/Watch?videoId=getting-started-with-graph-databases-using-neo4j[Tutorial: Neo4j on .Net Now by Brian Lanham^] -* http://blog.tatham.oddie.com.au/2014/02/11/podcast-graph-databases-and-neo4j-with-richard-and-carl-from-net-rocks/[Podcast: .Net Rocks with Tatham Oddie^] -* link:/developer/in-production/guide-cloud-deployment#_windows_azure[Cloud Deployment to Azure] -* https://chocolatey.org/packages?q=neo4j[Neo4j Chocolatey Package by Glenn Sarti^] -* http://jim-salmons.github.io/neo4jcp/[Tool: Neo4j Control Panel by Jim Salmons^] -//// - -include::./_includes/sample_project.adoc[] - -include::./_includes/community-driver-notice.adoc[] - -[#neo4jclient-lib] -=== Neo4jClient - -A .NET client for Neo4j, which makes it easy to write Cypher queries in C# with IntelliSense. -It also supports basic CRUD and legacy indexing. - -[cols="1,5"] -|=== -| icon:github[] Source | https://github.com/DotNet4Neo4j/neo4jclient -| icon:gift[] NuGet Package | https://nuget.org/packages/neo4jclient -| icon:user[] Authors | http://twitter.com/cskardon[Charlotte Skardon^] http://twitter.com/tathamoddie[Tatham Oddie^] -| icon:book[] Docs | https://github.com/DotNet4Neo4j/Neo4jClient/wiki[] -| icon:play-circle[] Example | {examples}/movies-dotnet-neo4jclient -| Protocol | Bolt, Http -|=== - -[#neo4j-driver-extensions] -=== Neo4j.Driver.Extensions - -`Neo4j.Driver.Extensions` provides a set of extension methods to the official driver API, aiming at reducing boilerplate and easing mapping to entity classes. - -[cols="1,5"] -|=== -| icon:github[] Source | https://github.com/DotNet4Neo4j/Neo4j.Driver.Extensions -| icon:gift[] NuGet Package | https://nuget.org/packages/neo4j.driver.extensions -| icon:user[] Authors | http://twitter.com/cskardon[Charlotte Skardon^] -| icon:book[] Docs | https://xclave.co.uk/2020/10/06/using-neo4j-driver-now-you-can-extend-it/[Introduction blogpost] -|=== diff --git a/modules/ROOT/pages/download-materials.adoc b/modules/ROOT/pages/download-materials.adoc deleted file mode 100644 index 8032da97..00000000 --- a/modules/ROOT/pages/download-materials.adoc +++ /dev/null @@ -1,63 +0,0 @@ -= Free downloadable Neo4j presentation materials -:slug: download-materials -:section: Documentation and Resources -:category: resources -:tags: resources, meetup, community, developer, events, training - -.on Unsplash -[caption="NeONBRAND ",link=https://unsplash.com/photos/1-aA2Fadydc] -image::https://images.unsplash.com/photo-1524178232363-1fb2b075b655?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1350&q=80[Picture of a presentation in progress] - -Have you taken your first steps with Neo4j? Are you motivated about how graph databases can make your live easier? Driven by the prospect of being to understand how your data is connected, rather than if? - -Many of us who have our graph ephiphanies are excited to share our discoveries with our colleagues, acquaintances and members of our technology communities. But as many of us know, creating the content and associated story is easier said then done! - -== Introducing the free downloadable Neo4j presentation materials - -We are delighted to share with you a set of growing resources to help share your graphy knowledge with others. Without further a do, introducing the free downloadable Neo4j presentation materials! - -Don't see a topic you'd like covered? mailto:devrel@neo4j.com[Let us know^], and we'll see what we can do! - -== Usage - -These are your slides to personalise, update, add to and use to help you tell your graph story. They are unbranded and available for you to adapt to your needs. There could be many ways that they may be helpful to you, for example: - -* Doing a meet-up presentation -* Running a lunch and learn session with colleagues -* Doing a client explainer -* Explain the basics of graph to a project team -* Using the slides as an explainer as part of a bigger talk -* And so on! - -As such, we are using the https://creativecommons.org/licenses/by-sa/4.0/[CC BY SA 4.0^] license for these slides. - -== Available slide packs - -Check out the different slide packs currently available below. All slides are Google Slides, and can be downloaded to other formats. More information on downloading can be found https://support.google.com/docs/answer/49114?hl=en&ref_topic=9052636#zippy=%2Cdownload-a-copy-of-a-file[here^]. - -=== Introduction to Graph Databases - -image::https://github.com/neo4j-documentation/developer-guides/blob/3d427a8cbe9f38cdcc41bbd0fe6dbf6a0b48b3c5/modules/ROOT/images/diagram.png?raw=true[Graph diagram],width="200px",float="right"] - -This pack covers what is a property graph database, how they are different to relational databases, and common scenarios where graph databses are a good fit. This is a good primer for introducing Neo4j, as well as highlighting resources available to learn more. - -https://dev.neo4j.com/intro_to_neo4j_slides[Get the pack here^] - -=== Introduction to Cypher - -//image::https://github.com/neo4j-documentation/developer-guides/blob/publish/modules/ROOT/images/_cdn/cypher/cypher_learning.jpg[Cypher Example,width="200px",float="left"] -image::https://github.com/neo4j-documentation/developer-guides/blob/publish/modules/ROOT/images/_cdn/cypher/cypher-basics-i/img/cypher_pattern_simple.png?raw=true[Placeholder,width="200px",float="left"] - -One of the most powerful features of Neo4j is the query language itself, Cypher. This pack covers an overview of Cypher, including an introduction to key words and structure. - -https://dev.neo4j.com/intro_to_cypher_slides[Get the pack here^] - -=== (Coming soon!) Hands-on Neo4j - -image::https://github.com/neo4j-documentation/developer-guides/blob/publish/modules/ROOT/images/coming_soon.png?raw=true[Coming Soon Image,width="200px",float="right"] - -A copy of our popular hands-on crash course to get people up to speed on what Neo4j is, and a walk-through, hands-on session using either Neo4j Sandbox or Neo4j AuraDB Free cloud database. If you want to see what this might look like, https://youtu.be/ou2st6FYxR8[check out a video here^]. - -== Do you have a talk on...? - -If there's a topic you'd like to see covered, please do mailto:devrel@neo4j.com[let us know^], and we'll see what we can do! diff --git a/modules/ROOT/pages/elastic-search.adoc b/modules/ROOT/pages/elastic-search.adoc deleted file mode 100644 index 429c1717..00000000 --- a/modules/ROOT/pages/elastic-search.adoc +++ /dev/null @@ -1,94 +0,0 @@ -= Neo4j and ElasticSearch -:level: Intermediate -:page-level: Intermediate -:author: Neo4j -:category: integrations -:tags: integrations, tools, elasticsearch, extensions, libraries -:description: There are various ways to integrate Neo4j with ElasticSearch, here we will list some approaches and point to solutions that enable you to reuse your existing ES infrastructure with Neo4j. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -You should have a sound understanding of both ElasticSearch and Neo4j, each data model and APIs to leverage them effectively together. - -[role=expertise {level}] -{level} - -[#neo4j-elastic] -== General Observations - -There are several way of integrating Neo4j with ElasticSearch. -In the past the easiest were River plugins which have been discontinued. -You can integrate with Neo4j's TransactionEventHandler to push graph changes directly to ElasticSearch. -Another option is to supply sources and sinks for LogStash. - -Another option would be to implement a full index-provider that uses ES as storage. -This is a tight integration which might suffer from the roundtrip latencies to ElasticSearch's REST API. - -[NOTE] -If you plan to connect Neo4j to ElasticSearch using the default Java driver, please be aware that there are incompatibilities between the Lucene version used by Neo4j and ElasticSearch, so you might need to go with a REST based solution like JEST. - -[#push-elasticsearch] -== Push to ElasticSearch - -Implementing a Neo4j Transaction Handler provides you with all the changes that were made within a transaction. -With the `afterCommit` notification method, we can make sure that we only send data to ElasticSearch that has been committed to the graph. -Using labels as filtering mechanism, you can render a node's properties as a JSON document and insert it asynchronously in bulk into ElasticSearch. - -To register the Kernel Extension, just drop the jar in Neo4j's classpath or `plugins` directory and configure the ElasticSearch-URL and the label/property combinations to trigger and render the update. - -.neo4j.properties ----- -elasticsearch.host_name=http://localhost:9200 -elasticsearch.index_spec=people:Person(first_name,last_name), places:Place(name) ----- - -We provided a sample implementation below, that you can use out of the box or extend for further customization and fine-tuning. - -//* [Introduction] -//* [Documentation] -* https://github.com/neo4j-contrib/neo4j-elasticsearch[GitHub Repository] -* https://github.com/neo4j-contrib/neo4j-elasticsearch#example[Configuration Example] - -[#graphaware-neoelastic] -== GraphAware Neo4j-To-Elasticsearch - -Neo4j plugin providing automatic replication to Elasticsearch - -* https://github.com/graphaware/neo4j-to-elasticsearch[Github Repository] - -[#graph-aided-search] -== GraphAware Graph-Aided-Search - -Elasticsearch plugin providing integration with Neo4j - -* https://github.com/graphaware/graph-aided-search[Github Repository] -* http://graphaware.com/neo4j/2016/04/20/graph-aided-search-the-rise-of-personalised-content.html[Introduction Article] -* https://github.com/graphaware/graph-aided-search-demo[Demo with docker-compose setup] - -//// -== ElasticSearch Index Provider - -To our knowledge there is no Index Provider for ElasticSearch yet. -If you plan to work on this, please let us know. -The Neo4j index provider APIs are quite straightforward to implement, so besides the latency concern mentioned before it should be worthwile. -//// - -[#logstash-integ] -== LogStash integration - -Pere Urbon who's experienced both in Neo4j and ElasticSearch, wrote open-source implementations of sinks and sources provided Neo4j. - -* [Introduction] -* [Documentation] -* [Source Code] - -//// -== River Plugins - -There is an ElasticSearch River plugin which uses Spring Data Neo4j under the hood. -Unfortunatly the River Plugin approach was deprecated by ElasticSearch, so that you might not want to start new projects using it. -//// diff --git a/modules/ROOT/pages/erlang-elixir.adoc b/modules/ROOT/pages/erlang-elixir.adoc deleted file mode 100644 index 62f76bec..00000000 --- a/modules/ROOT/pages/erlang-elixir.adoc +++ /dev/null @@ -1,67 +0,0 @@ -= Erlang and Elixir: Neo4j Community Driver -:level: Intermediate -:page-level: Intermediate -:author: Neo4j -:programming-language: erlang-elixir -:category: drivers -:tags: erlang, elixir, app-development, applications -:description: If you are an Erlang, or today an Elixir developer, this guide provides an overview of options for connecting to Neo4j. While this guide is not comprehensive it will introduce the different drivers and link to the relevant resources. -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/community-drivers/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -include::./_includes/common-language-prereq.adoc[] - -[role=expertise {level}] -{level} - -include::./_includes/community-driver-notice.adoc[] - -[#neo4j-erlang-elixir] -== Neo4j for Erlang/Elixir Developers - -image::{img}erlang.png[float=right,width=150] -image::{img}elixir.png[float=left,width=150] - -Members of the Erlang and Elixir community have invested a lot of time and love to develop these drivers, so if you use them, please provide feedback to the authors. - -include::./_includes/sample_project.adoc[] - -[#elixir-bolt] -== Elixir Bolt.Sips Driver - -Neo4j driver for Elixir wrapped around the Bolt protocol. - -.Features: -* It uses Bolt, Neo4j's standard network protocol, designed for high-performance -* Supports transactions, as well as simple and complex Cypher queries with or without parameters -* Connection pool implementation using "A hunky Erlang worker pool factory", aka: http://github.com/devinus/poolboy[Poolboy^] :) -* Supports Neo4j 3.0.x and forward - -|=== -| icon:user[] Author | http://twitter.com/florin[Florin Patrascu] -| icon:play-circle[] Example Project | {examples}/bolt_movies_elixir_phoenix -| icon:gift[] Package | https://hex.pm/packages/bolt_sips -| icon:github[] Source | https://github.com/florinpatrascu/bolt_sips -| icon:book[] Docs | http://hexdocs.pm/bolt_sips/ -|=== - -[#elixir-rest-wrapper] -== Neo4j.Sips - -A simple Elixir wrapper around the Neo4j graph database REST API. -It aims to help Elixir developers to play with Neo4j and to eventually become the main support for a future https://github.com/elixir-lang/ecto[Ecto^] adapter. - -[cols="1,4"] -|=== -| icon:user[] Author | http://twitter.com/florin[Florin Patrascu] -| icon:play-circle[] Example Project | {examples}/movies-elixir-phoenix -| icon:gift[] Package | https://hex.pm/packages/neo4j_sips -| icon:github[] Source | https://github.com/florinpatrascu/neo4j_sips -| icon:book[] Docs | https://github.com/florinpatrascu/neo4j_sips/blob/master/README.md -|=== diff --git a/modules/ROOT/pages/example-data.adoc b/modules/ROOT/pages/example-data.adoc deleted file mode 100644 index c34f3632..00000000 --- a/modules/ROOT/pages/example-data.adoc +++ /dev/null @@ -1,253 +0,0 @@ -= Guide: Example Datasets -:level: Intermediate -:page-level: Intermediate -:sandbox: https://neo4j.com/sandbox/ -:sandbox-script: https://github.com/neo4j-contrib/sandbox-guides/tree/master/ -:author: Michael Hunger -:category: import-export -:tags: graph-data, data-sets, import-data, graph-examples, sandboxes, graph-gallery -:description: This Guide introduces different example datasets for Neo4j and demonstrates how to import and explore them. -:page-pagination: previous -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/appendix/example-data/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -You should be comfortable installing Neo4j (Desktop, Docker) or using an instance in the cloud (AuraDB, Sandbox). - -[role=expertise {level}] -{level} - -[#data-sets] -== Datasets - -For getting started with using Neo4j it's helpful to use example datasets relevant to your domain and use-cases. -For each we want to provide a description, the graph model and some use-case queries. - -[TIP] -Have fun, and provide us feedback for this page or create a GitHub issue in the linked repositories. - -[#built-in-examples] -== Built-In Examples - -Neo4j Browser comes with two built-in examples, which you can create and explore using interactive slideshows. - -The *"Movies"* example, is launched via the `:play movie-graph` command and contains a small graph of movies and people related to those movies as actors, directors, producers etc. - -image::{img}browser-example-guides-movies.png[link=https://neo4j.com/developer/guide-neo4j-browser/#_built_in_guides] - -The *"Northwind"* example, is run via `:play northwind-graph` and contains an traditional retail-system with products, orders, customers, suppliers and employees. -It walks you through the import of the data and incrementally complex queries using the available data. - -image::{img}browser-example-guides-northwind.png[link=https://neo4j.com/developer/guide-neo4j-browser/#_built_in_guides] - -[#neo4j-sandbox] -== Neo4j Sandboxes - -To explore a wide variety of datasets in an *online setup* without a local installation, you can use the link:/sandbox/?ref=developer-ex-data[Neo4j sandbox^]. - -Each sandbox is available for at least 3 days after creation and can also be remotely accessed from applications using any Neo4j driver. - -image::{img}neo4j-sandboxes.png[link=https://neo4j.com/sandbox/?ref=developer-ex-data-img] - -Except for the "blank" sandbox, all other sandboxes come prepopulated with the domain data and focus on use-case specific queries. - -All Sandboxes provide access to Neo4j Browser, Neo4j Bloom, APOC, Graph Data Science, neosemantics (n10s) and a GraphQL integration. - -=== Sandbox Dataset GitHub Repositories - -The data, browser guide, code examples (JavaScript, Java, Python, Go, C#), Cypher queries, Bloom perspectives for each Sandbox are all available in https://github.com/neo4j-graph-examples[GitHub repositories^] - -The use-cases range from - -* https://sandbox.neo4j.com?usecase=recommendations[movie recommendations^] (https://github.com/neo4j-graph-examples/recommendations[Repo]) -* https://sandbox.neo4j.com?usecase=network-management[network management^] (https://github.com/neo4j-graph-examples/network-management[Repo]) -* https://sandbox.neo4j.com?usecase=icij-paradise-papers[investigative data from the ICIJ (Panama Papers)^] (https://github.com/neo4j-graph-examples/icij-paradise-papers[Repo]) -* https://sandbox.neo4j.com?usecase=pole[crime investigation^] (https://github.com/neo4j-graph-examples/pole[Repo]) -* https://sandbox.neo4j.com?usecase=twitter-v2[social networks^] optionally using your own Twitter account (https://github.com/neo4j-graph-examples/twitter[Repo]). - - -[[demo-server]] -include::https://raw.githubusercontent.com/neo4j-graph-examples/demo.neo4jlabs.com/master/README.adoc[leveloffset=+1] -// ,lines=0..-5 - -[#guide-examples] -== Other Guide Examples - -Other examples that you can quickly run within your own Neo4j Browser are: - -* `:play got` Game of Thrones Interactions -* `:play nasa` NASA knowledge graph example -* `:play ukcompanies` UK company registration, property ownership, political donations -* `:play stackoverflow` Stack Overflow users, tags and Q&A data -* `:play recipes` BBC Good Foods recipe data -* `:play listings` Airbnb listings data -* `:play football_transfers` Football (Soccer) transfer data -// :play twitter-neo4j-stream -// `:play life-science-import` drug and genome data import and repurposing examples - -[#graph-gallery] -== Graph Gallery - -Even broader is the selection of graph examples that have been *provided by Neo4j users*. - -NOTE: Disclaimer: These examples are not curated and might not always represent the best graph data model. - -You can find a featured selection grouped by industry and use case at https://neo4j.com/graphgists - -image::{img}neo4j-graphgists.png[link=https://neo4j.com/graphgists] - -Those examples are presented in a more long-form style that also discusses data modeling and use an temporary Neo4j store in the background. - -To execute these examples within your Neo4j Desktop, install the *"Graph Gallery"* app from: https://install.graphapp.io - -Then you can search and browse all available examples locally and run them against your *local* databases. - -image::{img}graph-examples.png[link=https://install.graphapp.io] - -If you want to submit your own graph data model example, please head to https://portal.graphgist.org to have a look at even more (non-featured) examples and create your own entries. - -[#means-data-import] -== Means of Data Import - -[#load-data-source] -=== Loading Data from Source Data - -The most reliable way to get a dataset into Neo4j is to import it from the raw sources. -Then you are independent of database versions, which you otherwise might have to upgrade. -That's why we provided raw data (CSV, JSON, XML) for several of the datasets, accompanied by import scripts in Cypher. - -You could run the Cypher script using a command-line client like `cypher-shell`. - -// image::{img}cypher-import-shell.png[] - -.Run Cypher Shell from the "Terminal" of your Graph Database in Neo4j Desktop -[source, shell] ----- -./bin/cypher-shell -u neo4j -p "password" -f import-file.cypher ----- - -You can also drag and drop or paste the script into Neo4j Browser (check that "multi-statement editor" is enabled in the settings) and run it from there. - -image::{img}cypher-import-browser.png[] - -CSV data can be imported using either link:https://neo4j.com/developer/guide-import-csv/#import-load-csv[`LOAD CSV` clause in Cypher^] or https://neo4j.com/docs/operations-manual/current/tutorial/neo4j-admin-import/[`neo4j-admin import` for initial bulk imports^] of large datasets. - -For loading JSON, XML, XLS etc. you need to have the https://neo4j.com/labs/apoc[APOC^] utility library installed, which comes with a https://neo4j.com/labs/apoc/4.2/import/[number of procedures for importing data^] also from other databases. - -[#load-db-copy] -=== Using a dump of a Neo4j database - -Other datasets are provided as dump of a Neo4j datastore. - -.Community Edition (replace the default database) -1. Please stop your Neo4j server -2. Then you can import the file using the `./bin/neo4j-admin load --force true --from file.dump` command. -3. Start the Neo4j server - -.Enterprise Edition (also Neo4j Desktop) -1. Import the file using the `./bin/neo4j-admin load --force true --from file.dump --database ` command. -2. Make the new database known to the system database with `CREATE DATABASE dbname` which will also automatically start it - - -[WARNING] -The Neo4j version of some of the datasets might be older than your Neo4j version. -Then you might need to configure Neo4j to upgrade your database automatically, by setting `dbms.allow_upgrade=true` in your Neo4j settings, or directly in `$NEO4J_HOME/conf/neo4j.conf` - -[#example-datasets] -== Large Data Dumps - -// export URL=example-data.neo4j.org/3.0-datasets/; aws s3 ls s3://${URL} | awk -v url=$URL '{ split($4,a,".");print "* http://" url $4 "[" a[1] " (" int($3/1024/1024) "MB)]"}' - -[#stack-overflow] -=== Stack Overflow - -This is a graph-import of the Stack Overflow archive with 16.4M questions, 52k tags and 8.9M users (http://example-data.neo4j.org/so.dump[Stack Overflow Dump (6.2GB)]). -This graph is pretty big, for global graph queries you'd need a page-cache of 6G and heap of 16G to work with it. - -Here is an https://towardsdatascience.com/tagoverflow-correlating-tags-in-stackoverflow-66e2b0e1117b[article explaining the data model] and some exploratory analysis we ran on the data. - -image::https://cdn-images-1.medium.com/max/1600/0*lOrKWCLdlLGG4BXe.jpg[] - -The database is also available as in the <> as outlined above - -//// -|=== -|Title | Description | Code | Download -|Jim Webber's Doctor Who Dataset | The Dr.Who universe of doctors, actors, enemies and props from the Neo4j Koans Tutorial. | link:http://github.com/jimwebber/neo4j-tutorial[GitHub]| link:http://example-data.neo4j.org/files/drwho.zip[drwho.zip] -|Movie Database | 12k movies, 50k actors. Original Source: http://TheMovieDB.org[TheMovieDB] | link:https://github.com/neo4j-examples/cineasts-spring-data-neo4j[GitHub] | http://example-data.neo4j.org/3.0-datasets/cineasts.tgz[cineasts_12k_movies_50k_actors.tgz (14MB)] -|The Musicbrainz main entities | Most of the interesting entities (800,000 Artists, 12,000,000 Tracks, 1,200,000 Releases, 75,000 Record Labels) from the link:http://musicbrainz.org[Musicbrainz dataset].| link:/blog/musicbrainz-in-neo4j-part-1/[Blog Post] -| http://example-data.neo4j.org/3.0-datasets/musicbrainz.tgz[musicbrainz_21.tgz (4.5GB)] -|=== - -[#graph-databases] -==== Datasets from the Book "Graph Databases" - -You can find the free ebook for the O'Reilly book here: - -|Title | Description | Code | Download - -|Access Control -|A complex access control scenario (p. 116-124) -| src | http://example-data.neo4j.org/3.0-datasets/accesscontrol.tgz[accesscontrol.tgz (165MB)] -|Logistics Routing -|Routing computation with partial leg optimization (p. 12) | src | http://example-data.neo4j.org/3.0-datasets/logistics.tgz[logistics.tgz (59MB)] -| recommendations | desc | src | http://example-data.neo4j.org/3.0-datasets/recommendations.tgz[recommendations.tgz (0MB)] -| social | desc | src | http://example-data.neo4j.org/3.0-datasets/socialnetwork.tgz[socialnetwork.tgz (530MB)] - - -| title | desc | src | http://example-data.neo4j.org/3.0-datasets/cineasts.tgz[cineasts.tgz (14MB)] -| title | desc | src | http://example-data.neo4j.org/3.0-datasets/cineasts_csv.tgz[cineasts_csv.tgz (0MB)] -| title | desc | src | http://example-data.neo4j.org/3.0-datasets/elections.tgz[elections.tgz (64MB)] -| title | desc | src | http://example-data.neo4j.org/3.0-datasets/ldbc_sf001_p006.tgz[ldbc_sf001_p006.tgz (404MB)] -| title | desc | src | http://example-data.neo4j.org/3.0-datasets/ldbc_sf010_p006.tgz[ldbc_sf010_p006.tgz (3939MB)] -| title | desc | src | http://example-data.neo4j.org/3.0-datasets/musicbrainz.tgz[musicbrainz.tgz (4555MB)] -| title | desc | src | http://example-data.neo4j.org/3.0-datasets/pokec.tgz[pokec.tgz (1148MB)] - -//|Federal Election Commission Campaign Data - (63.91MB)| "The 2012 presidential campaign data from our Federal Election Committee Campaign Data link:/blog/follow-the-data-fec-campaign-data-challenge/[Workshop].| link:https://github.com/akollegger/FEC_GRAPH[GitHub]| link:http://example-data.neo4j.org/files/fec_data_presidential_2012.zip[fec_data_presidential_2012.zip] -//|Small Cineasts Movies & Actors (0.14MB)| Small dataset of the Spring Data Neo4j Cineasts.net link:http://spring.neo4j.org/tutorial[tutorial].| link:https://github.com/SpringSource/spring-data-neo4j/tree/master/spring-data-neo4j-examples/cineasts[GitHub] | link:http://example-data.neo4j.org/files/cineasts_39_movies_446_actors.zip[cineasts_39_movies_446_actors.zip] -//|Hubway Data Challenge (50MB) | Hubway is a bike sharing service. The challenge data consists 95 Boston stations and link:blog/using-spring-data-neo4j-for-the-hubway-data-challenge/[500k bike rides]. | link:https://github.com/jexp/hubway-sdn"[GitHub] | link:http://example-data.neo4j.org/files/hubway_data_challenge_boston.zip[hubway_data_challenge_boston.zip] -//|Neo Love (1.6MB) | Neo4j brings the love with a link:http://maxdemarzi.com/2013/04/19/match-making-with-neo4j/[sample dating site].| link:https://github.com/maxdemarzi/neo_love[GitHub] | link:https://dl.dropboxusercontent.com/u/57740873/neo_love.graph.db.zip[neo_love.graph.db.zip] -//|ConceptNet 5 (243MB) | ConceptNet is a link:http://maxdemarzi.com/2013/05/13/knowledge-bases-in-neo4j/[semantic network] built from nodes representing concepts, and labeled relationships between them| link:https://github.com/maxdemarzi/neo_concept[GitHub] | link:https://dl.dropboxusercontent.com/u/57740873/conceptnet.graph.db.zip[conceptnet.graph.db.zip] -//|Neo Permissions (560MB) | Check permissions from files and folders to users and groups in this link:http://maxdemarzi.com/?s=PERMISSION+RESOLUTION[ACL example]. | link:https://github.com/maxdemarzi/neo_permissions/blob/master/performance/src/test/resources/simulations/RandomPermissions.scala[GitHub] | link:https://dl.dropboxusercontent.com/u/57740873/datasets/neo_permissions.tar.gz[neo_permissions.tar.gz] - -=== Graph Generators - -* [LDBC Large Dataset Generator] - -//// - -[#data-import-articles] -== Articles on Neo4j Example Datasets - -These are not prebuilt data-stores but existing datasets (mostly CSV) to be imported. - -The linked articles and repositories also provide instructions for the import. - -* https://medium.com/neo4j/tagged/data[Developer Blog - Import/Export^] -* https://medium.com/neo4j/finding-influencers-and-communities-in-the-graph-community-e3d691296325[Twitter Social Graph Import^] -* https://medium.com/neo4j/graph-databases-for-journalists-5ac116fe0f54[Using Neo4j to explore public contracting data^] -* https://neo4j.com/blog/analyzing-panama-papers-neo4j/[The Panama Papers^] -* https://neo4j.com/developer/guide-importing-data-and-etl/[Northwind Database Import] -* https://neo4j.com/blog/import-10m-stack-overflow-questions/[Importing Stack Overflow into Neo4j] -* https://github.com/caesar0301/awesome-public-datasets[Awesome Public Datasets] -* https://medium.com/@christophewillemsen/github-events-analysis-with-neo4j-18bc6ffcab01[GitHub Event Data] -* https://medium.com/neo4j/whats-cooking-approaches-for-importing-bbc-goodfood-information-into-neo4j-64a481906172[Series: Importing BBC goodfood information into Neo4j^] -* https://medium.com/neo4j/importing-rdfs-owl-ontologies-into-neo4j-23e4e28ebbad[Importing RDFS/OWL ontologies into Neo4j^] -* https://medium.com/neo4j/importing-mapping-metaphor-into-neo4j-90ac9ead4d44[Importing Mapping Metaphor into Neo4j^] -* https://medium.com/@aejefferson/methods-for-loading-data-into-a-remote-neo4j-instance-part-1-abea3328dedf[Series: Methods for Loading Data into a Remote Neo4j Instance^] - -[#data-resources] -== Avid Bloggers on Data Import - -* https://tbgraph.wordpress.com/?s=import[Tomasz Bratanic] -* http://www.markhneedham.com/blog/?s=neo4j+import[Mark Needham] -* http://blog.bruggen.com/search/label/import[Rik van Bruggen] -* http://www.lyonwj.com/?s=import[William Lyon] -* https://medium.com/@mesirii[Michael Hunger] diff --git a/modules/ROOT/pages/example-project.adoc b/modules/ROOT/pages/example-project.adoc deleted file mode 100644 index 460f963d..00000000 --- a/modules/ROOT/pages/example-project.adoc +++ /dev/null @@ -1,267 +0,0 @@ -= Example Project -:level: Beginner -:page-level: Beginner -:author: Neo4j -:category: drivers -:tags: examples, app-development, project, applications -:description: This guide explains the example application we use to introduce the different Neo4j drivers in detail. -:redirect: https://graphacademy.neo4j.com/courses/developer/ - -.Goals -[abstract] -{description} - -include::./_includes/common-language-prereq.adoc[] - -[role=expertise {level}] -{level} - -// tag::intro-app-project[] -[#intro-app-project] -== Example Project Description - -To demonstrate connection to and usage of Neo4j in different programming languages we've created an example application. -It is a http://my-neo4j-movies-app.herokuapp.com/[simple, one-page webapp^], that uses Neo4j's movie demo database (movie, actor, director) as data set. -The same front-end web page in all applications consumes 3 REST endpoints provided by backend implemented in the different programming languages and drivers. - -* movie search by title -* single movie listing -* graph visualization of the domain - -image::{img}movie_application.png[link="http://my-neo4j-movies-app.herokuapp.com/"] - -[#app-project-source] -== GitHub - -The source code for all the different language examples is available on https://github.com/neo4j-examples?query=movies[GitHub^] as individual repositories that can be cloned and directly used as starting points. - -[#app-project-model] -== Domain Model - -We use a simple Movie database domain, which is also built into Neo4j Browser. -If you need the data separately, it is available in this https://github.com/neo4j-graph-examples/movies[GitHub repository^]. - -[source,cypher] ----- -(:Person {name: string})-[:ACTED_IN {roles: [string]}]->(:Movie {title: string, released: number}) ----- - -image::graph-examples-movies-model.svg[width=400] - -An example subgraph of the data is the neighborhood of "Tom Hanks". - -image::graph-examples-movies-example.png[width=400] - -[#app-project-data] -== Data Setup - -=== Neo4j Labs Demo Server - -Most of the examples should already work with the following settings: - -[cols="2*"] -|=== -| icon:link[] Connection URI -| `neo4j+s://demo.neo4jlabs.com` - -| icon:database[] Database Name -| `movies` - -| icon:sign-in[] Username -| `movies` - -| icon:user-secret[] Password -| `movies` -|=== - -Other data setup options are explained <>. - -[#app-project-webpage] -== Webpage - -The webpage is a single, simple Bootstrap page that uses jQuery AJAX calls to access the backend and adds the returned JSON result data to HTML DOM elements in the page. - -For the search, that is the `/search?q=query` endpoint whose results are added as table rows in a listing. - -If you click on any of the movie titles, the right-side view is populated with the movie details. - -For the movie details it uses the `/movie/A%20Movie%20Title` endpoint and the results are rendered as panel title, image source, and unordered list for the crew. - -[#app-project-vis] -== Graph Visualization - -For the Graph Visualization we use http://d3js.org[d3.js^]. Our `/graph` endpoint already returns the data in the format of "nodes" and "links"-list that d3 can use directly. -We then apply the force-layout algorithm to render nodes as circles and relationships as lines, and add some minimal styling to the visualization to provide the movie title/person name as title attribute to the node circles which is shown as a tooltip. - -[#app-project-endpoints] -== Endpoints -=== Search Movies - ----- -// list of JSON objects for movie search results -curl http://localhost:8080/search?q=matrix - -[{"movie": {"released": 1999, "tagline": "Welcome to the Real World", "title": "The Matrix"}}, - {"movie": {"released": 2003, "tagline": "Free your mind", "title": "The Matrix Reloaded"}}, - {"movie": {"released": 2003, "tagline": "Everything that has a beginning has an end", "title": "The Matrix Revolutions"}}] ----- - -The Cypher query used, with the parameters `{query:"matrix"}`. - -The search movies query ``MATCH``es the movies by title with `CONTAINS` and then ``RETURN``s the movie nodes as a list of maps with the `title`, `released` and `tagline` attributes. - -[source,cypher] ----- -MATCH (movie:Movie) - WHERE lower(movie.title) CONTAINS $query - RETURN movie ----- - -=== Get Movie - ----- -// JSON object for single movie with cast -curl http://localhost:8080/movie/The%20Matrix - -{"title": "The Matrix", - "cast": [{"job": "acted", "role": ["Emil"], "name": "Emil Eifrem"}, {"job": "acted", "role": ["Agent Smith"], "name": "Hugo Weaving"}, ... - {"job": "directed", "role": null, "name": "Andy Wachowski"}, {"job": "produced", "role": null, "name": "Joel Silver"}]} ----- - -The Cypher query is used, with the parameters `{title:"The Matrix"}` - -It matches the movie by title, then optionally finds all people working on that movie and returns the movie title and a crew-list consisting of a map with person-name, job identifier derived from the relationship-type and optionally a role for actors. - -This is the Cypher statement used, it finds a movie by title and then returns for all people their name, possible roles, and the job (acted, directed, produced) as the first part of the lowercase rel-type `ACTED_IN -> acted`. - -[source,cypher] ----- -MATCH (movie:Movie {title:$title}) -OPTIONAL MATCH (movie)<-[rel]-(person:Person) -RETURN movie.title as title, -collect({name:person.name, role:rel.roles, job:head(split(toLower(type(rel)),'_'))}) as cast -LIMIT 1 ----- - -=== Graph Visualization - ----- -// JSON object for whole graph viz (nodes, links - arrays) -curl http://localhost:8080/graph[?limit=50] - -{"nodes": - [{"title":"Apollo 13","label":"movie"},{"title":"Kevin Bacon","label":"actor"}, - {"title":"Tom Hanks","label":"actor"},{"title":"Gary Sinise","label":"actor"}, - {"title":"Ed Harris","label":"actor"},{"title":"Bill Paxton","label":"actor"}], - "links": - [{"source":1,"target":0},{"source":2,"target":0},{"source":3,"target":0}, - {"source":4,"target":0},{"source":5,"target":0}]} ----- - -The Cypher query used finds all pairs of movies and actors and returns the movie title and a collection of all actor names as cast. -A separate function then takes this result and converts it into the node- and link-list that d3 expects. - -The parameter `{limit:50}` is used to prevent the visualization from becoming a hairball. - -[source,cypher] ----- -MATCH (m:Movie)<-[:ACTED_IN]-(a:Person) - RETURN m.title as movie, collect(a.name) as cast - LIMIT $limit ----- - - -[#app-project-deploy] -== Deployment - -=== Run Locally - -Then setup and start the language/stack specific implementation of the backend and open the web-application on `http://localhost:8080`. - -You can search for movies by title or click on any result entry to see the details. - -=== Deployment Options - -There are many deployment options, from Docker containers or Kubernetes pods to serverless AWS-lambdas, GCP-cloud-run functions to PaaS environments like Heroku, Cloudfound or DigitialOcean App Platform. - -To keep it simple we only show deployment to Heroku here, more involved deployments will be discussed later. - -=== Deploy to Heroku - -Many of the mentioned https://github.com/neo4j-examples?query=movies[GitHub example repositories^] feature a "Deploy to Heroku" button. -You can either use that or follow the manual process outlined below. - -We want to install our application to the cloud, for instance the http://heroku.com[Heroku^] PaaS. -You can either use the demo server mentioned above or the Neo4j AuraDB Free, Neo4j Sandbox or Cloud Marketplaces shown <> - -Install the https://toolbelt.heroku.com/[Heroku Toolbelt^] and git. - -Then run these commands: - -[source,shell] ----- -# Clone the appropriate repository for your language -# initialize the git repository and add files -git init -git add . -git commit -m"my neo4j movies app" - -# create heroku application, please change the app-name -export app=my-neo4j-movies-app -heroku apps:create $app - -# configure your app to use the connection credentials -# e.g. bolt://user:password@host:port -heroku config:set NEO4J_URL= --app $app - -# or depending on the repository you might have separate username and password environment variables -heroku config:set NEO4J_USER=neo4j --app $app -heroku config:set NEO4J_PASSWORD=neo4j --app $app - -# deploy to heroku -git push heroku master - -# open application -heroku open --app $app ----- - -Then your app is ready to roll. - -[[other-setups]] -== Other Neo4j Database Setup Options - -=== Neo4j AuraDB Free - - * Log in to https://console.neo4j.io/?ref=developer-pages[Neo4j AuraDB Free]. - * Proceed to the database creation page - * Choose AuraDB Free - * Select a region near you - * Pick the movies database - * Confirm and save the credentials - * Wait until the database is started and also save the connection URL - * Then use the connection URL and credentials for your backend setup (environment variables) - -=== Neo4j Desktop - -- (http://neo4j.com/download[Download & Install Neo4j Desktop^]) -- After installation, there is a "first use" graph available -- To change the password from the auto-generated one, click the three dots to Manage and choose the Admistration tab -- Open the Neo4j Browser with the blue "Open" button to explore - -If you want to create a new database, do so within a Desktop Project, then start Neo4j-Browser - -- Install the Movies dataset with `:play movies` -- Click the large `CREATE` statement and hit the triangular "Run" button to insert the data. - -The connection details will be `bolt://localhost`, username: `neo4j` and your chosen password. - -=== Neo4j Sandbox - - - Log in to https://sandbox.neo4j.com/[Neo4j Sandbox]. - - Create a project with the "Movies" dataset and launch it. - - The Connection Details are in the sandbox UI if you unfold the lower part with the black triangle - - -:leveloffset: -1 - -include::./_includes/drivers_example_list.adoc[] diff --git a/modules/ROOT/pages/first-steps-cypher.adoc b/modules/ROOT/pages/first-steps-cypher.adoc deleted file mode 100644 index 81f8393b..00000000 --- a/modules/ROOT/pages/first-steps-cypher.adoc +++ /dev/null @@ -1,93 +0,0 @@ -= First Steps with Cypher -:neo4j-version: 3.0 -:author: Michael Hunger -:style: #65b144/#58b535/white:Person(name),#008cc1/#0f5788/white:Database(name) - -== Create a Record for Yourself - -//setup -[source,cypher] ----- -CREATE (you:Person {name:"You"}) -RETURN you ----- - -`CREATE` creates nodes with labels and properties. - -// graph - -You like _Neo4j_, right? - -Let's find ourselves and add a new relationship to a new node. - -//setup -[source,cypher] ----- -MATCH (you:Person {name:"You"}) -CREATE (you)-[like:LIKE]->(neo:Database {name:"Neo4j" }) -RETURN you,like,neo ----- - -`CREATE` can create single nodes, or more complex structures. - -// graph - -== Create Your Friends - -//setup -[source,cypher] ----- -MATCH (you:Person {name:"You"}) -FOREACH (name in ["Johan","Rajesh","Anna","Julia","Andrew"] | - CREATE (you)-[:FRIEND]->(:Person {name:name})) ----- - -`FOREACH` allows you to execute update operations for each element of a list. - -// graph - -== Find Your Friends - -[source,cypher] ----- -MATCH (you {name:"You"})-[:FRIEND]->(yourFriends) -RETURN you, yourFriends ----- - -// graph_result - -// table - -Note that we get ourselves repeated for each path found in the graph. - -== Create Second Degree Friends and Expertise - -//setup -[source,cypher] ----- -MATCH (neo:Database {name:"Neo4j"}) -MATCH (anna:Person {name:"Anna"}) -CREATE (anna)-[:FRIEND]->(:Person:Expert {name:"Amanda"})-[:WORKED_WITH]->(neo) ----- - -`CREATE` can also add more complex patterns. - -ifndef::env-graphgist[] -image::{img}first_steps_friends_network.jpg[width=400] -endif::env-graphgist[] - -// graph - -== Find Someone in your Network Who Can Help You Learn Neo4j - -[source,cypher] ----- -MATCH (you {name:"You"}) -MATCH (expert)-[:WORKED_WITH]->(db:Database {name:"Neo4j"}) -MATCH path = shortestPath( (you)-[:FRIEND*..5]-(expert) ) -RETURN db,expert,path ----- - -// graph_result - -// table diff --git a/modules/ROOT/pages/get-started-notrack.adoc b/modules/ROOT/pages/get-started-notrack.adoc deleted file mode 100644 index 9d818531..00000000 --- a/modules/ROOT/pages/get-started-notrack.adoc +++ /dev/null @@ -1,161 +0,0 @@ -= Getting Started with Neo4j -:author: Jennifer Reif -:category: documentation -:tags: get-started, introduction, graph, database, graph-platform, training -:description: Become an expert Neo4j developer with these introductory tutorials and guidelines that cover the entire development lifecycle, from download to deployment. - -++++ - -++++ - -[#starting-neo4j] -Taking the first steps with Neo4j? - -These introductory tutorials and developer guides were written to guide you through much of your initial learning for Neo4j and give you resources to continue your education. - -[#first-steps] -== Your First Steps - -Getting started with Neo4j is easy. -Take your first steps to become an expert Neo4j developer. - -1. link:/download/[Install Neo4j^] or link:/sandbox/?ref=developer-start[Start a Sandbox Online^] -2. link:/developer/cypher/[Learn to Create and Query Data with Cypher^] -3. link:/developer/guide-importing-data-and-etl/[Import Your Data^] -4. link:/developer/language-guides/[Build an Application^] -5. Get Help on our https://community.neo4j.com[Neo4j Online Community^] - -[#guide-overview] -== Developer Guide Overview - -We cover a variety of topics - from understanding graph database concepts to building applications that interact with Neo4j to running Neo4j in production. - -Videos, text, examples, and code are just some of the formats in which we deliver the information to encourage you and aid all learning styles. - -[#graph-db] -== What is a Graph Database? - -Connected information is everywhere in the world around us. -Neo4j was built to efficiently store, handle, and query highly-connected data in your data model. -With a powerful and flexible data model, you can represent your real-world, variably-structured information without a loss of richness. - -++++ -
    - -
    -++++ - -link:/developer/graph-database/[Learn More: What is a Graph Database?^, role="button feature-box_button"] - -[#graph-platform] -== Neo4j Graph Platform - -At Neo4j, we want to provide options to solve many different kinds of business and technical needs. -Our goal is that our products are simple and fit your use case, whatever it may be. -Whether you are relying upon graphs for transactions, market analysis, operations optimizations, or anything else, Neo4j strives to provide a seamless process for integrating our tools with the rest of your existing system. - -Capabilities in the Neo4j graph platform include aiding developers to import data to the graph, business analysts to explore the data with ease, and data scientists to make decisions based on analysis results. -No matter your role within your organization, we want to put the power of the graph and Neo4j within reach to help you maximize business value and achieve technical needs. - -link:/developer/graph-platform/[Learn More: Neo4j Graph Platform?^, role="button feature-box_button"] - -=== Graph Data Modeling - -The richness of graph data and the performance of your queries depends closely on how the data is modeled. -Data in Neo4j should resemble the real-world information and relationships, but data modeling focuses on understanding what questions the data will answer and what types of information you need from it. - -Your data model may also impact the queries you write and the performance of the results returned. -Choosing to store certain pieces of data as properties versus separate nodes and determining how to structure your relationships is part of the process to maintain good data and maximize query performance. - -link:/developer/data-modeling/[Learn More: Graph Data Modeling^, role="button feature-box_button"] - -=== Cypher - A Next-Generation Query Language - -We wanted to make querying graph data not only fun, but easy to learn, understand, and use for everyone. -That's why we created a human-friendly, declarative query language which uses ASCII-Art to represent visual graph patterns for finding or updating data in Neo4j. - -Cypher was based on the power of SQL, but optimized specifically for graphs. -The syntax is concise and straightforward, allowing users to easily write all the normal CRUD operations in a simple and maintainable way. - -link:/developer/cypher/[Learn More: Cypher Query Language^, role="button feature-box_button"] - -=== Graph Visualization - -Visualizations often provide a better understanding of your data than textual formats can support. -As one of our customers said of graph visualization, "I can look at a graph of transactions and fraudulent activity pops out for me." - -Neo4j offers methods for visualizing data, such as Neo4j Browser for developers, Neo4j Bloom for analysts and others looking for natural language search, and libraries for developers to embed graphs directly into their applications. -We also have several partners providing additional options in this space to fit a variety of visualization needs. - -link:/developer/guide-data-visualization/[Learn More: Graph Visualization^, role="button feature-box_button"] - -=== Data Import - -If you are looking to bring data into Neo4j from another source, Neo4j has a variety of tools and partner applications for different approaches to the data import problem. -We want to maximize value by simplifying the import process and allow you to start analyzing your graph data sooner. - -Our goal in this area to help developers transfer some of their own relevant data into Neo4j to see how it would look and what a graph database can help them do with it. -We also have options for other types of data import, including integrations with other vendor products and tools. -This area aids businesses to transport data from technology to technology as easily and smoothly as possible. - -link:/developer/guide-import-csv/[Learn More: Data Import^, role="button feature-box_button"] - -=== Drivers for Popular Programming Languages - -Developing applications using Neo4j is straightforward. -Using the *binary "Bolt" protocol*, we provide officially-supported drivers for languages such as *.Net, Java (also Spring), JavaScript, and Python*. -Other drivers for the binary and the http protocol are developed by our active contributor community. - -These drivers allow developers to build applications and integrations using the programming language(s) of their choice and easily interact with data in the Neo4j database. - -link:/developer/language-guides/[Learn More: Language Drivers^, role="button feature-box_button"] - -=== Extensions and Integrations - -Neo4j is supported by a rich ecosystem of libraries, tools, drivers, and guides provided by Neo4j, partners, users, and community contributors. -We want to allow users the ability to integrate with familiar technologies and minimize pain associated with a rip-and-replace approach to solutions. - -This area continues to expand as the technology ecosystem and developer needs consistently grow. -Before writing any new code, we recommend checking out GitHub and other resources to see if something has already been developed to solve your need. - -link:/developer/integration/[Learn More: Extensions and Integrations^, role="button feature-box_button"] - -=== Deploying Neo4j - -If you are looking to run Neo4j in production environments, we provide various options for types of deployments and configurations. -Neo4j provides options for causal clustering, performance tuning, cloud deployment, backups and failovers, and more. -We also have tools to plan and manage your deployments and preferences, including a calculator to help you know how much hardware you will need to run your data in Neo4j. - -From local to cloud deployments and anything in between, we can help satisfy your preferences and requirements. -While each implementation will have its own advantages and difficulties, the power to choose resides in the business and technical needs of your company. - -link:/developer/in-production/[Learn More: Deploying Neo4j^, role="button feature-box_button"] - -[#reference-manuals] -== Reference Documentation - -The tutorials and guides in this site are meant to help you get up and running quickly while showing you how Neo4j integrates with a variety of programming languages and technologies. -Our core reference documentation will help you as you write your queries and code. - -link:/docs/[Neo4j Reference Docs^, role="button feature-box_button"] - -[#training-certification] -== Training and Certification: Neo4j GraphAcademy - -Neo4j GraphAcademy offers training classes online and in classrooms around the world. -From the fundamentals to advanced Cypher, these classes target audiences at any skill level. - -For those interested in adding Neo4j qualifications to their resume or demonstrating knowledge in Neo4j, we also offer a developer certification exam. -All of the study materials and resources are accessible from our website, but external resources can help you study, too. -The test is free, and you will receive a certificate and t-shirt after passing the test to become a Neo4j Certified Professional! -Visit our GraphAcademy pages to learn more and to take the exam. - -link:/graphacademy/[Neo4j GraphAcademy^, role="button feature-box_button"] - -[#contribute-neo4j] -== Contributing in Neo4j - -No matter your experience level, we would love to have your feedback and input on all things Neo4j! -If you are looking to get involved, check out the different ways you can join our community and help improve our ecosystem and the open source community. - -link:/developer/contribute/[Learn More: Contributing in Neo4j^, role="button feature-box_button"] diff --git a/modules/ROOT/pages/get-started.adoc b/modules/ROOT/pages/get-started.adoc deleted file mode 100644 index 80f382f3..00000000 --- a/modules/ROOT/pages/get-started.adoc +++ /dev/null @@ -1,170 +0,0 @@ -= Getting Started with Neo4j -:author: Jennifer Reif -:category: documentation -:tags: get-started, introduction, graph, database, graph-platform, training -:description: Become an expert Neo4j developer with these introductory tutorials and guidelines that cover the entire development lifecycle, from download to deployment. -:page-pagination: next -:page-newsletter: true -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -[#starting-neo4j] - -++++ -
    - -
    -++++ - -These developer guides are written to help you through your initial learning path with Neo4j. - -[#first-steps] -== Your First Steps - -1. https://neo4j.com/cloud/aura/?ref=developer-guides[Create an AuraDB Free Instance] -2. link:https://graphacademy.neo4j.com[Learn to Create and Query Data with Cypher^] -3. link:/developer/guide-importing-data-and-etl/[Import Your Data^] -4. link:/developer/language-guides/[Build an Application^] and host it on link:/cloud/aura[Neo4j AuraDB Free^] -5. Get Help on our https://community.neo4j.com[Neo4j Online Community^] - -[#guide-overview] -== Developer Guide Overview - -We cover a variety of topics - from understanding graph database concepts to building applications that interact with Neo4j to running Neo4j in production. - -++++ - -++++ - -Videos, text, examples, and code are just some of the formats in which we deliver the information to encourage you and aid all learning styles. - -[#graph-db] -== What is a Graph Database? - -Connected information is everywhere in the world around us. -Neo4j was built to efficiently store, handle, and query highly-connected data in your data model. -With a powerful and flexible data model, you can represent your real-world, variably-structured information without a loss of richness. - -// REMOVE old out of date video with old branding -// ++++ -//
    -// -//
    -// ++++ - -link:/developer/graph-database/[Learn More: What is a Graph Database?^, role="button feature-box_button"] - -[#graph-platform] -== Neo4j Graph Platform - -At Neo4j, we want to provide options to solve many different kinds of business and technical needs. -Our goal is that our products are simple and fit your use case, whatever it may be. -Whether you are relying upon graphs for transactions, market analysis, operations optimizations, or anything else, Neo4j strives to provide a seamless process for integrating our tools with the rest of your existing system. - -Capabilities in the Neo4j graph platform include aiding developers to import data to the graph, business analysts to explore the data with ease, and data scientists to make decisions based on analysis results. -No matter your role within your organization, we want to put the power of the graph and Neo4j within reach to help you maximize business value and achieve technical needs. - -link:/developer/graph-platform/[Learn More: Neo4j Graph Platform?^, role="button feature-box_button"] - -=== Graph Data Modeling - -The richness of graph data and the performance of your queries depends closely on how the data is modeled. -Data in Neo4j should resemble the real-world information and relationships, but data modeling focuses on understanding what questions the data will answer and what types of information you need from it. - -Your data model may also impact the queries you write and the performance of the results returned. -Choosing to store certain pieces of data as properties versus separate nodes and determining how to structure your relationships is part of the process to maintain good data and maximize query performance. - -link:/developer/data-modeling/[Learn More: Graph Data Modeling^, role="button feature-box_button"] - -=== Cypher - A Next-Generation Query Language - -We wanted to make querying graph data not only fun, but easy to learn, understand, and use for everyone. -That's why we created a human-friendly, declarative query language which uses ASCII-Art to represent visual graph patterns for finding or updating data in Neo4j. - -Cypher was based on the power of SQL, but optimized specifically for graphs. -The syntax is concise and straightforward, allowing users to easily write all the normal CRUD operations in a simple and maintainable way. - -link:/developer/cypher/[Learn More: Cypher Query Language^, role="button feature-box_button"] - -=== Graph Visualization - -Visualizations often provide a better understanding of your data than textual formats can support. -As one of our customers said of graph visualization, "I can look at a graph of transactions and fraudulent activity pops out for me." - -Neo4j offers methods for visualizing data, such as Neo4j Browser for developers, Neo4j Bloom for analysts and others looking for natural language search, and libraries for developers to embed graphs directly into their applications. -We also have several partners providing additional options in this space to fit a variety of visualization needs. - -link:/developer/graph-visualization/[Learn More: Graph Visualization^, role="button feature-box_button"] - -=== Data Import - -If you are looking to bring data into Neo4j from another source, Neo4j has a variety of tools and partner applications for different approaches to the data import problem. -We want to maximize value by simplifying the import process and allow you to start analyzing your graph data sooner. - -Our goal in this area to help developers transfer some of their own relevant data into Neo4j to see how it would look and what a graph database can help them do with it. -We also have options for other types of data import, including integrations with other vendor products and tools. -This area aids businesses to transport data from technology to technology as easily and smoothly as possible. - -link:/developer/guide-import-csv/[Learn More: Data Import^, role="button feature-box_button"] - -=== Drivers for Popular Programming Languages - -Developing applications using Neo4j is straightforward. -Using the *binary "Bolt" protocol*, we provide officially-supported drivers for languages such as *.Net, Java (also Spring), JavaScript, and Python*. -Other drivers for the binary and the http protocol are developed by our active contributor community. - -These drivers allow developers to build applications and integrations using the programming language(s) of their choice and easily interact with data in the Neo4j database. - -link:/developer/language-guides/[Learn More: Language Drivers^, role="button feature-box_button"] - -=== Extensions and Integrations - -Neo4j is supported by a rich ecosystem of libraries, tools, drivers, and guides provided by Neo4j, partners, users, and community contributors. -We want to allow users the ability to integrate with familiar technologies and minimize pain associated with a rip-and-replace approach to solutions. - -This area continues to expand as the technology ecosystem and developer needs consistently grow. -Before writing any new code, we recommend checking out GitHub and other resources to see if something has already been developed to solve your need. - -link:/developer/integration/[Learn More: Extensions and Integrations^, role="button feature-box_button"] - -=== Deploying Neo4j - -If you are looking to run Neo4j in production environments, we provide various options for types of deployments and configurations. -Neo4j provides options for causal clustering, performance tuning, cloud deployment, backups and failovers, and more. -We also have tools to plan and manage your deployments and preferences, including a calculator to help you know how much hardware you will need to run your data in Neo4j. - -From local to cloud deployments and anything in between, we can help satisfy your preferences and requirements. -While each implementation will have its own advantages and difficulties, the power to choose resides in the business and technical needs of your company. - -link:/developer/in-production/[Learn More: Deploying Neo4j^, role="button feature-box_button"] - -[#reference-manuals] -== Reference Documentation - -The tutorials and guides in this site are meant to help you get up and running quickly while showing you how Neo4j integrates with a variety of programming languages and technologies. -Our core reference documentation will help you as you write your queries and code. - -link:/docs/[Neo4j Reference Docs^, role="button feature-box_button"] - -[#training-certification] -== Training and Certification: Neo4j GraphAcademy - -Neo4j GraphAcademy offers training classes online and in classrooms around the world. -From the fundamentals to advanced Cypher, these classes target audiences at any skill level. - -For those interested in adding Neo4j qualifications to their resume or demonstrating knowledge in Neo4j, we also offer a developer certification exam. -All of the study materials and resources are accessible from our website, but external resources can help you study, too. -The test is free, and you will receive a certificate and t-shirt after passing the test to become a Neo4j Certified Professional! -Visit our GraphAcademy pages to learn more and to take the exam. - -link:/developer/about-graphacademy/[Neo4j GraphAcademy^, role="button feature-box_button"] - -[#contribute-neo4j] -== Contributing in Neo4j - -No matter your experience level, we would love to have your feedback and input on all things Neo4j! -If you are looking to get involved, check out the different ways you can join our community and help improve our ecosystem and the open source community. - -link:/developer/contribute/[Learn More: Contributing in Neo4j^, role="button feature-box_button"] diff --git a/modules/ROOT/pages/getting-started-resources.adoc b/modules/ROOT/pages/getting-started-resources.adoc deleted file mode 100644 index 03ed6890..00000000 --- a/modules/ROOT/pages/getting-started-resources.adoc +++ /dev/null @@ -1,85 +0,0 @@ -= Getting Started Resources -:author: Jennifer Reif -:category: documentation -:tags: get-started, introduction, resources, neo4j-help, cypher, graph, nosql-graph, neo4j-training -:page-pagination: -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/appendix/getting-started-resources/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -[#getting-started-resources] -To help you along your path of learning more about Neo4j, we want to provide you with the resources we used throughout this section, as well as a few additional resources for further information and development. - -[#graphdb-resources] -== Graph Database Concepts -* https://www.youtube.com/watch?v=5Tl8WcaqZoc&list=PL9Hl4pk2FsvWM9GWaguRhlCQ-pa-ERd4U[Video Series: Intro to Graph Databases^] -* https://neo4j.com/graph-databases-book/[Free eBook: O'Reilly Graph Databases^] -* https://dzone.com/articles/graph-databases-for-beginners-native-vs-non-native[DZone: Graph Databases for Beginners^] - -[#rdbms-graph-resources] -== Graph for Relational Developers -* https://neo4j.com/resources/rdbms-developer-graph-white-paper/[Free eBook: Relational to Graph^] -* https://dzone.com/refcardz/from-relational-to-graph-a-developers-guide[DZone Refcard: From Relational to Graph^] -* https://neo4j.com/developer/data-modeling/[Relational to Graph Data Modeling^] -* https://medium.com/neo4j/tap-into-hidden-connections-translating-your-relational-data-to-graph-d3a2591d4026[Neo4j ETL Tool^] - -[#cypher-resources] -== Cypher Query Language -* https://neo4j.com/developer/cypher/[Cypher Query Language^] -* https://neo4j.com/docs/cypher-refcard/current/[Cypher Refcard^] -* https://neo4j.com/developer/guide-sql-to-cypher/[From SQL to Cypher^] - -[#nosql-graph-resources] -== Graph for NoSQL Developers -* https://dzone.com/articles/nosql-database-types-1[DZone: NoSQL Database Types^] - -[#sandbox-resources] -== Sandbox Use-Case Guides -* link:/sandbox/?ref=developer-start-resources[Start a Sandbox Online^] - -[#training-resources] -== Training, Courses, and Webinars - -*Free*, self-paced courses at https://graphacademy.neo4j.com/[GraphAcademy^]. - -Here are our latest online, self-paced training courses: - -*Beginner Learning Path:* - -* https://graphacademy.neo4j.com/courses/neo4j-fundamentals/[Neo4j Fundamentals^] -* https://graphacademy.neo4j.com/courses/cypher-fundamentals/[Cypher Fundamentals^] -* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^] -* https://graphacademy.neo4j.com/courses/importing-data/[Importing CSV Data into Neo4j^] - -*Cypher Learning Path:* - -* https://graphacademy.neo4j.com/courses/cypher-intermediate-queries/[Cypher Intermediate Queries^] - -*Developer Learning Path:* - -* https://graphacademy.neo4j.com/courses/app-nodejs/[Building Neo4j Applications with Node.js^] -* https://graphacademy.neo4j.com/courses/app-python/[Building Neo4j Applications with Python^] -* https://graphacademy.neo4j.com/courses/app-java/[Building Neo4j Applications with Java^] -* https://graphacademy.neo4j.com/courses/app-dotnet/[Building Neo4j Applications with .NET^] -* https://graphacademy.neo4j.com/courses/app-go/[Building Neo4j Applications with Go^] - -*Data Scientist Learning Path:* - -* https://graphacademy.neo4j.com/courses/gds-product-introduction/[Introduction to Neo4j Graph Data Science^] -* https://graphacademy.neo4j.com/courses/graph-data-science-fundamentals/[Neo4j Graph Data Science Fundamentals^] - -Classroom and virtual training classes: - -* https://neo4j.com/events/world/training/[Find Other Online and Classroom Training Sessions^] - -[#neo4j-events] -== Neo4j Events -* https://neo4j.com/events/world/all/[Neo4j Events] - -[#other-neo4j-resources] -== Other Resources -* http://www.opencypher.org/[openCypher project^] -* https://neo4j.com/customers/[Neo4j Customers^] -* xref:graph-database.adoc[Neo4j Features^] -* http://neo4j.com/editions/[Neo4j Editions^] diff --git a/modules/ROOT/pages/go.adoc b/modules/ROOT/pages/go.adoc deleted file mode 100644 index e53af98c..00000000 --- a/modules/ROOT/pages/go.adoc +++ /dev/null @@ -1,49 +0,0 @@ -= Using Neo4j from Go -:level: Intermediate -:page-level: Intermediate -:author: Neo4j -:programming-language: golang -:category: drivers -:tags: golang, go, app-development, applications -:description: If you are a Go developer, this guide provides an overview of options for connecting to Neo4j. While this guide is not comprehensive it will introduce the different drivers and link to the relevant resources. -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/neo4j-go/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -include::./_includes/common-language-prereq.adoc[] - -[role=expertise {level}] -{level} - -[#neo4j-go] -== Neo4j for Go Developers - -image::{img}GO.jpg[width=150,float=right] - -Neo4j provides drivers which allow you to make a connection to the database and develop applications -which create, read, update, and delete information from the graph. - -[#go-driver] -== Neo4j Go Driver - -include::./_includes/go-driver.adoc[] - -include::./_includes/sample_project.adoc[] - -include::./_includes/community-driver-notice.adoc[] - -[#golang-bolt] -=== GoGM: Golang Object Graph Mapper - -[cols="1,4"] -|=== -| icon:user[] Author | https://github.com/erictg[Eric Solender^], CTO and co-founder of https://mindstand.com/about-us/[Mindstand^] -// | icon:gift[] Package | -| icon:github[] Source | https://github.com/mindstand/gogm -| icon:book[] Docs | https://github.com/mindstand/gogm/blob/master/README.md -|=== diff --git a/modules/ROOT/pages/graph-database.adoc b/modules/ROOT/pages/graph-database.adoc deleted file mode 100644 index 38e3af7f..00000000 --- a/modules/ROOT/pages/graph-database.adoc +++ /dev/null @@ -1,146 +0,0 @@ -= What is a Graph Database? -:level: Beginner -:page-level: Beginner -:author: Jennifer Reif -:category: documentation -:tags: get-started, introduction, graph, database, property-graph, use-cases, cypher -:page-pagination: next -:page-newsletter: true -:page-ad-overline-link: https://neo4j.com/cloud/aura/?ref=developer-guides -:page-ad-overline: Neo4j Aura -:page-ad-title: Experience Graph Databases -:page-ad-description: Free forever, no credit card required. -:page-ad-link: https://neo4j.com/cloud/aura/?ref=developer-guides -:page-ad-underline-role: button -:page-ad-underline: Start on AuraDB -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/get-started-with-neo4j/graph-database/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -[role=expertise {level}] -{level} - -[#what-is-graphdb] -A graph database stores nodes and relationships instead of tables, or documents. -Data is stored just like you might sketch ideas on a whiteboard. -Your data is stored without restricting it to a pre-defined model, allowing a very flexible way of thinking about and using it. - -++++ -
    - -
    -++++ - -[#why-graphdb] -== Why Graph Databases? - -We live in a connected world, and understanding most domains requires processing rich sets of connections to understand what's really happening. -Often, we find that the connections between items are as important as the items themselves. - -image:{img}graph-example.png[role="popup-link"] - -How else do people do this today? While existing relational databases can store these relationships, they navigate them with expensive `JOIN` operations or cross-lookups, often tied to a rigid schema. -It turns out that "relational" databases handle relationships poorly. -In a graph database, there are no JOINs or lookups. -Relationships are stored natively alongside the data elements (the nodes) in a much more flexible format. -Everything about the system is optimized for traversing through data quickly; millions of connections per second, per core. - -// TODO: not ready yet but this point here is where we would introduce the "graph problem problem" of the key technical challenges graphs solve, and link to the main developer page when it's ready. -Graph databases address big challenges many of us tackle daily. -Modern data problems often involve many-to-many relationships with heterogeneous data that sets up needs to: - -- Navigate deep hierarchies, -- Find hidden connections between distant items, and -- Discover inter-relationships between items. - -Whether it's a social network, payment networks, or road network you'll find that everything is an interconnected graph of relationships. -And when we want to ask questions about the real world, many questions are _about the relationships_ rather than about the individual data elements. - -[#property-graph] -== The Property Graph Model - -In Neo4j, information is organized as nodes, relationships, and properties. - -.Building blocks of the property graph model -image:{img}sample-cypher.png[role="popup-link"] - -*_Nodes_* are the entities in the graph. - -- Nodes can be tagged with _labels_, representing their different roles in your domain. (For example, `Person`). -- Nodes can hold any number of key-value pairs, or _properties_. (For example, `name`) -- Node labels may also attach metadata (such as index or constraint information) to certain nodes. - -*_Relationships_* provide directed, named, connections between two node entities (e.g. Person `LOVES` Person). - -- Relationships always have a direction, a type, a start node, and an end node, and they can have properties, just like nodes. -- Nodes can have any number or type of relationships without sacrificing performance. -- Although relationships are always _directed_, they can be navigated efficiently in any direction. - -If you'd like to learn more about any of these, you can read more about link:/developer/data-modeling/[Graph Data Modeling]. - -[#neo4j-overview] -== What is Neo4j? - -Neo4j is an open-source, NoSQL, native graph database that provides an ACID-compliant transactional backend for your applications that has been publicly available since 2007. - -Neo4j is offered as a managed service via link:{aura_signup}[AuraDB]. -But you can also run Neo4j yourself with either Community Edition or Enterprise Edition. -The Enterprise Edition includes all that Community Edition has to offer, plus extra enterprise requirements such as backups, clustering, and failover abilities. Neo4j is written in Java and Scala, and the source code is available on https://github.com/neo4j/neo4j[GitHub^]. - -image:{img}colored-tile-graph.png[role="popup-link"] - -Neo4j is a _native graph database_, which means that it implements a true graph model all the way down to the storage level. -The data isn't stored as a "graph abstraction" on top of another technology, it's stored just as you whiteboard it. -This is important because it's the reason why Neo4j outperforms other graphs and stays so flexible. -Beyond the core graph, Neo4j provides what you'd expect out of a database; ACID transactions, cluster support, and runtime failover. This stability and maturity is why it's been used in production scenarios for large enterprise workloads for years. - -What makes Neo4j the easiest graph to work with? - -- *Cypher*, a declarative query language similar to SQL, but optimized for graphs. -Now used by other databases like SAP HANA Graph and Redis graph via the http://www.opencypher.org/[openCypher project^]. -- *Constant time traversals* in big graphs for both depth and breadth due to efficient representation of nodes and relationships. -Enables scale-up to billions of nodes on moderate hardware. -- *Flexible* property graph schema that can adapt over time, making it possible to materialize and add new relationships later to shortcut and speed up the domain data when the business needs change. -- link:/developer/language-guides[*Drivers*] for popular programming languages, including Java, JavaScript, .NET, Python, and many more. - -// OLD IMAGE contains old branding and needs replacement, and is only a visual recap of the bulleted list above. -// image::{img}neo4j_overview.jpg[role="popup-link"] - -[#neo4j-uses] -== Where and How is Neo4j Used? - -Neo4j is used today by thousands of https://neo4j.com/customers/[startups, educational institutions, and large enterprises^] in all sectors including financial services, government, energy, technology, retail, and manufacturing. -From innovative new technology to driving businesses, users are generating insights with graph, generating new revenue, and improving their overall efficiency. - -image::{img}use-case-summary.png[role="popup-link"] - -Learn more about different https://neo4j.com/use-cases/[use cases] and companies using them in the video below. - -// OLD RYAN VIDEO -- preserved here for context, but old branding, lower production values, older UI, etc. -// ++++ -//
    -// -//
    -// ++++ - -[#graphdb-resources] -== Resources -* https://graphacademy.neo4j.com/courses/neo4j-fundamentals/[Neo4j Fundamentals Course on GraphAcademy] -* https://neo4j.com/graph-databases-book/[Free eBook: O'Reilly Graph Databases^] -* https://www.youtube.com/watch?v=5Tl8WcaqZoc&list=PL9Hl4pk2FsvWM9GWaguRhlCQ-pa-ERd4U[Video Series: Intro to Graph Databases^] - - -[.discrete.ad] -== link:https://graphacademy.neo4j.com/?ref=guides[Learn with GraphAcademy^] - -image::https://graphacademy.neo4j.com/courses/neo4j-fundamentals/badge/[float=left] - -[.discrete] -=== link:https://graphacademy.neo4j.com/courses/neo4j-fundamentals/?ref=guides[Neo4j Fundamentals^] - -In this free course, we take you on a journey from 1736 Prussia for a brief history of graph theory, discuss the types of graphs you may see in the wild, and walk through an example dataset. - -This course is designed for both beginners and non-technical audiences who are interested in learning more about Graphs and Neo4j. - -link:https://graphacademy.neo4j.com/courses/neo4j-fundamentals/?ref=guides[Learn more^,role=button] diff --git a/modules/ROOT/pages/graph-db-vs-nosql.adoc b/modules/ROOT/pages/graph-db-vs-nosql.adoc deleted file mode 100644 index ded8a8f1..00000000 --- a/modules/ROOT/pages/graph-db-vs-nosql.adoc +++ /dev/null @@ -1,103 +0,0 @@ -= Concepts: NoSQL to Graph -:level: Beginner -:page-level: Beginner -:author: Jennifer Reif -:category: documentation -:tags: get-started, introduction, nosql, graph, key-value, document, compare, concepts -:description: This guide explains how graph databases are related to other NoSQL databases and how they differ. -:page-pagination: -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/appendix/graphdb-concepts/graphdb-vs-nosql/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -You need no prior knowledge of other NoSQL databases, although it is helpful to have read the guide on link:/developer/graph-database[graph databases] and understand basic data modeling questions and concepts. - -[role=expertise {level}] -{level} - -[#nosql-vs-graph] -Although unhelpfully named, the NoSQL ("Not only SQL") space brings together many interesting solutions offering different data models and database systems, each more suitable than traditional SQL solutions for certain use cases and shapes of data. - -With the advent of the NoSQL movement, the "one-size-fits-all" proposition of large relational systems was replaced by conscious decisions about finding the right tool for the job. - -++++ -
    - -
    -++++ - -Most NoSQL systems are *aggregate-oriented*, grouping the data based on a particular criterion and the database type (such as document store, key-value pair, etc). -This model provides only simple, limited operations and only forms one dedicated view of your data. -Focusing on one aggregate at a time allows users to easily spread many chunks of data across a network of machines along the aggregate dimension (for instance, the *Document* in document databases), but that means that other projections and perspectives have to be computed by crunching or duplicating your data. - - -[quote, "Graph Databases, O'Reilly"] -Most NoSQL databases store sets of disconnected aggregates. This makes it difficult to use them for connected data and graphs. One well-known strategy for adding relationships to such stores is to embed an aggregate's identifier inside the field belonging to another aggregate — effectively introducing foreign keys. But, this requires joining aggregates at the application level, which quickly becomes prohibitively expensive. - -Other NoSQL databases lack relationships. -Graph databases, on the other hand, handle fine-grained networks of information, providing *any perspective* on your data that fits your use case. -The well-known and trusted transactional guarantees from relational systems also protect updates of the graph data in Neo4j, conforming to ACID standards. - -Let's compare the graph data model to other NoSQL models. - -[#nosql-to-graph] -== Translating NoSQL Knowledge to Graphs - -With the advent of the NoSQL movement, businesses of all sizes have a variety of modern options from which to build solutions relevant to their use cases. - -* Calculating average income? Ask a *relational database*. -* Building a shopping cart? Use a *key-value Store*. -* Storing structured product information? Store as a *document*. -* Describing how a user got from point A to point B? Follow a *graph*. - -The chart below shows how each database type stacks up on a spectrum measuring depth and size. -While key-value stores can handle massive sizes, they are designed for a high-level view (low depth) of the data. -Graph databases retain minimum sizing, even at a greater depth of data than other types of databases. -The other types of databases fall somewhere in between those ranges. - -image::{img}database_compare.jpg[500,500,role="popup-link"] - -[#keyvalue-graph-model] -== Key-Value vs. Graph: Data Model Differences - -The *key-value* model is great and highly performant for lookups of huge amounts of simple or even complex values. -The image below shows how a typical key-value store is structured. - -.Key-Value Model (click to zoom) -image:{img}key_value_model.jpg[500,500,role="popup-link"] - -However, when the values are themselves interconnected, you have a graph. -Neo4j lets you traverse quickly among all the connected values and find insights in the relationships. -The graph version below shows how each key is related to a single value and how different values can be related to one another (like nodes connected to one another through relationships). - -.Key-Value as Graph (click to zoom) -image:{img}key_value_as_graph.jpg[500,500,role="popup-link"] - -[#document-graph-model] -== Document vs. Graph: Data Model Differences - -The structured hierarchy of a *Document* model accommodates a lot of schema-free data that can easily be represented as a tree. -Although trees are a type of graph, a tree represents only one projection or perspective of your data. -The image below demonstrates how a document store hierarchy is structured as pieces within larger components. - -.Document Model (click to zoom) -image:{img}document_model.jpg[500,500,role="popup-link"] - -If you refer to other documents (or contained elements) within that tree, you have a more expressive representation of the same data that you can easily navigate using a graph. -A graph data model lets more than one natural representation emerge dynamically as needed. -The graph version below demonstrates how moving this data to a graph structure allows you to view different levels and details of the tree in different combinations. - -.Document as Graph (click to zoom) -image:{img}document_as_graph.jpg[500,500,role="popup-link"] - -[#nosql-graph-resources] -== Resources -* https://dzone.com/articles/nosql-database-types-1[DZone: NoSQL Database Types^] -* https://neo4j.com/blog/aggregate-stores-tour/?ref=blog[Blog post: Tour of Aggregate Stores^] diff --git a/modules/ROOT/pages/graph-db-vs-rdbms.adoc b/modules/ROOT/pages/graph-db-vs-rdbms.adoc deleted file mode 100644 index efb1b86d..00000000 --- a/modules/ROOT/pages/graph-db-vs-rdbms.adoc +++ /dev/null @@ -1,180 +0,0 @@ -= Transition from Relational to Graph Database - Neo4j -:level: Beginner -:page-level: Beginner -:author: Jennifer Reif -:category: documentation -:tags: get-started, introduction, relational, graph, compare, concepts -:description: This guide explores the concepts of graph databases from a relational developer's point of view. -:page-pagination: next -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/appendix/graphdb-concepts/graphdb-vs-rdbms/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} -It aims to explain the conceptual differences between relational and graph database structures and data models. -It also gives a high-level overview of how working with each database type is similar or different - from the relational and graph query languages to interacting with the database from applications. - -.Prerequisites -[abstract] -You should be familiar with the relational database model and understand the basics of the link:/developer/graph-database#property-graph[property-graph model]. -It is also helpful to understand basic data modeling questions and concepts. - -[role=expertise {level}] -{level} - -[#relational-vs-graph] -Relational databases have been the work horse of software applications since the 80's, and continue as such to this day. -They store highly-structured data in tables with predetermined columns of specific types and many rows of those defined types of information. -Due to the rigidity of their organization, relational databases require developers and applications to strictly structure the data used in their applications. - -In relational databases, references to other rows and tables are indicated by referring to primary key attributes via foreign key columns. -Joins are computed at query time by matching primary and foreign keys of all rows in the connected tables. -These operations are compute-heavy and memory-intensive and have an exponential cost. - -When many-to-many relationships occur in the model, you must introduce a _JOIN_ table (or associative entity table) that holds foreign keys of both the participating tables, further increasing join operation costs. -The image below shows this concept of connecting a Person (from Person table) to a Department (in Department table) by creating a Person-Department join table that contains the ID of the person in one column and the ID of the associated department in the next column. - -As you can probably see, this makes understanding the connections very cumbersome because you must know the person ID and department ID values (performing additional lookups to find them) in order to know which person connects to which departments. -Those types of costly join operations are often addressed by denormalizing the data to reduce the number of joins necessary, therefore breaking the data integrity of a relational database. - -.Relational Model (click to zoom) -image:{img}relational_model.jpg[role="popup-link"] - -Although not every use case is a good fit for this stringent data model, the lack of viable alternatives and the wide support for relational databases made it difficult for alternative models to break into the mainstream. -However, the NoSQL era arrived in the market, filling some needs for users and businesses, but still missing the importance of the connections between data. -This is how graph databases were born. -They were designed to provide the greatest advantage in the connected world we live in today. - -[#relational-to-graph] -== Translating Relational Knowledge to Graphs - -Unlike other database management systems, relationships are of equal importance in the graph data model to the data itself. -This means we are not required to infer connections between entities using special properties such as foreign keys or out-of-band processing like map-reduce. - -By assembling nodes and relationships into connected structures, graph databases enable us to build simple and sophisticated models that map closely to our problem domain. -The data stays remarkably similiar to its form in the real world - small, normalized, yet richly connected entities. -This allows you to query and view your data from any imaginable point of interest, supporting many different use cases. - -Each node (entity or attribute) in the graph database model directly and physically contains a list of relationship records that represent the relationships to other nodes. -These relationship records are organized by type and direction and may hold additional attributes. -Whenever you run the equivalent of a _JOIN_ operation, the graph database uses this list, directly accessing the connected nodes and eliminating the need for expensive search-and-match computations. - -This ability to pre-materialize relationships into the database structure allows Neo4j to provide performance of several orders of magnitude above others, especially for join-heavy queries, allowing users to leverage a _minutes to milliseconds_ advantage. - -++++ -
    - -
    -++++ - -[#rdbms-graph-model] -== Data Model Differences - -As you can probably imagine from the structural differences discussed above, the data models for relational versus graph are very different. -The straightforward graph structure results in much simpler and more expressive data models than those produced using traditional relational or other NoSQL databases. - -If you are used to modeling with relational databases, remember the ease and beauty of a well-designed, normalized entity-relationship diagram - a simple, easy-to-understand model you can quickly whiteboard with your colleagues and domain experts. -A graph is exactly that - a clear model of the domain, focused on the use cases you want to efficiently support. - -Let's compare the two data models to show how the structure differs between relational and graph. - -.Relational - Person and Department tables (click to zoom) -image:{img}relational_as_graph.jpg[role="popup-link"] - -In the above relational example, we search the Person table on the left (potentially millions of rows) to find the user Alice and her person ID of 815. Then, we search the Person-Department table (orange middle table) to locate all the rows that reference Alice's person ID (815). Once we retrieve the 3 relevant rows, we go to the Department table on the right to search for the actual values of the department IDs (111, 119, 181). -Now we know that Alice is part of the 4Future, P0815, and A42 departments. - -.Graph - Alice and 3 Departments as nodes (click to zoom) -image:{img}relational_graph_model.jpg[role="popup-link"] - -In the above graph version, we have a single node for Alice with a label of Person. -Alice belongs to 3 different departments, so we create a node for each one and with a label of Department. -To find out which departments Alice belongs to, we would search the graph for Alice's node, then traverse all of the BELONGS_TO relationships from Alice to find the Department nodes she is connected to. -That's all we need - a single hop with no lookups involved. - -[TIP] -==== -More information on this topic can be found in the https://neo4j.com/developer/data-modeling/[Data Modeling section^]. -==== - - -[#rdbms-graph-query] -== Data Storage and Retrieval - -Querying relational databases is easy with SQL - a declarative query language that allows both easy ad-hoc querying in a database tool, as well as use-case-specific querying from application code. -Even object-relational mappers (ORMs) use SQL under the hood to talk to the database. - -Do graph databases have something similar? -Yes! - -Cypher, Neo4j's declarative graph query language, is built on the basic concepts and clauses of SQL but has a lot of additional graph-specific functionality to make it easy to work with your graph model. - -If you have ever tried to write a SQL statement with a large number of joins, you know that you quickly lose sight of what the query actually does because of all the technical noise in SQL syntax. -In Cypher, the syntax remains concise and focused on domain components and the connections among them, expressing the pattern to find or create data more visually and clearly. -Other clauses outside of the basic pattern matching look very similar to SQL, as Cypher was built on the predecessor language's foundations. - -We will cover Cypher query language syntax in an upcoming guide, but let us look at a brief example of how a SQL query differs from a Cypher query. -In the organizational domain from our data modeling example above, what would a SQL statement that *lists the employees in the IT Department* look like, and how does it compare to the Cypher statement? - -.SQL Statement -[source,sql] ----- -SELECT name FROM Person -LEFT JOIN Person_Department - ON Person.Id = Person_Department.PersonId -LEFT JOIN Department - ON Department.Id = Person_Department.DepartmentId -WHERE Department.name = "IT Department" ----- - -.Cypher Statement -[source,cypher] ----- -MATCH (p:Person)-[:WORKS_AT]->(d:Dept) -WHERE d.name = "IT Department" -RETURN p.name ----- - -[TIP] -==== -You can find more about Cypher syntax in the upcoming guides for the https://neo4j.com/developer/cypher/[Cypher Query Language^] and transitioning https://neo4j.com/developer/guide-sql-to-cypher/[from SQL to Cypher^]. -==== - -[#rdbms-graph-practice] -== Transitioning from Relational to Graph - In Practice - -If you do decide to move your data from a relational to a graph database, the steps to transition your applications to use Neo4j are actually quite simple. -You can connect to Neo4j with a driver or connector library designed for your stack or programing language, just as you can with other databases. -Thanks to Neo4j and its community, there are Neo4j drivers that mimic existing database driver idioms and approaches for nearly any popular programing language. - -For instance, the Neo4j JDBC driver would be used like this to query the database for _John's departments_: - -[source, clike] ----- -Connection con = DriverManager.getConnection("jdbc:neo4j://localhost:7474/"); - -String query = - "MATCH (:Person {name:{1}})-[:EMPLOYEE]-(d:Department) RETURN d.name as dept"; -try (PreparedStatement stmt = con.prepareStatement(QUERY)) { - stmt.setString(1,"John"); - ResultSet rs = stmt.executeQuery(); - while(rs.next()) { - String department = rs.getString("dept"); - .... - } -} ----- - -[TIP] -==== -For more information, you can visit our pages for https://neo4j.com/developer/language-guides/[Building Applications^] to see how to connect to Neo4j using different programming languages. -==== - -[#rdbms-graph-resources] -== Resources -* https://neo4j.com/resources/rdbms-developer-graph-white-paper/[Free eBook: Relational to Graph^] -* https://dzone.com/refcardz/from-relational-to-graph-a-developers-guide[DZone Refcard: From Relational to Graph^] -* https://neo4j.com/developer/data-modeling/[Data Modeling: Relational to Graph] diff --git a/modules/ROOT/pages/graph-model-refactoring.adoc b/modules/ROOT/pages/graph-model-refactoring.adoc deleted file mode 100644 index 47786815..00000000 --- a/modules/ROOT/pages/graph-model-refactoring.adoc +++ /dev/null @@ -1,339 +0,0 @@ -= Graph Model Refactoring -:level: Intermediate -:page-level: Intermediate -:author: Mark Needham -:category: modeling -:tags: graph-modeling, data-model, schema, refactoring, apoc -:description: Building on the Cypher Basics guides, this guide provides a worked example of changing a graph model. Upon finishing this guide, you should be able to evolve your graph model based on changing requirements. -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-modeling/graph-model-refactoring/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -You should be familiar with xref:graph-database.adoc[graph database] concepts and the xref:graph-database.adoc#property-graph[property graph model]. -This guide is a continuation of the concepts discussed in the previous Cypher sections. -You should be familiar with link:/developer/cypher/[MATCH], xref:cypher:updating.adoc[Create/Update/Delete], and xref:cypher:filtering-query-results.adoc[Filtering] concepts before walking through this guide. - -[role=expertise {level}] -{level} - -[#airports-dataset] -== Airports dataset - -In this guide we're going to use an airports dataset that contains connections between US airports in January 2008. -We have the data in a CSV file, and this is the graph model that we're going to import it into: - -image::{img}initial_model.png[role="popup-link"] - -Before we import any data, we're going to create a unique constraint on the `Airport` label and `code` property to ensure that we don't accidentally import duplicate airports. -The following query creates this constraint: - -[source,cypher] ----- -CREATE CONSTRAINT ON (airport:Airport) -ASSERT airport.code IS UNIQUE ----- - -.Results -|=== -| 0 rows available after 86 ms, consumed after another 0 ms. Added 1 constraints -|=== - -And the following query loads the data from a CSV file using the `LOAD CSV` tool: - -[source,cypher] ----- -LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j-contrib/training/master/modeling/data/flights_1k.csv" AS row -MERGE (origin:Airport {code: row.Origin}) -MERGE (destination:Airport {code: row.Dest}) -MERGE (origin)-[connection:CONNECTED_TO { - airline: row.UniqueCarrier, - flightNumber: row.FlightNum, - date: date({year: toInteger(row.Year), month: toInteger(row.Month), day: toInteger(row.DayofMonth)}), - cancelled: row.Cancelled, - diverted: row.Diverted}]->(destination) -ON CREATE SET connection.departure = localtime(apoc.text.lpad(row.CRSDepTime, 4, "0")), - connection.arrival = localtime(apoc.text.lpad(row.CRSArrTime, 4, "0")) ----- - -This query: - -* Creates a node with an `Airport` label with a `code` property that has a value from the `Origin` column in the CSV file -* Creates a node with an `Airport` label with a `code` property that has a value from the `Dest` column in the CSV file -* Creates a relationship of type `CONNECTED_TO` with several properties based on columns in the CSV file. - -If we run this query we'll see the following output: - -.Results -|=== -| Added 62 labels, created 62 nodes, set 7062 properties, created 1000 relationships, completed after 376 ms. -|=== - -This model is a good starter one, but there are some improvements that we can make. - -[#property-to-boolean] -== Convert property to boolean - -The `diverted` and `cancelled` properties on the `CONNECTED_TO` relationships contain string values of `1` and `0`. -Since these values are representing booleans, we can use the https://neo4j.com/docs/labs/apoc/current/graph-updates/graph-refactoring/normalize-boolean/[`apoc.refactor.normalizeAsBoolean`^] procedure to convert the values from strings to booleans. - -image::{img}boolean_refactoring.png[role="popup-link"] - -The following query does the conversion for the `diverted` property: - -[source,cypher] ----- -MATCH (:Airport)-[connectedTo:CONNECTED_TO]->(:Airport) -CALL apoc.refactor.normalizeAsBoolean(connectedTo, "diverted", ["1"], ["0"]) -RETURN count(*) ----- - -.Results -[opts="header"] -|=== -| count(*) -| 1000 -|=== - -And the following query does the conversion for the `cancelled` property: - - -[source,cypher] ----- -MATCH (origin:Airport)-[connectedTo:CONNECTED_TO]->(departure) -CALL apoc.refactor.normalizeAsBoolean(connectedTo, "cancelled", ["1"], ["0"]) -RETURN count(*) ----- - -.Results -[opts="header"] -|=== -| count(*) -| 1000 -|=== - -If we have a lot of relationships to update, we may get an OutOfMemory exception if we try to refactor them all in one transaction. -We can therefore process them in batches using the https://neo4j.com/docs/labs/apoc/current/graph-updates/periodic-execution/#commit-batching[`apoc.periodic.iterate`^] procedure. -The following query does this for the `cancelled` and `reverted` properties in the same query: - -[source,cypher] ----- -UNWIND ["cancelled", "reverted"] AS propertyToDelete -CALL apoc.periodic.iterate( - "MATCH (:Airport)-[connectedTo:CONNECTED_TO]->(:Airport) RETURN connectedTo", - "CALL apoc.refactor.normalizeAsBoolean(connectedTo, $propertyToDelete, ['1'], ['0']) - RETURN count(*)", - {params: {propertyToDelete: propertyToDelete}, batchSize: 100}) -YIELD batches -RETURN propertyToDelete, batches ----- - -The `apoc.periodic.iterate` procedure in this query takes in 3 parameters: - -* An outer Cypher query that finds and returns a stream of `CONNECTED_TO` relationships to be processed. -* An inner Cypher query that processes those `CONNECTED_TO` relationships, converting to boolean any values for the specified property on those relationships. It does this using the `apoc.refactor.normalizeAsBoolean` procedure, which itself takes in several parameters: - ** the entity on which the property exists - ** the name of the property to normalize - ** a list of values that should be considered `true` - ** a list of values that should be considered `false` -* Configuration values for the procedure, including: - ** `params` - parameters passed into those Cypher queries - ** `batchSize`- controls the number of inner statements that are run within a single transaction - -When we run this query we'll see the following output: - -.Results -[opts="header"] -|=== -| propertyToDelete | batches -| "cancelled" | 10 -| "reverted" | 10 -|=== - -Once we've done this, we can write the following query to return all cancelled connections: - -[source,cypher] ----- -MATCH (origin:Airport)-[connectedTo:CONNECTED_TO]->(destination) -WHERE connectedTo.cancelled -RETURN origin.code AS origin, - destination.code AS destination, - connectedTo.date AS date, - connectedTo.departure AS departure, - connectedTo.arrival AS arrival ----- - -.Results -[opts="header"] -|=== -| origin | destination | date | departure | arrival -| "LAS" | "OAK" | 2008-01-03 | 07:00 | 08:30 -| "LAX" | "SFO" | 2008-01-03 | 09:05 | 10:25 -| "LAX" | "OAK" | 2008-01-03 | 11:00 | 12:15 -| "LAX" | "SJC" | 2008-01-03 | 19:30 | 20:35 -| "LAX" | "SFO" | 2008-01-03 | 16:20 | 17:40 -| "MDW" | "STL" | 2008-01-03 | 11:10 | 12:15 -| "MDW" | "BDL" | 2008-01-03 | 08:45 | 11:40 -| "MDW" | "DTW" | 2008-01-03 | 06:00 | 08:05 -| "MDW" | "STL" | 2008-01-03 | 14:45 | 15:50 -| "MDW" | "BNA" | 2008-01-03 | 19:25 | 20:45 -| "OAK" | "BUR" | 2008-01-03 | 13:10 | 14:15 -| "OAK" | "BUR" | 2008-01-03 | 17:05 | 18:10 -|=== - -[#create-node-from-relationship] -== Create node from relationship - -Next, imagine that we want to write a query that finds a specific flight. -This is quite difficult with our existing model because flights are represented as relationships. -We can evolve our model to create a `Flight` node from the properties stored on the `CONNECTED_TO` relationship. - -image::{img}flight_node.png[role="popup-link"] - -The following query does this refactoring: - -[source,cypher] ----- -CALL apoc.periodic.iterate( - "MATCH (origin:Airport)-[connected:CONNECTED_TO]->(destination:Airport) RETURN origin, connected, destination", - "CREATE (flight:Flight { - date: connected.date, - airline: connected.airline, - number: connected.flightNumber, - departure: connected.departure, - arrival: connected.arrival, - cancelled: connected.cancelled, - diverted: connected.diverted - }) - MERGE (origin)<-[:ORIGIN]-(flight) - MERGE (flight)-[:DESTINATION]->(destination) - DELETE connected", - {batchSize: 100}) ----- - -As with our previous query, this query uses the `apoc.periodic.iterate` procedure so that we can do the refactoring in batches rather than within a single transaction. -The procedure takes in 3 parameters: - -* An outer Cypher query that finds and returns a stream of `CONNECTED_TO` relationships, and origin and destination airports that need to be processed. -* An inner Cypher query that processes those entities, creating a node with the label `Flight` and creating relationships from that node to the origin and destination airports. -* `batchSize` configuration, which sets to `100` the number of inner statements that are run within a single transaction. - -If we execute the query we'll see the following output: - -.Results -[opts="header"] -|=== -| batches | total | timeTaken | committedOperations | failedOperations | failedBatches | retries | errorMessages | batch | operations | wasTerminated -| 10 | 1000 | 0 | 1000 | 0 | 0 | 0 | {} | {total: 10, committed: 10, failed: 0, errors: {}} | {total: 1000, committed: 1000, failed: 0, errors: {}} | FALSE -|=== - - -We can also do this refactoring using the https://neo4j.com/docs/labs/apoc/current/graph-updates/graph-refactoring/extract-node-from-relationship/[`apoc.refactor.extractNode`^] procedure. - -[source,cypher] ----- -CALL apoc.periodic.iterate( - "MATCH (origin:Airport)-[connected:CONNECTED_TO]->(destination:Airport) - RETURN origin, connected, destination", - "CALL apoc.refactor.extractNode([connected], ['Flight'], 'DESTINATION', 'ORIGIN') - YIELD input, output, error - RETURN input, output, error", - {batchSize: 100}); ----- - -This does the same as the previous query, but the outer Cypher query uses the `apoc.refactor.extractNode` procedure to create the `Flight` node and create relationships to origin and destination airports. -If we run this query we'll see the following output: - -.Results -[opts="header"] -|=== -| batches | total | timeTaken | committedOperations | failedOperations | failedBatches | retries | errorMessages | batch | operations | wasTerminated -| 10 | 1000 | 0 | 1000 | 0 | 0 | 0 | {} | {total: 10, committed: 10, failed: 0, errors: {}} | {total: 1000, committed: 1000, failed: 0, errors: {}} | FALSE -|=== - - -[#create-node-from-property] -== Create node from property - -At the moment the airline for our flights is stored in the `airline` property on `Flight` nodes. -This means that if we wanted to return a stream of all airlines we'd need to scan through every flight and check the `airline` property on each of those flights. - -We can make it easier, and more efficient, to write this query by creating a node with an `Airline` label for each airline: - -image::{img}airline.png[role="popup-link"] - -Let's first create a constraint on the `Airline` label and `name` property so that we don't create duplicate airline nodes: - -[source,cypher] ----- -CREATE CONSTRAINT ON (airline:Airline) -ASSERT airline.name IS UNIQUE ----- - -.Results -|=== -| 0 rows available after 107 ms, consumed after another 0 ms. Added 1 constraints -|=== - -And now we can execute the following query to do the refactoring: - -[source,cypher] ----- -CALL apoc.periodic.iterate( - 'MATCH (flight:Flight) RETURN flight', - 'MERGE (airline:Airline {name:flight.airline}) - MERGE (flight)-[:AIRLINE]->(airline) - REMOVE flight.airline', - {batchSize:10000, iterateList:true, parallel:false} -) ----- - -Again we're using the `apoc.periodic.iterate` procedure, with the following parameters: - -* An outer Cypher statement that returns a stream of `Flight` nodes to be processed -* An inner Cypher statementthat processes these flight nodes, creating `Airline` nodes based on flights' `airline` property and created an `AIRLINE` relationship from the `Flight` to the `Airline` node. We then remove the `airline` property from the `Flight` node. - -If we run this query we'll see the following output: - -.Results -[opts="header"] -|=== -| batches | total | timeTaken | committedOperations | failedOperations | failedBatches | retries | errorMessages | batch | operations | wasTerminated -| 1 | 1000 | 0 | 1000 | 0 | 0 | 0 | {} | {total: 1, committed: 1, failed: 0, errors: {}} | {total: 1000, committed: 1000, failed: 0, errors: {}} | FALSE -|=== - -We can then write the following query to find the airlines and number of flights involving each: - -[source,cypher] ----- -MATCH (airline:Airline)<-[:AIRLINE]-(:Flight) -RETURN airline.name AS airline, count(*) AS numberOfFlights ----- - -This does the same as the previous query, but the outer Cypher query uses the `apoc.refactor.extractNode` procedure to create the `Flight` node and create relationships to origin and destination airports. -If we run this query we'll see the following output: - -.Results -[opts="header"] -|=== -| airline | numberOfFlights -| "WN" | 1000 -|=== - - - -[#cypher-resources] -=== Resources - -This guide has shown how to refactor a graph model, with help from procedures in the APOC Library. -Below are some resources for learning more about refactoring in Neo4j: - -* link:https://neo4j.com/developer/neo4j-apoc/[APOC Library^] -* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals course^] -* https://graphacademy.neo4j.com/courses/importing-data/[Importing CSV Data into Neo4j course^] which also contains some refactoring examples. \ No newline at end of file diff --git a/modules/ROOT/pages/graph-platform.adoc b/modules/ROOT/pages/graph-platform.adoc deleted file mode 100644 index 54c7d2d4..00000000 --- a/modules/ROOT/pages/graph-platform.adoc +++ /dev/null @@ -1,47 +0,0 @@ -= Neo4j Graph Platform -:author: Jennifer Reif -:category: documentation -:tags: graph-platform, graph, database, desktop, browser, bloom, labs, integrations -:page-pagination: -:page-newsletter: true -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/#_the_neo4j_graph_data_platform - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -[#neo4j-platform] -There are a variety of ways to interact with and use graph data in Neo4j. -This guide introduces the different products we provide at Neo4j to suit your diverse business and technology needs and shows you how to get started using them. - -From our core product (the graph database) to visualization for business users, we provide a variety of opportunities to maximize your business and simplify your data. - -[#platform-components] -== Components of the Neo4j Graph Platform - -image::{img}neo4j_graph_platform.jpg[role="popup-link"] - -Each of the elements listed below was designed to fill a business or technical need in the image above. -We continue to improve the different perspectives from which to view data, as well as capabilities of the products themselves. - -Detailed information and walkthroughs are covered in more detail in subsequent pages of this section, but if you have any questions or issues, don't hesitate to reach out through the https://community.neo4j.com[Neo4j Online Community^]! - -* link:/developer/neo4j-database/[Neo4j Graph Database] - our core graph database that is built to store and retrieve connected data. There are link:/licensing/[two editions^] - a Community Edition and an Enterprise Edition. Everything in our platform interacts with data stored in the database. -* link:/developer/neo4j-desktop/[Neo4j Desktop] - application to manage local instances of Neo4j. Free download includes Neo4j Enterprise Edition license. -* link:/developer/neo4j-browser/[Neo4j Browser] - online browser interface to query and view the data in the database. Basic visualization capabilities using Cypher query language. -* link:/developer/neo4j-bloom/[Neo4j Bloom^] - visualization tool for business users that does not require any code or programming skills to view and analyze data. link:/docs/bloom-user-guide/current/[Documentation^] is also available in our docs section. -* link:/developer/aura-cloud-dbaas/[Neo4j AuraDB] - database-as-a-service offering managed by Neo4j for graph databases in the cloud. Find out more general information on the link:/aura/[product page^]. -* link:/developer/graph-algorithms/[Graph Data Science^] - officially-supported library for executing graph algorithms with Neo4j and optimized for enterprise workloads and pipelines. link:/docs/graph-data-science/current/[Documentation^] for the library is also available. - -== Neo4j Integration Tools - -Neo4j also has a variety of extension libraries and developer tools that can be added to existing products to enhance functionality. -Some of these projects have been adopted by our link:/labs/[Neo4j Labs^] team to help developers integrate with other innovative tools and libraries. - -Tutorials and other specific information for some of these is included in this section. -Other non-Neo4j Labs extensions will be covered in the link:/developer/integration/[Extensions & Integrations] section of these guides. - -Neo4j Labs integrations: - -link:/developer/neo4j-apoc/[APOC] - the standard utility library of procedures and functions for Neo4j. + -link:/developer/graphql/[GraphQL and GRANDstack] - integrate with other popular data technologies or full-stack solutions. + -link:/developer/neo4j-etl/[ETL Tool] - migrate data from a relational database to Neo4j using an application and simple user interface. diff --git a/modules/ROOT/pages/graph-visualization.adoc b/modules/ROOT/pages/graph-visualization.adoc deleted file mode 100644 index 7b7ff3fb..00000000 --- a/modules/ROOT/pages/graph-visualization.adoc +++ /dev/null @@ -1,217 +0,0 @@ -= Graph Visualization -:level: Beginner -:author: Jennifer Reif -:category: visualization -:tags: visualization, graph, tools, browser, bloom, introduction, tools, charts, maps, heatmaps, 3d -:description: This guide explains graph visualization tool options, and how to get insights from your data using visualization tools. -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/graph-visualization/graph-visualization/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -[role=expertise {level}] -{level} - -[#about-graph-vis] -Neo4j is designed to be very visual in nature. -Native graph databases like Neo4j focus on relationships. Visualizing these relationships can give a unique -"big picture" to your data that is difficult or impossible to get with traditional tables and business intelligence packages. - -image:{img}bloom-movies-view-1.png[role="popup-link"] - -Graph visualization takes these capabilities one step further by drawing the graph in various formats so users can interact with the data in a more user-friendly way. -With visualization tools, a full or partial graph can come to life and allow the user to explore it, setting various rules or views in order to analyze it from different perspectives. - -image:{img}bloom-movies-view-2.png[role="popup-link"] - -This section is designed to help you understand how to export your graph data in Neo4j for display as a visualization, and list options so you can choose what suits your needs. - -[#why-vis-graph] -== Why Visualize a Graph? - -You have many options for working with data; JSON, XML, tabular, and others. -Why represent it visually? - -Anyone reviewing a graph can see the connections, determine areas of interest, or quickly assess the current state and organization of the data. -As you can imagine, this can provide insight where other types of data formats cannot, bringing enormous value. -Visualizations help make anomalies or relevant patterns stand out to help human eyes and brains detect them, where other types of data formats might not highlight hidden structures as well. - -.Graph -image:{img}vis_movies_graph_format.jpg[role="popup-link"] - -Let us look at a very rudimentary example of this using our movie data from the earlier data modeling section guides. -In the graph view above, we can easily pick out that `Lana Wachowski` directed both `Cloud Atlas` and `The Matrix` movies, where in the tabular representation, that information is not as clear or easy-to-find. - -.Table -image:{img}vis_movies_tabular_format.jpg[role="popup-link"] - -Even if you feel that the relationship is not hard to find in the tabular format, imagine if we were looking at a graph that contained these individuals' entire filmography careers, as well as hundreds of other actors, directors, and film crew members. -The connections could easily be lost in a non-visual presentation. - -[#neo4j-vis-tools] -== Neo4j Visualization Tools and Products - -Neo4j has two main visualization tools that are built and designed to work specifically with data in Neo4j’s graph database: link:/developer/neo4j-browser/[Neo4j Browser] and https://neo4j.com/bloom/[Neo4j Bloom^]. -We will briefly discuss the key details of each here. - -=== Neo4j Bloom - -Bloom is a standalone product focused on visualization that comes with every link:{aura_signup}[AuraDB Instance], and is available with a Neo4j Desktop. -It was designed for business analysts annd nother non-developers to interact with graph data without writing any code. - -image:{img}bloom_vis_yelp.jpg[role="popup-link"] - -Users can use natural language to query the database and explore patterns, clusters, and traversals in their graph data. -It is also possible to create different dissections of the graph (called perspectives) that allow users to view different aspects and slices of graph data for further analysis. - -=== Neo4j Browser - -Neo4j Browser is an interactive cypher command shell for developers that allows you to interact with your graph and visualize the information in it. Neo4j Browser is bundled with Neo4j and is available in all editions and versions of Neo4j. - -image:{img}neo4j-browser-oneshot.png[role="popup-link"] - -Its visualization functionality is designed to display a node-graph representation of the underlying data stored in the database in response to a given Cypher query, showing circles for nodes and lines for relationships. -Neo4j Browser also provides some functionality for styling with color and size based on node labels and relationship types, or you can customize your own styles by importing a GRASS (graph-stylesheet) file for Neo4j Browser to reference. -You can also use the built-in drop-down buttons on query result panes to easily export the data to link:/developer/neo4j-browser#browser-tips[PNG, SVG, or CSV formats]. - -[#other-vis] -== Alternative Visualizations of Graph Data - -Not all graph visualizations represent data in circles and lines for nodes and relationships. -Users may want to view data in various chart-based, map-based, or 3D formats. - -[#graph-vis-chart] -=== Chart-based visualizations - -Viewing data in familiar chart formats such as bar charts, histograms, pie charts, dials, meters and other representations might be preferred for various users and business needs. -There are tools that support these types of charts for metrics and dashboarding. - -There are several open source tools available, but we will mention a few with links that we have used before. -Feel free to explore others! - -==== Tableau - -image:{img}tableau.png[,width=200] - -image::{img}tableau-neo4j.jpg[role="popup-link",float="right",width=350] - -Tableau is a data analysis tool that can take data from a variety of sources and blend or split the data based on user specification. -Using the link:https://neo4j.com/bi-connector/[Neo4j Connector for BI] you can make a connection between -Neo4j and Tableau as you would any other SQL databases, and visualize data directly. - -Once the data is in Tableau, the user can interact with a drag-and-drop Tableau GUI to aggregate, splice, and style various combinations of the data into colorized visualizations in countless formats. - -==== *amCharts* - -Blog post: https://medium.com/neo4j/showing-charts-for-neo4j-query-results-using-amcharts-and-structr-efae0b7a04f0[Charts for Neo4j query results with amCharts+Structr^] - -image::{img}amcharts_structr.jpg[role="popup-link"] - -==== *Chart.js* - -Blog post: https://neo4j.com/blog/charting-neo4j-3-0/[Charting Neo4j^] - -image::{img}chart_js_visualization.jpg[role="popup-link"] - -==== *Nivo* - -Blog post: https://medium.com/neo4j/working-with-neo4j-date-and-spatial-types-in-a-react-js-app-5475b5042b50[Neo4j Spatial with Nivo charts^] - -image::{img}nivo_charts_bar.jpg[role="popup-link"] -image::{img}nivo_charts_circle.jpg[role="popup-link"] - -[#graph-vis-map] -=== *Map-based visualizations* - -image::{img}mapbox_visualization.jpg[role="popup-link",float="right",width=350] - -Graph data is an excellent fit for mapping and representing geographic data, as it is laid out by entities and connections (locations/points and routes to get to those locations). -Neo4j can help plot latitude and longitude, polygon geometries, routes, as well as distances, so a tool to overlay a map visualization on the front-end of this data provides a great deal of value for interacting and exploring an area. - -Commercial tools by Tom Sawyer and Keylines both also support this type of visualization. - -.*Leaflet.js / Mapbox* -Leaflet.js is an open source library that allows us to create multiple layers and show/hide various layers. -It is designed to be interactive and function on mobile phones, as well as traditional devices. -You can extend functionality with a variety of plugins, including Mapbox. -With these tools, you can create a base map layer (such as map tiles) and data visualizations live in map layers that are plotted on top of the map tiles. -Mapbox also gives you the capability to add an interactive map. - -=== Leaflet.js Resources -* Leaflet.js website: https://leafletjs.com/[Leaflet.js^] -* Blog post: https://www.lyonwj.com/2017/11/28/geocoding-paradise-papers-neo4j-spatial-visualization/[Leaflet.js to visualize Paradise Papers data^] -* Blog post: https://medium.com/neo4j/working-with-neo4j-date-and-spatial-types-in-a-react-js-app-5475b5042b50[Using Leaflet.js and Mapbox to visualize spatial data in Neo4j^] -* Example source code: https://github.com/johnymontana/spacetime-reviews[Leaflet/Mapbox spatial Neo4j^] -* Example source code: https://github.com/johnymontana/osm-routing-app[Leaflet/Mapbox interactive map^] -* Video: https://neo4j.com/graphconnect-2018/session/neo4j-spatial-mapping[GraphConnect spatial Neo4j with Leaflet/Mapbox^] - -[#graph-vis-heatmap] -=== *Heatmap visualizations* - -image::{img}heatmap_visualization.jpg[role="popup-link",float="right",width=350] - -A heatmap is a data visualization where colors are used to represent data values. -It is often imposed on a map, but could also be on a matrix as well. -When heatmaps are used on a map, pockets of activity may be spread out, so some form of interpolation is often used. - -We will list the tool(s) we have encountered so far, but we will add to this as we interact with more. - -* *Leaflet.js plugins:* -** Blog post: https://www.lyonwj.com/2017/11/28/geocoding-paradise-papers-neo4j-spatial-visualization/[Leaflet.js heatcanvas plugin^] - -[#graph-vis-3d] -=== *3D visualizations* - -image::{img}graph_vis_3d.jpg[role="popup-link",float="right",width=350] - -Adding a third dimension may increase some complexity in the visualization, but also adds value. -Exploring your data in 3D can help navigate through large amounts of data better and more clearly. -Clustering should also be more apparent in a 3D visualization because data can be more spread out when using the third dimension, where 2D can cause groups to overlap or display more closely. - -Kineviz (commercial tool) also supports this type of visualization. - -.*3d-force-graph* -With this open source library, there are a couple of different components for handling the physics behind three dimensions and for actually rendering the visualization. -It uses an iterative approach for rendering in 3D and creates stunning, interactive visualizations. -The tool includes features for customizing styles of nodes and relationships, as well as container layouts, rendering controls, configuring simulation, and user interaction. -The data structure required is similar to previous tools we have seen, with collections for nodes and relationships. -3d-force-graph also offers functionality for visualizations to use with virtual reality. - -==== 3d-force-graph Resources -* Source code: https://github.com/vasturiano/3d-force-graph[3d-force-graph Github^] -* Author post: https://bl.ocks.org/vasturiano/02affe306ce445e423f992faeea13521[Example^] -* Blog post: https://medium.com/neo4j/visualizing-graphs-in-3d-with-webgl-9adaaff6fe43[Visualizing Graphs in 3D^] - -[#graph-vis-other] -==== *Other categories* - -There are still other tools for visualization that may not necessarily fit into the categories we have discussed so far. -Instead, they expand the current boundaries and find uniquely powerful ways to utilize graph technologies. -Thinking outside the box increases the possibilities of graph even further! - -.*Graphileon* -image:{img}graphileon-logo.png[width=200] - -image::{img}graphileon_visualization.jpg[role="popup-link",float="right",width=350] - -Graphileon is a platform for building graphy applications by composing functions and UI elements. -It can be harnessed by users such as consultants and designers for styling and dashboards. -Developers can also integrate with other technologies to customize applications, embed views, or extend functionality. - -[#vis-tools] -== Partner and Community Visualization Tools - -Outside of Neo4j's offerings, partners and community members have built tools and integrations to connect graph data in Neo4j with more graph visualizations. -Learn more about options and functionality of these tools in the guide linked below. - -link:/developer/tools-graph-visualization/[Graph Visualization Tools] - -[#graph-vis-resources] -== Resources -* https://neo4j.com/developer/neo4j-browser/[Neo4j Browser] -* https://neo4j.com/blog/neo4j-bloom-everywhere-this-spring/[Blog post: Neo4j Bloom^] -* https://medium.com/neo4j/hands-on-graph-data-visualization-bd1f055a492d[Blog post: Hands-on Graph Visualization^] diff --git a/modules/ROOT/pages/graphgist-portal.adoc b/modules/ROOT/pages/graphgist-portal.adoc deleted file mode 100644 index b83bad4f..00000000 --- a/modules/ROOT/pages/graphgist-portal.adoc +++ /dev/null @@ -1,61 +0,0 @@ -= What is the Neo4j GraphGist Project? -:level: Beginner -:page-level: Beginner -:graphgist: https://neo4j.com/graphgists/ -:graphgists_list: https://neo4j.com/graphgists/ -:graphgist_portal: http://portal.graphgist.org/ -:author: Neo4j -:category: modeling -:tags: graph-modeling, data-model, graphgist, example-graph -:description: This article introduces the GraphGist project and guides the reader through the process of creating and submitting a simple GraphGist. -:page-aliases: graphgist.adoc -:page-deprecated-title: the Neo4j GraphGists homepage -:page-deprecated-redirect: https://neo4j.com/graphgists/ - -// This page has been deprecated in favour of the Neo4j GraphGists homepage. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -You should have a good understanding of the Neo4j's xref:graph-database.adoc#property-graph[property graph data model]. It is also helpful to know a bit about the link:/developer/cypher[Cypher query language]. - -[role=expertise {level}] -{level} - -[#about-graphgists] -{graphgists_list}[Neo4j Graph Gists] are an easy way to create and share your documents containing not just prose, structure and pictures but most importantly **example graph models and use cases** expressed in Neo4j's query language, link:/developer/cypher[Cypher]. -They are very helpful to share knowledge among the Neo4j community and help developers and others within your own organization by showing real-life solutions. - -image::graphgists.jpg[role="popup-link"] - -These documents are written in a simple, textual markup language (http://asciidoctor.org[AsciiDoc^]) and are rendered in your browser as rich and interactive web pages that you can quickly evolve by describing simple how-tos or questions to providing an extensive use case specification. - -To see the expressive power of this approach, here are some winners of our past community competitions: - -[#graphgist-examples] -== Examples - -[none] -* **Finance** {graphgist}bank-fraud-detection[Bank Fraud Detection^] by https://twitter.com/kennybastani[@kennybastani^] -* **Sports** {graphgist}fis-alpine-skiing-seasons[Alpine Skiing seasons^] by https://twitter.com/pac_19[@pac_19^] -* **Life Science** {graphgist}pharmaceutical-drugs-and-their-targets[Pharmaceutical Drugs and their Targets^] by https://twitter.com/joshkunken[@joshkunken^] -// * **Resources** {graphgist}/?8141937[Piping Water]^ by https://twitter.com/shaundaley1[@shaundaley1^] -* **Retail** {graphgist}northwind-recommendation-engine[Northwind Recommendation Engine^] by https://twitter.com/adamcowley[@adamcowley^] -* **Telecommunications** {graphgist}amazon-web-services-global-infrastructure-graph[Amazon Web Services Global Infrastructure Graph^] by https://twitter.com/aidanjcasey[@aidanjcasey^] -* **Infrastructure** {graphgist}roads-nodes-and-automobiles[Roads, Nodes and Automobiles^] by http://www.jacqui.tk[@tekiegirl^] -* **Recommendation** {graphgist}movie-recommendations-with-k-nearest-neighbors-and-cosine-similarity[Movie Recommendations with k-NN and Cosine Similarity^] by http://twitter.com/_nicolemargaret[@_nicolemargaret^] -* **Fun** {graphgist}tic-tac-toe[Tic Tac Toe^] by https://twitter.com/sylvainroussy[@sylvainroussy^] -* **Many more** examples can be found in the link:{graphgists_list}[GraphGist Collection^] - -[#graphgist-info] -== Getting Started - -More information on getting started building your own GraphGist is available on the {graphgist_portal}[GraphGist Portal^]. -You can also explore the full range of GraphGists from xref:neo4j-desktop.adoc[Neo4j Desktop] using the *Graph Gallery* Graph App. -You can install the Graph App with a single click at https://install.graphapp.io[install.graphapp.io^] - -image::GraphGallery.jpg[role="popup-link"] - diff --git a/modules/ROOT/pages/graphql.adoc b/modules/ROOT/pages/graphql.adoc deleted file mode 100644 index 6c02e127..00000000 --- a/modules/ROOT/pages/graphql.adoc +++ /dev/null @@ -1,387 +0,0 @@ -= Neo4j and GraphQL -:doctype: book -:level: Beginner -:page-level: Beginner -:author: Will Lyon -:category: integrations -:tags: labs, graphql, queries, grandstack, react, apollo, javascript, java -:page-pagination: -:page-newsletter: true -:page-deprecated-title: the Neo4j GraphQL Library -:page-deprecated-redirect: https://neo4j.com/docs/graphql-manual/current/ - -// This page has been deprecated in favour of the Neo4j GraphQL Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -[#about-graphql] -image::https://upload.wikimedia.org/wikipedia/commons/thumb/1/17/GraphQL_Logo.svg/200px-GraphQL_Logo.svg.png[float=right,width=200] - -GraphQL is a specification for an API query language and a runtime for fulfilling these queries. GraphQL models application data as a graph and allows API clients to query the data as a graph irrespective of how the data is stored in the backend. While not specific to graph databases, using GraphQL with Neo4j offers several advantages including a consistent graph data model, increased developer productivity, and performance benefits. - -== GraphQL Overview - -GraphQL is fundamentally an API query language, as explained on the https://graphql.org/[GraphQL.org landing page^]: - -> GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. - -A GraphQL query includes an entry point (a Query or Mutation field in GraphQL terminology), (optionally) arguments, and a _selection set._ The selection set specifies a nested traversal through the data graph as well as which fields to be returned. - -Let's look at an example GraphQL query: - -.GraphQL example query -[source,GraphQL] ----- -{ - movies(where: { title: "Matrix, The" }) { - title - plot - year - actors { - name - } - } -} ----- - -The response to this query is a JSON document which matches the shape of the query - only the data requested by the client and included in the selection set is returned. - -.GraphQL example result -[source,JSON] ----- -{ - "data": { - "movies": [ - { - "title": "Matrix, The", - "plot": "A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.", - "year": 1999, - "actors": [ - { - "name": "Hugo Weaving" - }, - { - "name": "Laurence Fishburne" - }, - { - "name": "Keanu Reeves" - }, - { - "name": "Carrie-Anne Moss" - } - ] - } - ] - } -} ----- - - -The typical approach to building a GraphQL API involves creating GraphQL _type definitions_ which define the data available in the API and how they are connected, and writing GraphQL _resolver functions_ which contain the logic for "resolving" data from the data layer. These type definitions and resolver functions are then combined into an executable GraphQL schema which can be served over HTTP. - -GraphQL type definitions are typically defined using the GraphQL Schema Definition Language (SDL). SDL is a language-agnostic way to define GraphQL types. The GraphQL type definitions for the example queries above would look like this in SDL: - -.GraphQL example schema -[source,GraphQL] ----- -type Movie { - title: String! - year: Int - plot: String - actors: [Person] -} - -type Person { - name: String! - movies: [Movie] -} ----- - -The typical approach of writing GraphQL resolver functions manually can often involve lots of boilerplate code. Fortunately, we can often leverage tools like Neo4j's OGM and Spring Data integration when building GraphQL APIs as described in https://info.michael-simons.eu/2021/07/13/neo4j-java-and-graphql/[this blog post.^] - -For further enhanced developer productivity the https://neo4j.com/product/graphql-library/[Neo4j GraphQL Library^] can be used to build GraphQL APIs backed by Neo4j with minimal code. - -== The Neo4j GraphQL Library - -image::https://dist.neo4j.com/wp-content/uploads/20210423155831/graphql-diagram.svg[] - -The Neo4j GraphQL Library is a JavaScript library that can be used with any JavaScript GraphQL implementation, such as Apollo Server. By mapping GraphQL type definitions to the property graph model used by Neo4j, the Neo4j GraphQL Library can generate a CRUD API backed by Neo4j. This means developers don't even need to implement GraphQL resolver functions as the Neo4j GraphQL Library handles generating database queries from arbitrary GraphQL requests at query time. - -Features of the Neo4j GraphQL Library include: - -* Auto-generated GraphQL https://neo4j.com/docs/graphql-manual/current/queries/[queries^] and https://neo4j.com/docs/graphql-manual/current/mutations/[mutations^] based on https://neo4j.com/docs/graphql-manual/current/type-definitions/[GraphQL type definitions^] -* Complex https://neo4j.com/docs/graphql-manual/current/filtering/[filtering^], https://neo4j.com/docs/graphql-manual/current/sorting/[sorting^], and https://neo4j.com/docs/graphql-manual/current/pagination/[pagination^] -* Support for https://neo4j.com/docs/graphql-manual/current/custom-resolvers/[custom resolvers^] -* A powerful GraphQL schema directive driven https://neo4j.com/docs/graphql-manual/current/auth/[authorization model^] -* Support for https://neo4j.com/docs/graphql-manual/current/type-definitions/cypher/[custom logic using Cypher^] -* An https://neo4j.com/docs/graphql-manual/current/ogm/[Object Graph Mapper (OGM)^] based on GraphQL type definitions - -=== Using The Neo4j GraphQL Library With Neo4j AuraDB - -Let's see how we can create a simple GraphQL API using the Neo4j GraphQL Library and https://www.neo4j.com/cloud/aura?ref=developer-guides[Neo4j AuraDB^]. We'll be creating a Node.js GraphQL API application that will use the Neo4j GraphQL Library and Apollo Server to create a GraphQL API for movies data matching our example above. - -First, in a new directory we'll create a new Node.js project: - -.... -npm init -y -.... - -And install the necessary dependencies: - -.... -npm install @neo4j/graphql neo4j-driver graphql apollo-server dotenv -.... - -Next, we'll provision a Neo4j AuraDB instance. We'll use the free-tier of Neo4j AuraDB so we won't need to put in a credit card or incur any costs. Log in to https://dev.neo4j.com/aura[Neo4j AuraDB^] and select "Create Free DB". - -image::https://dist.neo4j.com/wp-content/uploads/20210826145910/neo4j-aura-free.png[] - -A random password for your Neo4j AuraDB instance will be generated, be sure to save this somewhere as we'll need to use it to access our Neo4j database. - -image::https://dist.neo4j.com/wp-content/uploads/20210826150418/neo4j-aura-password.png[] - -Provisioning our Neo4j AuraDB instance will take a few moments. Once it is ready in the Neo4j AuraDB dashboard we'll see the connection URI, which we'll need to connect our GraphQL API to Neo4j AuraDB. - -image::https://dist.neo4j.com/wp-content/uploads/20210826151045/neo4j-aura-dashboard.png[] - -Next, create a new file `.env` with the connection credentials for our Neo4j AuraDB instance. This will allow us to set our connection details in environment variables, without mixing these secrets in our code. Here's what the `.env` file would look like given the Neo4j AuraDB credentials above, but be sure to replace with your own connection credentials for your Neo4j AuraDB instance. - -.`.env` -[source] ----- -NEO4J_USER=neo4j -NEO4J_PASSWORD=a2y4FVUlfPdDPzU5EUeEq-arDdyokWStO1m7wlkY8u4 -NEO4J_URI=neo4j+s://80be87a6.databases.neo4j.io ----- - -Let's reuse our movie and actor GraphQL type definitions from above and use them with the Neo4j GraphQL Library. We'll also create an instance of the Neo4j JavaScript driver to connect to our Neo4j AuraDB instance, reading the credentials from our `.env` file as environment variables. Finally, we pass the GraphQL schema object generated by the Neo4j GraphQL Library to Apollo Server and start the GraphQL server. - -.`index.js` -[source,JavaScript] ----- -const { gql, ApolloServer } = require("apollo-server"); -const { Neo4jGraphQL } = require("@neo4j/graphql"); -const neo4j = require("neo4j-driver"); -require("dotenv").config(); - -const typeDefs = gql` - type Movie { - title: String! - year: Int - plot: String - actors: [Person!]! @relationship(type: "ACTED_IN", direction: IN) - } - - type Person { - name: String! - movies: [Movie!]! @relationship(type: "ACTED_IN", direction: OUT) - } -`; - -const driver = neo4j.driver( - process.env.NEO4J_URI, - neo4j.auth.basic(process.env.NEO4J_USER, process.env.NEO4J_PASSWORD) -); - -const neoSchema = new Neo4jGraphQL({ typeDefs, driver }); - -neoSchema.getSchema().then((schema) => { - const server = new ApolloServer({ - schema: schema - }); - - server.listen().then(({ url }) => { - console.log(`GraphQL server ready on ${url}`); - }); -}); ----- - -Notice that we added the https://neo4j.com/docs/graphql-manual/current/type-definitions/relationships/[`@relationship` GraphQL schema directive^] to our type definitions. This directive allows us to encode the relationship type and directive into our GraphQL type definitions. - -We can start our Node.js GraphQL server by running `node index.js` and then navigate to `http://localhost:4000` in a web browser to access Apollo Studio. - -=== Create a Movie with a Mutation - -Since we started a fresh Neo4j AuraDB database, let's start by creating some data. The Neo4j GraphQL Library generates the https://neo4j.com/docs/graphql-manual/current/mutations/[GraphQL Mutation type^] used for creating data. Here, we'll use the `createMovies` mutation to create a movie node and the actors connected to it in a single nested operation. - -.GraphQL Mutation to create a movie and actors -[source,GraphQL] ----- -mutation { - createMovies( - input: { - title: "Matrix, The" - plot: "A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers." - year: 1999 - actors: { - create: [ - { node: { name: "Hugo Weaving" } } - { node: { name: "Laurence Fishburne" } } - { node: { name: "Keanu Reeves" } } - { node: { name: "Carrie-Anne Moss" } } - ] - } - } - ) { - movies { - title - plot - year - actors { - name - } - } - } -} ----- - -The response to this GraphQL mutation will match the selection set and shows us the data created by the mutation operation. - - -[%collapsible] -.Click here to reveal the GraphQL response -==== - -.GraphQL response -[source,JSON] ----- -{ - "data": { - "createMovies": { - "movies": [ - { - "title": "Matrix, The", - "plot": "A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.", - "year": 1999, - "actors": [ - { - "name": "Hugo Weaving" - }, - { - "name": "Laurence Fishburne" - }, - { - "name": "Keanu Reeves" - }, - { - "name": "Carrie-Anne Moss" - } - ] - } - ] - } - } -} ----- - -==== - -If we check our database using Neo4j Browser we can see the data we've created in Neo4j: - -image::https://dist.neo4j.com/wp-content/uploads/20210831112825/matrixgraph.png[] - -=== Querying Data - -The Neo4j GraphQL Library automatically creates three options for querying a type, one for querying the raw data, one for counting the data and one for aggregating the data. -These auto-generated types also support advanced filtering, allowing you to control what child data is returned. - -The Library will recognise the `@relationship` directives and add an aggregator to allow you to quickly access the count of records. -For example, we can use the `actorsConnection` field to retrieve a count of the number of `actors` in a given movi. - - -.An advanced GraphQL query -[source,GraphQL] ----- -query { - movies( - where: { imdbRating_GT: 4} // <1> - options: {limit: 2, sort:[ {imdbRating: DESC} ]} // <2> - ) { - title - actors (where: { name_NOT: null }) { // <3> - name - born - } - actorsConnection { // <4> - totalCount - } - } -} - ----- - -<1> Filter for movies with an `imdbRating` greater than 4 -<2> Limit the number of results to 2 and sort by the `imdbRating` property in descending order -<3> Only return actors where the `name` property is not null -<4> Use the `actorsConnection` property to return a count - -[%collapsible] -.Click here to reveal the results -==== - -[source,json] ----- -{ - "data": { - "movies": [ - { - "title": "Band of Brothers", - "actors": [ - { - "name": "Scott Grimes", - "born": null - }, - { - "name": "Shane Taylor", - "born": null - }, - { - "name": "Ron Livingston", - "born": "1967-06-05" - }, - { - "name": "Damian Lewis", - "born": "1971-02-11" - } - ], - "actorsConnection": { - "totalCount": 4 - } - }, - { - "title": "Civil War, The", - "actors": [ - { - "name": "Sam Waterston", - "born": null - }, - { - "name": "Jason Robards", - "born": null - }, - { - "name": "Julie Harris", - "born": null - }, - { - "name": "Morgan Freeman", - "born": "1937-06-01" - } - ], - "actorsConnection": { - "totalCount": 4 - } - } - ] - } -} ----- -==== - - -You can learn more about the Neo4j GraphQL Library in the https://neo4j.com/docs/graphql-manual/current/[documentation.^] - -[#graphql-resources] -== Resources -* https://neo4j.com/product/graphql-library/[The Neo4j GraphQL Library overview page^] -* https://neo4j.com/docs/graphql-manual/current/[Neo4j GraphQL Library Documentation^] -* GraphAcademy Course: https://neo4j.com/graphacademy/training-graphql-apis/enrollment/[Building GraphQL APIs With The Neo4j GraphQL Library^] -* Book: https://www.manning.com/books/fullstack-graphql-applications[Full Stack GraphQL Applications With React, Node.js, and Neo4j]^ diff --git a/modules/ROOT/pages/guide-cloud-deployment.adoc b/modules/ROOT/pages/guide-cloud-deployment.adoc deleted file mode 100644 index d1039f94..00000000 --- a/modules/ROOT/pages/guide-cloud-deployment.adoc +++ /dev/null @@ -1,117 +0,0 @@ -= Hosting Neo4j in the Cloud -:author: Jennifer Reif -:category: cloud -:tags: cloud, hosting, aura, aws, google, azure, kubernetes, docker, paas -:page-deprecated-title: the Neo4j Operations Manual -:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/cloud-deployments/ - -// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -[#neo4j-cloud] -Do you want to deploy Neo4j to the cloud? -This section features guides and tutorials to help you understand the available options. - -Topics include: - -* Deploying with Neo4j AuraDB -* Deploying on Google Cloud Platform (GCP) -* Deploying on Amazon EC2 -* Deploying on Microsoft Azure -* Deploying with Docker, Kubernetes, and more (any cloud platform) -* Deploying on other PaaS offerings - -[#neo4j-aura] -== Neo4j AuraDB - -link:/cloud/aura/[Neo4j AuraDB^] is a database-as-a-service hosted by Neo4j that is designed to be flexible and highly reliable in providing graphs for any project. - -Whether dealing with critical business applications or 24/7 access needs, Neo4j AuraDB ensures it is always available. -This means developers can build applications and solutions with Neo4j anytime, anywhere without the maintenance concerns of other databases or implementations. - -For more information, check out the link:/cloud/aura/[AuraDB product page^] or get help on an existing instance with our link:/developer/aura-cloud-dbaas/[AuraDB guides]. -Helpful guides and support are available on the https://aura.support.neo4j.com/hc/en-us[AuraDB support^] pages. - -[#gcp-cloud] -== Google Cloud Platform (GCP) - -Google Cloud Platform, offered by Google, is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube. -Alongside a set of management tools, it provides a series of modular cloud services including computing, data storage, data analytics, and machine learning. - -Running on Google Cloud provides a few options, depending on what you want to do. - -* Neo4j AuraDB is available via the https://console.cloud.google.com/marketplace/product/endpoints/prod.n4gcp.neo4j.io[official GCP Marketplace^] that allows you to subscribe, provision and bill the instances to your GCP account - -* Single instance - our {opsmanual}/cloud-deployments/neo4j-gcp/single-instance-vm/[Google cloud image documentation^] gives you the steps to launch a single instance from an image with a few commands and interact with the Neo4j instance. - -* Causal Cluster - the Enterprise edition of Neo4j is also https://console.cloud.google.com/launcher/details/neo4j-public/neo4j-enterprise-causal-cluster[available on GCP Marketplace^], and users can launch a causal cluster from there. -The launch and interaction steps to deploy Neo4j causal clusters on GCP are shown in the {opsmanual}/cloud-deployments/neo4j-gcp/causal-cluster-vm/[Neo4j documentation^]. -We also have a link:/developer/neo4j-google-cloud-launcher/[step-by-step tutorial] for deploying a Neo4j cluster with GCP. - -* Google Kubernetes Marketplace (Docker container-based) - Neo4j Enterprise is also https://console.cloud.google.com/marketplace/details/neo4j-public/causal-cluster-k8s[available on Kubernetes Marketplace^], so users can launch Neo4j clusters into Google Kubernetes Engine (GKE) clusters. -For a walkthrough, please consult the blog post showing how to https://medium.com/google-cloud/launching-neo4j-on-googles-kubernetes-marketplace-97c23c94e960[launch Neo4j on Google Kubernetes Marketplace^]. -Technical artifacts and templates that support GKE can be found on the https://github.com/neo-technology/neo4j-google-k8s-marketplace[GitHub repository^]. - -The Neo4j documentation also contains a section on {opsmanual}/cloud-deployments/neo4j-gcp/automation-gcp/[automating your deployment^] using Google Cloud Deployment Manager. - -[#aws-cloud] -== Amazon EC2 - -Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. -It is designed to make web-scale cloud computing easier for developers. - -There are three options for running on EC2 detailed below, with each option depending on the needs of the user and environment. - -* Single instance (VM-based) - instructions for launching VMs with Amazon's command line tool are provided in the developer guide to xref:neo4j-cloud-aws-ec2-ami.adoc[deploy Neo4j on EC2 with a custom Amazon Machine Image (AMI)^]. -Using this method, both Community and Enterprise options are available. - -* Neo4j Community edition - https://aws.amazon.com/marketplace/pp/B071P26C9D[available from the AWS marketplace^]. - -[NOTE] -==== -For this installation, we recommend you select the option in security group settings to "Create new security group based on seller settings". -This will ensure that when the instance is launched, the default ports that are needed will be open. -==== - -* Causal Cluster - https://aws.amazon.com/marketplace/pp/B07D441G55[launch directly from the AWS Marketplace^], as well. -This option creates a multi-VM clustered configuration with the choice to configure a number of aspects of the cluster, including number of core nodes, read replicas, hardware sizing, encrypted EBS volumes, and other options. - -The Neo4j documentation also contains a section on {opsmanual}/cloud-deployments/neo4j-aws/automation-aws/[automating your deployment^] using CloudFormation. - -[#azure-cloud] -== Microsoft Azure - -Microsoft Azure is a cloud computing service created by Microsoft for building, deploying, and managing applications and services through a global network of Microsoft-managed data centers. -It provides software as a service, platform as a service and infrastructure as a service and supports many different programming languages, tools and frameworks, including both Microsoft-specific and third-party software and systems. - -Neo4j can be deployed directly from https://azuremarketplace.microsoft.com/en-us/marketplace/apps?search=neo4j&page=1[Azure Marketplace^]. - -* Single Instance - for a walkthrough of how to create single instances, please consult the documentation to {opsmanual}/cloud-deployments/neo4j-azure/single-instance-azure/[deploy Neo4j on Azure^]. - -* Causal Cluster - a walkthrough for creating clusters is in the documentation to {opsmanual}/cloud-deployments/neo4j-azure/causal-cluster-azure/[deploy a Neo4j causal cluster on Azure^]. - -The Neo4j documentation also contains a section on {opsmanual}/cloud-deployments/neo4j-azure/automation-azure/[automating your deployment^] using Azure Resource Manager. - -[#kube-docker] -== Kubernetes, Mesosphere, & Docker (on any cloud) - -For Docker users, Neo4j provides link:/developer/docker/[complete instructions] on using provided Docker containers. - -For users who wish to run Neo4j in kubernetes clusters, Neo4j provides a https://github.com/neo4j-contrib/neo4j-helm[helm chart^], which permits installation using standard `kubectl` and `helm` tools. -These kubernetes tools and approaches all take advantage of the standard Neo4j docker container mentioned above, but add clustering and coordination capabilities. -Additional information is provided in the link:/developer/guide-orchestration/[orchestration developer guide], and a walkthrough of how to use the helm chart to get started is available on link:/blog/kubernetes-deploy-neo4j-clusters/[Neo4j blog^]. - -For those interested in running Neo4j with Mesosphere DC/OS, the link:/developer/guide-orchestration/[orchestration developer guide] provides more details and resources. - -[#paas-integ] -== Other PaaS Offerings - -There are other hosting providers that can run Neo4j, as well. - -* https://www.digitalocean.com/community/tutorials/how-to-install-neo4j-on-an-ubuntu-vps[Digital Ocean^] -* https://github.com/jelastic-public-cartridges/openshift-origin-cartridge-neo4j-v21[Jelastic OpenShift Cartridge^] - -[#cloud-resources] -== Questions? - -You can ask questions and connect with other people launching Neo4j in the cloud through the https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^]. -Official documentation for launching Neo4j on each of the cloud provider platforms is in the link:{opsmanual}/cloud-deployments/[Neo4j Operations Manual^]. diff --git a/modules/ROOT/pages/guide-clustering-neo4j.adoc b/modules/ROOT/pages/guide-clustering-neo4j.adoc deleted file mode 100644 index 4c9808cd..00000000 --- a/modules/ROOT/pages/guide-clustering-neo4j.adoc +++ /dev/null @@ -1,102 +0,0 @@ -= How to set up a Neo4j Cluster -:level: Advanced -:page-level: Advanced -:author: Neo4j -:category: cluster -:tags: administration, causal-cluster, backup, ha, setup -:description: This guide describes how to configure a high availability cluster of Neo4j Enterprise. It includes topics such as data synchronization, mending procedures, configuring an arbiter, and slave-only mode configuration. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -You should know how to link:/download[download] and install Neo4j on your system. -If you are a developer, you should be accustomed to the graph data model and have written your Neo4j application. -A sound understanding of Neo4j's APIs, link:../guide-performance-tuning[tuning], security, and upgrade aspects will help you set up a stable cluster. - -[role=expertise {level}] -{level} - -[#neo4j-cluster] -== Data Synchronization between master and slaves - -The Neo4j High Availability Cluster is a fault tolerant, full master-slave replication setup. -Any data (graph or index) created on the master is propagated to the slaves depending on the settings of the master `ha.tx_push_factor` (number of slaves to actively push to during commit) and slaves `ha.pull_interval` (frequency of pulling updates from the master). - -[#cluster-config] -== Configuring a Two-Slave Cluster with an Arbiter - -There's a {opsmanual}/tutorial/highly-available-cluster/[step-by-step tutorial for setting up a HA cluster]. -For a two-node cluster, you'll actually want to run three separate machines, and use the Neo4j arbiter on the third. -Details on the arbiter are available in the {opsmanual}/clustering/high-availability/arbiter-instances/[Neo4j Operations Manual]. -The configuration for the arbiter is identical to that of a regular instance, with the exception of anything relating to the actual graph database itself (since an arbiter doesn't hold any data). - -The `ha.initial_hosts` property should be the same on all the instances, including the arbiter. -The arbiter should have a unique `ha.server_id` (`3` is fine). - -[#follow-server] -=== Setting a Server in Slave-Only Mode - -// what is the new link? -See the {opsmanual}/clustering/high-availability/configuration/[documentation] on `ha.slave_only`. - -You might want to configure a machine with that setting if it’s acting as a reporting instance but you need to make sure that two members don’t have that setting, or you won’t have any failover in the cluster. - -[#backup-cluster] -== Backing Up a Cluster Server - -Neo4j Server must be configured to run a backup service. -This is enabled via the configuration parameter `online_backup_enabled`, and is enabled by default. -The interface and port the backup service listens on is configured via the parameter `online_backup_server` and defaults to the `loopback` interface and port `6362`. -It is typical to reconfigure this to listen on an external interface, by setting `online_backup_server=:6362`. -It can also be configured to listen on all interfaces by setting `online_backup_server=0.0.0.0:6362`. - -It is best to use the back tool with the command line `./bin/neo4j-backup -host 192.168.1.34 -to /mnt/backup/neo4j-backup`, which will backup the store from that specific host even if that host is running in a cluster. - -// what is the new link? -Additional details can be found {opsmanual}/backup/#backup-introduction[in the manual]. - -image::{img}neo4j-logo.png[] - -[#mend-leader] -== Mending Procedure for Master - -* Shut down all members of the cluster and stop any load against the cluster -* On the master instance, copy the `data/graph.db` folder as a backup -* Modify neo4j-server.properties, setting the `org.neo4j.server.database.mode` to STANDALONE` -* Start the database and, once it's running, stop it again (this is to ensure he database is cleanly shut down) -* Remove all files named `nioneo_logical.log.vXX` in `data/graph.db`, being careful not to remove any files without the "`v`", like `nioneo_logical.log.1` or `nioneo_logical.log.active` -* Start the server and use either neo4j-shell or the neo4j browser to issue the following two queries: - -. First query. Note the id returned here - -[source, cypher] ----- -CREATE (n) RETURN id(n) ----- - -. Second query. Use the id returned from the previous query - -[source, cypher] ----- -MATCH (n) WHERE id(n) = DELETE n ----- - -* Stop the server -* Set org.neo4j.server.database.mode to HA -* Start the server - -[#follower-mend] -== Mending Procedure for Slaves - -* On each slave, remove the `data/graph.db` folder -* Start the slave, and watch `data/graph.db` for it to copy the store from the master -* Once copying is done, move to do the same thing on the second slave - -[#cluster-resources] -== Resources -* {opsmanual}/clustering/high-availability/[Neo4j High Availability,role=docs] -* {opsmanual}/tutorial/highly-available-cluster/[Tutorial Cluster Setup,role=docs] -* link:http://jimwebber.org/2011/03/strategies-for-scaling-neo4j/[Strategies for Scaling Neo4j,role=blog^] diff --git a/modules/ROOT/pages/guide-create-neo4j-browser-guide.adoc b/modules/ROOT/pages/guide-create-neo4j-browser-guide.adoc deleted file mode 100644 index 52b865ef..00000000 --- a/modules/ROOT/pages/guide-create-neo4j-browser-guide.adoc +++ /dev/null @@ -1,431 +0,0 @@ -= Tutorial: Create a Custom Browser Guide -:level: Intermediate -:page-level: Intermediate -:play: https://localhost:7474/browser?cmd=play&arg= -:author: Neo4j -:category: documentation -:tags: resources, browser, custom-guide, community, examples -:description: This guide explains how to create a walkthrough (in slideshow format) for the Neo4j Browser that enables your audience to explore your database and model interactively. -:page-type: Tutorial -:page-newsletter: true -:page-deprecated-title: the Getting Started Manual -:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/appendix/example-data/ - -// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future. - -.Goals -[abstract] -{description} - -.Prerequisites -[abstract] -You should have a good understanding of link:/developer/cypher-query-language/[Cypher] and the link:/developer/neo4j-browser[Neo4j Browser] and link:/developer/guide-data-modeling[graph data modeling] for your domain. -Other helpful knowledge (though not required) includes creating a link:/graphgists[GraphGist^] and familiarity with web user experience. - -[role=expertise {level}] -{level} - -[#custom-guide] -If you are familiar with the Neo4j Browser, chances are that you have used our interactive browser guides to learn concepts, load data, and explore your graph. -The popular {play}movie%20graph[`:play movie graph`^] browser guide is just one example of this. - -These guides help you provide a guided tour to a data set or use case to those connecting to Neo4j. -Your users can interactively read through the slideshow, execute statements to import and query the data, or look at pictures and other media for detailed explanations. -You can also inlcude special links for interacting with the Neo4j browser. - -There are a variety of uses for the browser guides ranging from personal development to sharing knowledge with colleagues or other developers. -Companies can even use these as onboarding or training tools for employees. -We use these guides ourselves in many areas, including those listed below. - -* link:/sandbox/?ref=developer-custom-guide[Neo4j Sandbox^] - each sandbox comes with a built-in, default guide to help you get started with whichever sandbox you chose! -* link:/graphacademy/[Online and classroom training^] - using these published guides in the classroom allows attendees to work through the material at their own pace and have access to the guide 24/7 after class ends. -* Meetups and presentations - presenters can blend slides with code for a better live experience on stage. -* Exploration of interesting datasets - publish all resources (data model, import, queries) in one place and refer back to that work whenever you need to. -* Rendering https://portal.graphgist.org[GraphGists^] as guides - converting GraphGists to browser guides allows you to access them inside your development environment and code alongside, rather than simply reading. - -.Example Northwind dataset guide -image:{img}browser-guide-northwind.jpg[role="popup-link"] - -Here are a few of our favorite examples using Neo4j browser guides: - -* The https://offshoreleaks.icij.org/pages/database[Panama Papers download^] comes with a comprehensive guide -* http://blog.bruggen.com/search/label/beergraphguide[Rik van Bruggens beer graph^] -* Daniel Himmelsteins https://think-lab.github.io/d/216/[Protein Networks (Hetnet)^] -* Will Lyons Game of Thrones Graph: {play}https://guides.neo4j.com/got[`:play https://guides.neo4j.com/got`^] -* https://twitter.com/graphcommons/status/815999498245853185[GraphCommons graphs^] -* Data Journalism: {play}https://guides.neo4j.com/ddj/[`:play https://guides.neo4j.com/ddj/`^] - -[#browser-guides] -== Technical Background - -The browser guides are simple HTML pages with one section per slide. -Each section of the slideshow is encompassed in `` tags, which users can interactively page to view content. -We can include statements for users to execute for importing or querying the data by putting them inside `
    ` tag areas.
-Pictures and other media for detailed explanations are enclosed in `` and other media tags.
-You can use other standard HTML elements to include tables, bullets, links, etc.
-There are some special CSS classes that interact actions within Neo4j Browser, e.g. for `:play` or `:help` commands.
-
-image::{img}browser-guide-form-fields.jpg[link="{play}northwind%20graph"]
-
-We can also use form fields within the guides to automatically populate the input in the text on slides and in statements.
-This is similar to the built-in {play}query%20template[`:play query-template`^].
-
-[#host-guide]
-== Hosting the Browser Guide
-
-The guides can be hosted anywhere.
-For security reasons, Neo4j's standard config is setup to accept files hosted on `guides.neo4j.com` and `localhost`.
-However, if you want to host the guide elsewhere, you will need to *whitelist* the other domain in your `$NEO4J_HOME/conf/neo4j.conf` along with the default whitelists, as shown below.
-
-[source,shell]
-----
-#comma-separated list of base-urls, or * for everything
-browser.remote_content_hostname_whitelist=https://example.com,https://guides.neo4j.com,localhost
-----
-
-The server hosting the guide is required to support proper CORS headers for both `GET` and `OPTIONS` requests because the Neo4j Browser accesses a foreign URL.
-This means that GitHub pages and Dropbox folders do not work, but Amazon S3 (or a custom Python or Java webserver) can be configured to work well enough.
-
-[NOTE]
---
-1. For security reasons, Javascript and Angular tags are stripped from the HTML, even from whitelisted sources.
-
-2. If your Neo4j instance is accessed via an `https://` URL, only guides hosted on `https://` URLs will render.
---
-
-You can also set your guide to display on Browser startup in two ways:
-
-.1. Add play command as setting in neo4j.conf
-[source,shell]
-----
-browser.post_connect_cmd=:play test
-----
-
-[NOTE]
---
-The Browser will search for a guide URL based on the host site plus guide name.
-For instance, the configuration setting above will look for a guide at the path `https://guides.neo4j.com/test`.
-This also works with guides in folders (i.e. `https://guides.neo4j.com/folder/test`).
---
-
-.2. Use URL parameters in a link to open Browser and pre-fill command to the guide
-[source,shell]
-[subs=attributes]
-----
-{play}<guide url or name>
-
-{play}https://guides.neo4j.com/got
-
-{play}northwind%20graph
-----
-
-[#format-create]
-== Slide Format and Creation
-
-The HTML format is https://github.com/neo4j-contrib/neo4j-guides/blob/master/docs/html-guides.adoc[described in detail here^].
-Don't worry, you do not have to create the HTML manually.
-Although, you can fine-tune the generated HTML from the next step afterwards.
-
-To make it easier to create Browser guides, we created a https://github.com/neo4j-contrib/neo4j-guides[simple tooling repository^] that uses AsciiDoc as source format and an HTML template with the slide structure.
-
-AsciiDoc is used in many places, including for O'Reilly books, various documentation manuals (such as ours), these developer pages, our link:/developer/kb[knowledge base^], https://portal.graphgist.org/about[GraphGists^], but also in Readme and Wiki files on GitHub.
-It was developed for technical documentation and is more powerful than markdown.
-
-You can find a simple https://github.com/neo4j-contrib/graphgist/blob/master/gists/syntax.adoc[syntax overview here^].
-
-We use the https://asciidoctor.org[AsciiDoctor^] toolchain and a variant of its https://github.com/asciidoctor/asciidoctor-backends/tree/master/erb/html5[HTML5 erb-templates^].
-
-Our process is straightforward.
-
-1. Find or create a simple AsciiDoc file (see below) and convert it to the slide-html.
-2. Turn each second-level header into a new slide and turn `[source,cypher]` blocks into clickable statements.
-3. Follow any other regular HTML transformations for other content.
-4. For deeper details on the AsciiDoc syntax, please see the https://asciidoctor.org/docs/user-manual/[AsciiDoctor User Manual^].
-
-[#example-guide]
-== Worked Example
-
-We will briefly step through an test guide as an example.
-You can later create your own custom guides for your material using these same steps.
-
-1. Clone and open the guide repository.
-+
-[source,shell]
-----
-git clone https://github.com/neo4j-contrib/neo4j-guides
-#SSH command is `git clone git@github.com:neo4j-contrib/neo4j-guides.git`
-cd neo4j-guides
-----
-
-2. Find the `adoc` directory and create a file called `test.adoc` inside it.
-+
-image::{img}custom_guide_test.jpg[role="popup-link"]
-
-3. Insert the contents below into the newly created `test.adoc` file and save the changes.
-+
-
-[source,shell,.small,indent=0]
-----
-    = A Test Guide
-
-    == First Slide: Media
-
-    image::https://avatars3.githubusercontent.com/u/201120[width=200,float=right]
-
-    This is just a test guide.
-
-    But it comes with a picture and a video:
-
-    ++++
-    
    
-    
-    
    
-    ++++
-
-    == Second Slide: Statements
-
-    === Creating Data
-
-    The area below becomes a clickable statement.
-
-    [source,shell]
-    ----
-    CREATE (db:Database {name:"Neo4j"})
-    RETURN db
-    ----
-
-    === Querying Data
-    :name: pass:a['Neo4j']
-
-    We use a form field here:
-
-    ++++
-    
-    ++++
-
-    [source,cypher,subs=attributes]
-    ----
-    MATCH (db:Database {name: $name})
-    RETURN db
-    ----
-
-    == Third Slide: Links
-
-    * https://neo4j.com/developer/cypher[Learn more about Cypher]
-    * pass:a[Help Keys]
-    * pass:a[Another Guide]
-
-    image::https://avatars3.githubusercontent.com/u/201120[width=100,link="https://example.com"]
-----
-
-4. Pass the `test.adoc` file to the `run.sh` script (as shown below) to convert to the HTML slides.
-+
-[source,shell]
-----
-./run.sh adoc/test.adoc html/test.html
-
-#optional arguments, leveloffset - to change the heading level up or down, base-url and additional attributes
-./run.sh path/to/test.adoc path/to/test.html [+1] https://example.com/guides/test
-
-#run the local python server to serve on localhost:8001
-python http-server.py
-----
-
-5. Test the test guide in your local browser:
-{play}https://localhost:8001/html/test.html[`:play https://localhost:8001/html/test.html`^]
-
-6. Upload the file to your target server.
-+
-[source,shell]
-----
-#Example target server
-s3cmd put -P html/test.html s3://guides.example.com/test
-----
-
-7. And test the guide one last time: `:play https://guides.example.com/test`
-+
-image::{img}browser-guide-demo.gif[]
-
-Congratulations!
-You have created your own custom browser guide to share your knowledge about Neo4j and can use these steps to create other helpful guides.
-
-[#gdoc-guide]
-== Creating Guides from Google Docs
-
-Something that is also really useful is to create guides from a collaboratively edited Google document.
-We will briefly explain how to do this.
-
-You can simply create a Google document with AsciiDoc content (like the one above) for collaborative editing.
-Make it publicly readable - in sharing settings, enable: "everyone with link can read".
-
-In the document, choose `File`, `Download as >`, `Plain Text (.txt)`.
-
-image::{img}gdownload_plaintxt.jpg[role="popup-link"]
-
-Then find the browser downloads and copy the link address of your Google Doc download.
-
-image::{img}chrome_downloads_link_address.jpg[role="popup-link"]
-
-Render the Google Doc to a browser guide, like we did before.
-An example using a script is shown below.
-
-.gdoc2guide.sh
-[source,shell]
-----
-#use the download id (not full link) to set the document id
-id=${1-"1HY3AX6dvd8UtJhp5XAsyFsQ0oyC6Z0pbwJvkyr4WHtM"}
-#choose a name for your guide
-name=${2-network}
-
-#use your full plain-text download link format here
-url="https://docs.google.com/a/neotechnology.com/document/export?format=txt&id=$\{id\}"
-
-curl -sL "$url" -o adoc/$name.adoc
-./run.sh adoc/$name.adoc html/$name.html
-s3cmd put -P html/$name.html s3://guides.neo4j.com/$name
-----
-
-[#sample-collection]
-== Example Collection
-
-In this section, we will list some of our existing and most popular browser guides we have created for users to learn and discover Neo4j.
-We hope that these will show some examples of things you can do with your own custom guides and encourage you to create and publish more alongside ours.
-
-This type of resource can help spread knowledge about Neo4j and the different kinds of things it can do and the problems it can solve.
-It can also show others how you went about constructing your graph model, importing your data set, and exploring that data as a graph.
-
-To see more built-in and community browser guides, check out the link:/developer/browser-guide-list[developer guide] for the full list of what is publicly available.
-
-=== Sandbox
-
-image::{img}sandbox_use_cases_2019.jpg[role="popup-link",float="right",width=350]
-
-link:/sandbox/?ref=developer-guide-example[Neo4j Sandbox^] uses Browser guides to step the user through the dataset presented for a particular use case.
-These guides are displayed when the sandbox is loaded and shows the steps for the data set background, model, loading, and querying.
-Some of our sandboxes even incorporate extensions and other tools, such as graph algorithms, APOC, and Bloom.
-
-=== ICIJ Panama Papers Guide
-
-image::{img}browser-guide-panama-papers.jpg[role="popup-link",float="right",width=350]
-
-The award-winning, investigative work around the link:/blog/icij-neo4j-unravel-panama-papers/[Panama Papers^] leak by the journalists of the https://www.icij.org/[ICIJ^] who used Neo4j to analyze terabytes of unstructured and structured data.
-The ICIJ went on to release follow-ups to the initial leak with the Paradise Papers, Offshore Leaks, and Bahamas Leaks.
-
-All of the data for these investigations is available as a https://offshoreleaks.icij.org/pages/database[database download^].
-We also have a sandbox on the https://sandbox.neo4j.com/?usecase=icij-paradise-papers&ref=developer-paradise-papers[Paradise Papers^] that includes a comprehensive browser guide to explore the vast network of offshore.
-
-=== GraphGist Portal
-
-image::{img}browser-guide-graphgist.jpg[role="popup-link",float="right",width=350]
-
-If you are not familiar with our link:/developer/graphgist/[GraphGists^], they are designed as a way for the Neo4j community to share their use cases and graphs with others.
-Our regular GraphGists are published on web pages and often include information about the project such as data model, sample queries, and project background.
-These pages are also designed to be interactive, so that visitors can execute queries and see results in the page.
-
-The https://portal.graphgist.org[GraphGist Portal^] is a separate website external to Neo4j and displays all of the GraphGists.
-The portal also provides a few additional features, most notably that it allows any GraphGist to be viewed as a browser guide.
-
-All you need to do to launch any one of these GraphGists as a browser guide is click on the GraphGist you are interested in from the GraphGists tab at the top, and then click `Run this gist in the Neo4j console` link on the right hand sidebar.
-This will bring up a smaller window with the `:play` command to run the guide in the Neo4j Browser and any potential whitelisting settings.
-
-You can also execute this Browser guide that lists a few of the GraphGist guides to check out: {play}https://guides.neo4j.com/graphgists/[`:play https://guides.neo4j.com/graphgists/`^]
-
-=== Built-in Training Guides
-
-Neo4j has created a few starter guides for those new to Neo4j to show them how to use it.
-Topics ranging from understanding what graph is to Cypher to modeling to import are provided with the links listed below.
-
-[options="header"]
-|===
-| Guide Name | Browser Command
-| Neo4j Browser Intro | `:play intro`
-| Neo4j Concepts | `:play concepts`
-| About Cypher | `:play fundamentals`
-| Intro to Cypher | `:play cypher`
-| The Movie Graph | `:play movie-graph`
-| Import: Relational to Graph | `:play northwind-graph`
-| Data Modeling: Flights | `:play modeling_airports`
-|===
-
-=== APOC
-
-image::{img}browser-guide-apoc.jpg[role="popup-link",float=right,width=350]
-
-We mentioned above that some of the Neo4j extensions and tools also had Browser guides.
-One of Neo4j's most popular libraries is link:/developer/neo4j-apoc/[APOC] (Awesome Procedures on Cypher).
-This project is packed full of useful procedures and functions for text manipulation, graph refactoring, data import, and more.
-It is also part of our link:/labs/[Neo4j Labs^] projects.
-
-Some of the https://neo4j-contrib.github.io/neo4j-apoc-procedures/[Github documentation^] content for APOC was turned into guides as an interactive manual.
-The Browser guide versions include background on the project and how to install APOC, as well as a few key procedures for loading different kinds of data, converting dates, and handling batching and background operations.
-
-* APOC guide: {play}https://guides.neo4j.com/apoc/[`:play apoc`^]
-
-=== Beer Graph Guide - Rik Van Bruggen
-
-Rik van Bruggen demonstrates in detail how to turn a data set or GraphGist into a proper Browser guide in the links provided.
-
-* Blog post: http://blog.bruggen.com/2016/03/the-beergraphguide-in-neo4j-browser.html[Making the BeerGraphGuide for Neo4j Browser^]
-* Blog post: http://blog.bruggen.com/2016/03/an-easier-better-tastier-beergraphguide.html[Improving the BeerGraphGuide^]
-* YouTube video: https://www.youtube.com/embed/jIT3O_fO7Tk[BeerGraphGuide^]
-
-=== HetNet Protein Networks - Daniel Himmelstein
-
-image::https://cloud.githubusercontent.com/assets/1117703/16320501/216f2626-3966-11e6-8a0d-215f70b44be2.png[link="https://cloud.githubusercontent.com/assets/1117703/16320501/216f2626-3966-11e6-8a0d-215f70b44be2.png",role="popup-link",float="right",width=350]
-
-Daniel used Browser guides to represent the topic of his PhD thesis - protein networks in a graph database.
-In https://think-lab.github.io/d/216/[this article^], he details the process of setting up a public server for hosting the dataset, as well as the steps involved in creating the guides.
-Daniel also presented his research at GraphConnect San Francisco in https://www.youtube.com/watch?v=jwhAlNgjvMA[this video^].
-
-=== Game of Thrones Character Interactions - Andrew Beveridge/Will Lyon
-
-Based on the popular https://en.wikipedia.org/wiki/Game_of_Thrones[Game of Thrones^] book series, mathematicians Andrew Beveridge and Jie Shan published the https://networkofthrones.wordpress.com/["Network of Thrones"^] research paper on interactions of characters in the books.
-Because a graph database follows the principles of network science, Will Lyon at Neo4j requested and received permission to take the published data and put it into Neo4j for analysis.
-
-Using this fun and familiar dataset, Will created a https://www.lyonwj.com/2016/06/26/graph-of-thrones-neo4j-social-network-analysis/[blog post^] that explains how to import the data into Neo4j and then expands into data science concepts of social network analysis and graph algorithms.
-Since then, Andrew Beveridge has released all https://networkofthrones.wordpress.com/data/[data^] for the existing book volumes, as well as for all seasons of the TV series version.
-
-The related "Graph of Thrones" Browser guide draws from the foundation of Will's original blog post, but also introduces the link:/developer/graph-algorithms/[Neo4j graph algorithms] library.
-This library is another one of Neo4j's popular extensions and includes algorithms for path-finding, centralities, communities, and more.
-Like link:/developer/neo4j-apoc/[APOC], the graph algorithms library is also part of link:/labs/[Neo4j Labs^].
-
-* Graph of Thrones intro guide: {play}https://guides.neo4j.com/got[`:play https://guides.neo4j.com/got`^]
-
-We also created a separate Graph of Thrones guide that aims to incorporate more of the universe with data from a variety of sources.
-
-* Graph of Thrones universe guide: {play}https://guides.neo4j.com/got/index.html[`:play https://guides.neo4j.com/got/index.html`^]
-
-=== Graph Commons
-
-https://graphcommons.com/[Graph Commons^], a website to create and share data networks, has a Neo4j example that can be played as a Browser guide using the URL shown below.
-
-{play}https://graphcommons.com/graphs/1a93e8fa-e3ce-4ec7-ba16-814b867d1bcb/neo4j[`:play https://graphcommons.com/graphs/1a93e8fa-e3ce-4ec7-ba16-814b867d1bcb/neo4j`^]
-
-[NOTE]
---
-You need to add the GraphCommons URL to the whitelist config, along with the default whitelists.
-
-[source,shell]
-----
-browser.remote_content_hostname_whitelist=https://graphcommons.com,https://guides.neo4j.com,localhost
-----
---
-
-++++
-
-
-++++
-
-=== jQAssistant
-
-The https://jqassistant.org[jQAssistant^] software analytics tool uses a guide to explore any scanned software project.
-The relevant Neo4j Browser guide with jQAssistant is listed below.
-
-{play}https://guides.neo4j.com/jqassistant[`:play https://guides.neo4j.com/jqassistant`^]
-
-== Resources
-
-* link:/developer/neo4j-browser/[Neo4j Browser Intro]
-* link:/developer/browser-guide-list/[Browser Guide List]
-* link:/graphgists/[GraphGist interactive guides^]
-* https://portal.graphgist.org/[GraphGist portal^]
diff --git a/modules/ROOT/pages/guide-data-modeling-notrack.adoc b/modules/ROOT/pages/guide-data-modeling-notrack.adoc
deleted file mode 100644
index 7f253e1c..00000000
--- a/modules/ROOT/pages/guide-data-modeling-notrack.adoc
+++ /dev/null
@@ -1,240 +0,0 @@
-= Graph Modeling Guidelines
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: modeling
-:tags: graph-modeling, data-model, schema, model-process, model-domain
-:description: This guide is designed to walk you through the graph data modeling lifecycle of Neo4j. You will be introduced to the basic process of designing a graph data model that can answer a wide range of business questions across a variety of domains.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should understand the basics of Neo4j's xref:graph-database.adoc#property-graph[property graph data model].
-
-[role=expertise {level}]
-{level}
-
-[#modeling-intro]
-== Introduction
-
-If you have ever worked with an object model or an entity-relationship diagram, the labeled property graph model will seem familiar.
-
-Graph data modeling is the process in which a user describes an arbitrary domain as a connected graph of nodes and relationships with properties and labels.
-A Neo4j graph data model is designed to answer questions in the form of Cypher queries and solve business and technical problems by organizing a data structure for the graph database.
-
-[#whiteboard-friendly]
-== Graph Data Model = Whiteboard-Friendly
-
-The graph data model is often referred to as being "whiteboard-friendly".
-Typically, when designing a data model, people draw example data on the whiteboard and connect it to other data drawn to show how different items connect.
-The whiteboard model is then re-formatted and structured to fit normalized tables for a relational model.
-
-A similar process exists in graph data modeling, as well.
-However, instead of modifying the data model to fit a normalized table structure, the graph data model stays exactly as it was drawn on the whiteboard.
-This is where the graph data model gets its name for being "whiteboard-friendly".
-
-Let us look at an example to demonstrate this.
-In the whiteboard drawing below, we have a data set about the movie "The Matrix".
-
-.Matrix - whiteboard model
-image:{img}matrix_whiteboard_model1.png[role="popup-link"]
-
-Next, we formalize our entities a bit and match expected syntax for relationship types to create the node/relationship view for the property graph model.
-
-.Matrix - match node and relationship format of property graph model
-image:{img}matrix_whiteboard_model2.png[role="popup-link"]
-
-For our next step, we add labels and determine properties of our nodes and relationships for the property graph model.
-
-.Matrix - add labels and properties
-image:{img}matrix_whiteboard_model3.png[role="popup-link"]
-
-Finally, we can view this data model in Neo4j and ensure it matches what we drew on the whiteboard.
-Also, notice how it is nearly identical to the whiteboard model we initially designed.
-
-.Matrix - final model in Neo4j
-image:{img}matrix_whiteboard_model4.png[role="popup-link"]
-
-The ability to easily whiteboard your data model makes the graph data model incredibly simple and visual.
-There is no need to draw up business model versions or explain ERD terms to business users.
-Instead, the graph data model is easily understood by anyone.
-
-[#describe-domain]
-== Describing a Domain
-
-To better understand the process of designing a graph data model, let us take an example domain for a small set of data and walk through each step of how to create a graph data model from it.
-Consider the following scenario describing our example data entities and connections.
-
-.Scenario
-[quote]
-Two _people_, *Sally* and *John*, +++are friends+++.
-Both *John* and *Sally* +++have read+++ the _book_, *Graph Databases*.
-
-
-We can use the information in this statement to build our model by identifying the components as labels, nodes, and relationships.
-Let us take the scenario into pieces and define them as parts of our property graph model.
-
-.Review - Property Graph Elements (click to zoom)
-image:{img}property_graph_elements.jpg[role="popup-link"]
-
-[#model-nodes]
-== Nodes
-
-The first entities that we will identify in our domain are the nodes.
-Nodes are one of two fundamental units that form a graph (the other fundamental unit is relationships).
-
-Nodes are often used to represent entities, but can also represent other domain components, depending on the use case.
-Nodes can contain properties that hold name-value pairs of data.
-Nodes can be assigned roles or types using one or more labels.
-
-****
-[TIP]
-You can often find nodes for the graph model by identifying nouns in your domain.
-Entities such as a car, a person, a customer, a company, an asset, and others similar can be defined as nodes for a good starting point.
-****
-
-We can identify nodes as entities with a unique conceptual identity.
-In our scenario we began for Sally and John, these entities are outlined below in bold.
-
-.Scenario - Defining Nodes
-[quote]
-Two people, *John* and *Sally*, are friends.
-Both *John* and *Sally* have read the book, *Graph Databases*.
-
-Extracting the nodes: +
-* *John* +
-* *Sally* +
-* *Graph Databases*
-
-****
-[NOTE]
-Remember that a graph database takes each instance of an entity as a separate node (John and Sally would be two separate nodes, even though they are both people), and Graph Databases would be a separate node from another book.
-****
-
-.Graph Model - Nodes
-image:{img}modeling_johnsally_nodes.jpg[400,400,role="popup-link"]
-
-[#add-labels]
-== Labels
-
-Now that we have an idea of what our nodes will be, we can decide what labels (if any) to assign our nodes to group or categorize them.
-The definition from https://neo4j.com/docs/developer-manual/current/[Neo4j's developer manual^] in the paragraph below best explains what labels do and how they are used in the graph data model.
-
-A label is a named graph construct that is used to group nodes into sets.
-All nodes labeled with the same label belongs to the same set.
-Many database queries can work with these sets instead of the whole graph, making queries easier to write and more efficient.
-A node may be labeled with any number of labels, including none, making labels an optional addition to the graph.
-
-****
-[TIP]
-Similar to how we found the nodes for our graph model by identifying the nouns in our scenario, you can identify labels by generic nouns or groups of persons, places, or things.
-General nouns that fit groups of items such as Vehicle, Person, Customer, Company, Asset, and similar terms can be used as labels in your graph.
-****
-
-To find out if we can group objects in our Sally and John scenario, we will start by identifying the roles of our nodes (John, Sally, Graph Databases) mentioned in the statement.
-We can find two different types of objects in the statement, which are emphasized below.
-
-.Scenario - Defining Labels
-[quote]
-Two _people_, John and Sally, are friends.
-Both John and Sally have read the _book_, Graph Databases.
-
-Extracting the labels: +
-* _Person_ +
-* _Book_
-
-Now that we have identified both our nodes and labels, we can update our graph data model to assign the labels to the nodes they describe.
-For *John* and *Sally*, we apply the role _Person_.
-For *Graph Databases*, we apply the role _Book_.
-
-.Graph Model - Labels
-image:{img}modeling_johnsally_labels.jpg[400,400,role="popup-link"]
-
-[#define-rels]
-== Relationships
-
-We now have our main entities and a way to group them, but we are still missing one vital piece of a graph database model - the relationships between the data!
-
-A relationship connects two nodes and allows us to find related nodes of data.
-It has a source node and a target node that shows the direction of the arrow.
-Although you must store a relationship in a particular direction, Neo4j has equal traversal performance in either direction, so you can query the relationship without specifying direction.
-
-The one core, consistent rule in a graph database is *"No broken links"*, ensuring that an existing relationship will never point to a non-existing endpoint.
-Since a relationship always has a start and end node, you cannot delete a node without also deleting its associated relationships.
-
-****
-[TIP]
-Just as we have found nodes and labels by looking for nouns, you can often find relationships for the graph model by identifying actions or verbs in your domain.
-Actions such as DRIVES, HAS_READ, MANAGES, ACTED_IN, and others similar can be defined as different types of relationships to exist between nodes.
-****
-
-Let us identify the interactions (which are underlined in our scenario below) between the *John*, *Sally*, and *Graph Database* nodes.
-
-.Scenario - Defining Relationships
-[quote]
-Two people, Sally and John, +++are friends+++.
-Both John and Sally +++have read+++ the book, Graph Databases.
-
-Relationships between nodes: +
-* John +++is friends with+++ Sally +
-* Sally +++is friends with+++ John +
-* John +++has read+++ Graph Databases +
-* Sally +++has read+++ Graph Databases
-
-To sum up our findings, our John and Sally nodes (labeled _Person_) can be connected to each other by the +++is friends with+++ relationship.
-John and Sally have both read the Graph Databases book, so we can connect each of their nodes (each labeled _Person_) to the Graph Databases node (labeled _Book_) with a +++has read+++ relationship.
-
-.Graph Model - Relationships
-image:{img}modeling_johnsally_relationships.jpg[400,400,role="popup-link"]
-
-[#fillin-properties]
-== Properties
-
-We have gone through the process of creating a basic graph data model for the interactions between people and books.
-We can take this data model further by defining attributes of these entities as key-value properties.
-
-Properties are name-value pairs of data that you can store on nodes or on relationships.
-Most standard data types are supported as properties, with the full list published in our https://neo4j.com/docs/developer-manual/current/introduction/graphdb-concepts/#graphdb-neo4j-properties[Developer Manual documentation^].
-
-Properties allow you to store relevant data about the node or relationship with the entity it describes.
-They can often be found by knowing what kinds of questions your use case needs to ask of your data.
-
-For our John and Sally scenario, we can list some questions that we might want to answer about the data.
-
-.Questions to ask of our John and Sally data model:
-* When did John and Sally become friends? Or how long have they been friends?
-* What is the average rating of the Graph Databases book?
-* Who is the author of the Graph Databases book?
-* How old is Sally?
-* How old is John?
-* Who is older, Sally or John?
-* Who read the _Graph Databases_ book first, Sally or John?
-
-From this list of questions, you can identify the attributes that we need to store on the entities within our data model in order to answer these questions.
-
-.Graph Model - Properties
-image:{img}modeling_johnsally_properties.jpg[400,400,role="popup-link"]
-
-With the final model, we now can answer each of the questions we defined in our list.
-Of course, we can grow and change the model over time and add/remove relationships, nodes, properties, and labels.
-The flexibility and simplicity of the property graph data model allows users to easily review the data structure and update it according to the changing needs of the business.
-
-[#graph-design]
-== Graph Data Modeling Design
-
-This guide is simply the introduction to data modeling using a simple, straightforward scenario.
-There are plenty of opportunities throughout the upcoming guides to practice modeling domains and analyzing changes to the model that might need to be made.
-
-Every data model is unique, depending on the use case and the types of questions that users need to answer with the data.
-Because of this, there is no "one-size-fits-all" approach to data modeling.
-Using best practices and careful modeling will provide the most valuable result in producing an accurate data model that benefits your processes and use case.
-A walkthrough of designs for different use cases is in our xref:modeling-designs.adoc[modeling designs] guide.
-
-[#modeling-resources]
-== Resources
-* link:/blog/data-modeling-basics/[Blog post: Graph Data Modeling Basics^]
-* link:/graphgists/[GraphGists: Graph Model Examples^]
-* link:/blog/data-modeling-pitfalls/[Blog post: Data Modeling Pitfalls to Avoid^]
diff --git a/modules/ROOT/pages/guide-data-modeling.adoc b/modules/ROOT/pages/guide-data-modeling.adoc
deleted file mode 100644
index 3325e859..00000000
--- a/modules/ROOT/pages/guide-data-modeling.adoc
+++ /dev/null
@@ -1,336 +0,0 @@
-= Graph Modeling Guidelines
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: modeling
-:tags: graph-modeling, data-model, schema, model-process, model-domain
-:description: This guide is designed to walk you through the graph data modeling lifecycle of Neo4j. You will be introduced to the basic process of designing a graph data model that can answer a wide range of business questions across a variety of domains.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-modeling/relational-to-graph-modeling/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Prerequisites
-[abstract]
-You should understand the basics of Neo4j's xref:graph-database.adoc#property-graph[property graph data model].
-
-[role=expertise {level}]
-{level}
-
-ifndef::env-guide[]
-[#modeling-intro]
-== Introduction
-
-If you have ever worked with an object model or an entity-relationship diagram, the labeled property graph model will seem familiar.
-
-Graph data modeling is the process in which a user describes an arbitrary domain as a connected graph of nodes and relationships with properties and labels.
-A Neo4j graph data model is designed to answer questions in the form of Cypher queries and solve business and technical problems by organizing a data structure for the graph database.
-
-[#whiteboard-friendly]
-== Graph Data Model = Whiteboard-Friendly
-
-The graph data model is often referred to as being "whiteboard-friendly".
-Typically, when designing a data model, people draw example data on the whiteboard and connect it to other data drawn to show how different items connect.
-The whiteboard model is then re-formatted and structured to fit normalized tables for a relational model.
-
-A similar process exists in graph data modeling, as well.
-However, instead of modifying the data model to fit a normalized table structure, the graph data model stays exactly as it was drawn on the whiteboard.
-This is where the graph data model gets its name for being "whiteboard-friendly".
-
-Let us look at an example to demonstrate this.
-In the whiteboard drawing below, we have a data set about the movie "The Matrix".
-
-.Matrix - whiteboard model
-image:{img}matrix_whiteboard_model1.png[role="popup-link"]
-
-Next, we formalize our entities a bit and match expected syntax for relationship types to create the node/relationship view for the property graph model.
-
-.Matrix - match node and relationship format of property graph model
-image:{img}matrix_whiteboard_model2.png[role="popup-link"]
-
-For our next step, we add labels and determine properties of our nodes and relationships for the property graph model.
-
-.Matrix - add labels and properties
-image:{img}matrix_whiteboard_model3.png[role="popup-link"]
-
-Finally, we can view this data model in Neo4j and ensure it matches what we drew on the whiteboard.
-Also, notice how it is nearly identical to the whiteboard model we initially designed.
-
-.Matrix - final model in Neo4j
-image:{img}matrix_whiteboard_model4.png[role="popup-link"]
-
-The ability to easily whiteboard your data model makes the graph data model incredibly simple and visual.
-There is no need to draw up business model versions or explain ERD terms to business users.
-Instead, the graph data model is easily understood by anyone.
-
-endif::[]
-
-[#describe-domain]
-== Describing a Domain
-
-To better understand the process of designing a graph data model, let us take an example domain for a small set of data and walk through each step of how to create a graph data model from it.
-Consider the following scenario describing our example data entities and connections.
-
-.Scenario
-[quote]
-Two _people_, *Sally* and *John*, +++are friends+++.
-Both *John* and *Sally* +++have read+++ the _book_, *Graph Databases*.
-
-
-We can use the information in this statement to build our model by identifying the components as labels, nodes, and relationships.
-Let us take the scenario into pieces and define them as parts of our property graph model.
-
-ifndef::env-guide[]
-.Review - Property Graph Elements (click to zoom)
-image:{img}property_graph_elements.jpg[role="popup-link"]
-
-[#model-nodes]
-== Nodes
-
-The first entities that we will identify in our domain are the nodes.
-Nodes are one of two fundamental units that form a graph (the other fundamental unit is relationships).
-
-Nodes are often used to represent entities, but can also represent other domain components, depending on the use case.
-Nodes can contain properties that hold name-value pairs of data.
-Nodes can be assigned roles or types using one or more labels.
-
-****
-[TIP]
-You can often find nodes for the graph model by identifying nouns in your domain.
-Entities such as a car, a person, a customer, a company, an asset, and others similar can be defined as nodes for a good starting point.
-****
-
-We can identify nodes as entities with a unique conceptual identity.
-In our scenario we began for Sally and John, these entities are outlined below in bold.
-endif::[]
-
-ifdef::env-guide[]
-== Defining Nodes
-endif::[]
-
-ifndef::env-guide[]
-.Scenario - Defining Nodes
-endif::[]
-[quote]
-Two people, *John* and *Sally*, are friends.
-Both *John* and *Sally* have read the book, *Graph Databases*.
-
-Extracting the nodes: +
-* *John* +
-* *Sally* +
-* *Graph Databases*
-
-****
-[NOTE]
-Remember that a graph database takes each instance of an entity as a separate node (John and Sally would be two separate nodes, even though they are both people), and Graph Databases would be a separate node from another book.
-****
-
-ifndef::env-guide[]
-.Graph Model - Nodes
-endif::[]
-
-image:{img}modeling_johnsally_nodes.jpg[400,400,role="popup-link"]
-
-ifndef::env-guide[]
-[#add-labels]
-== Labels
-
-Now that we have an idea of what our nodes will be, we can decide what labels (if any) to assign our nodes to group or categorize them.
-The definition from https://neo4j.com/docs/developer-manual/current/[Neo4j's developer manual^] in the paragraph below best explains what labels do and how they are used in the graph data model.
-
-A label is a named graph construct that is used to group nodes into sets.
-All nodes labeled with the same label belongs to the same set.
-Many database queries can work with these sets instead of the whole graph, making queries easier to write and more efficient.
-A node may be labeled with any number of labels, including none, making labels an optional addition to the graph.
-
-****
-[TIP]
-Similar to how we found the nodes for our graph model by identifying the nouns in our scenario, you can identify labels by generic nouns or groups of persons, places, or things.
-General nouns that fit groups of items such as Vehicle, Person, Customer, Company, Asset, and similar terms can be used as labels in your graph.
-****
-
-To find out if we can group objects in our Sally and John scenario, we will start by identifying the roles of our nodes (John, Sally, Graph Databases) mentioned in the statement.
-We can find two different types of objects in the statement, which are emphasized below.
-
-.Scenario - Defining Labels
-endif::[]
-ifdef::env-guide[]
-== Defining labels
-endif::[]
-
-[quote]
-Two _people_, John and Sally, are friends.
-Both John and Sally have read the _book_, Graph Databases.
-
-Extracting the labels: +
-* _Person_ +
-* _Book_
-
-Now that we have identified both our nodes and labels, we can update our graph data model to assign the labels to the nodes they describe.
-For *John* and *Sally*, we apply the label _Person_.
-For *Graph Databases*, we apply the label _Book_.
-
-ifndef::env-guide[]
-.Graph Model - Labels
-endif::[]
-
-image:{img}modeling_johnsally_labels.jpg[400,400,role="popup-link"]
-
-ifndef::env-guide[]
-[#define-rels]
-== Relationships
-
-We now have our main entities and a way to group them, but we are still missing one vital piece of a graph database model - the relationships between the data!
-
-A relationship connects two nodes and allows us to find related nodes of data.
-It has a source node and a target node that shows the direction of the arrow.
-Although you must store a relationship in a particular direction, Neo4j has equal traversal performance in either direction, so you can query the relationship without specifying direction.
-
-The one core, consistent rule in a graph database is *"No broken links"*, ensuring that an existing relationship will never point to a non-existing endpoint.
-Since a relationship always has a start and end node, you cannot delete a node without also deleting its associated relationships.
-
-****
-[TIP]
-Just as we have found nodes and labels by looking for nouns, you can often find relationships for the graph model by identifying actions or verbs in your domain.
-Actions such as DRIVES, HAS_READ, MANAGES, ACTED_IN, and others similar can be defined as different types of relationships to exist between nodes.
-****
-
-.Scenario - Defining Relationships
-endif::[]
-
-ifdef::env-guide[]
-== Defining Relationships
-endif::[]
-
-Let us identify the interactions (which are underlined in our scenario below) between the *John*, *Sally*, and *Graph Database* nodes.
-
-[quote]
-Two people, Sally and John, +++are friends+++.
-Both John and Sally +++have read+++ the book, Graph Databases.
-
-Relationships between nodes: +
-* John +++is friends with+++ Sally +
-* Sally +++is friends with+++ John +
-* John +++has read+++ Graph Databases +
-* Sally +++has read+++ Graph Databases
-
-To sum up our findings, our John and Sally nodes (labeled _Person_) can be connected to each other by the +++is friends with+++ relationship.
-John and Sally have both read the Graph Databases book, so we can connect each of their nodes (each labeled _Person_) to the Graph Databases node (labeled _Book_) with a +++has read+++ relationship.
-
-ifndef::env-guide[]
-.Graph Model - Relationships
-endif::[]
-
-image:{img}modeling_johnsally_relationships.jpg[400,400,role="popup-link"]
-
-ifndef::env-guide[]
-[#fillin-properties]
-== Properties
-
-We have gone through the process of creating a basic graph data model for the interactions between people and books.
-We can take this data model further by defining attributes of these entities as key-value properties.
-
-Properties are name-value pairs of data that you can store on nodes or on relationships.
-Most standard data types are supported as properties, with the full list published in our https://neo4j.com/docs/developer-manual/current/introduction/graphdb-concepts/#graphdb-neo4j-properties[Developer Manual documentation^].
-
-Properties allow you to store relevant data about the node or relationship with the entity it describes.
-They can often be found by knowing what kinds of questions your use case needs to ask of your data.
-
-endif::[]
-ifdef::env-guide[]
-== Defining Properties
-endif::[]
-
-For our John and Sally scenario, we can list some questions that we might want to answer about the data.
-
-.Questions to ask of our John and Sally data model:
-* When did John and Sally become friends? Or how long have they been friends?
-* What is the average rating of the Graph Databases book?
-* Who is the author of the Graph Databases book?
-* How old is Sally?
-* How old is John?
-* Who is older, Sally or John?
-* Who read the _Graph Databases_ book first, Sally or John?
-
-From this list of questions, you can identify the attributes that we need to store on the entities within our data model in order to answer these questions.
-
-ifndef::env-guide[]
-.Graph Model - Properties
-endif::[]
-
-image:{img}modeling_johnsally_properties.jpg[400,400,role="popup-link"]
-
-With the final model, we now can answer each of the questions we defined in our list.
-Of course, we can grow and change the model over time and add/remove relationships, nodes, properties, and labels.
-The flexibility and simplicity of the property graph data model allows users to easily review the data structure and update it according to the changing needs of the business.
-
-ifdef::env-guide[]
-== Implementing the model
-
-You use Cypher statements to create your graph. There are many ways to load data into the graph. Here we use the MERGE clause to create the data model.
-
-Execute this code to create the graph for this simple data model:
-
-[source,Cypher]
-----
-MERGE (j:Person {name: 'John'})
-  ON CREATE set j.age = 27
-MERGE (s:Person {name: 'Sally'})
-  ON CREATE set s.age = 32
-MERGE (b:Book {title: 'Graph Databases'})
-  ON CREATE set b.authors = ['Jim Webber', 'Ian Robinson']
-MERGE (j)-[rel1:IS_FRIENDS_WITH]->(s)
-  ON CREATE SET rel1.since = '01/09/2013'
-MERGE (j)-[rel2:HAS_READ]->(b)
-  ON CREATE SET rel2.on = '02/03/2013', rel2.rated = 5
-MERGE (s)-[rel3:HAS_READ]->(b)
-  ON CREATE SET rel3.on = '02/09/2013', rel3.rated = 4
-----
-
-== Viewing the data in the graph
-
-After you have created the graph, you can view it with this statement:
-
-[source,Cypher]
-----
-MATCH (n) RETURN n
-----
-
-In Neo4j Browser, you can hover over each node and relationship in the graph to view its properties.
-
-.Implemented model
-image:{img}ImplementedModel.jpg[400,400,role="popup-link"]
-
-endif::[]
-
-[#graph-design]
-== Graph Data Modeling Design
-
-This guide is simply the introduction to data modeling using a simple, straightforward scenario.
-There are plenty of opportunities throughout the upcoming guides to practice modeling domains and analyzing changes to the model that might need to be made.
-
-Every data model is unique, depending on the use case and the types of questions that users need to answer with the data.
-Because of this, there is no "one-size-fits-all" approach to data modeling.
-Using best practices and careful modeling will provide the most valuable result in producing an accurate data model that benefits your processes and use case.
-A walkthrough of designs for different use cases is in our
-ifndef::env-guide[]
-xref:modeling-designs.adoc[modeling designs^]
-endif::[]
-ifdef::env-guide[]
-https://neo4j.com/developer/modeling-designs/[modeling designs^]
-endif::[]
-guide.
-
-[#modeling-resources]
-== Resources
-ifdef::env-guide[]
-* https://neo4j.com/blog/data-modeling-basics/[Blog post: Graph Data Modeling Basics^]
-* https://neo4j.com/graphgists/[GraphGists: Graph Model Examples^]
-* https://neo4j.com/blog/data-modeling-pitfalls/[Blog post: Data Modeling Pitfalls to Avoid^]
-endif::[]
-ifndef::env-guide[]
-* link:/blog/data-modeling-basics/[Blog post: Graph Data Modeling Basics^]
-* link:/graphgists/[GraphGists: Graph Model Examples^]
-* link:/blog/data-modeling-pitfalls/[Blog post: Data Modeling Pitfalls to Avoid^]
-endif::[]
-*  https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^] course on GraphAcademy
diff --git a/modules/ROOT/pages/guide-import-csv.adoc b/modules/ROOT/pages/guide-import-csv.adoc
deleted file mode 100644
index 045a4c4b..00000000
--- a/modules/ROOT/pages/guide-import-csv.adoc
+++ /dev/null
@@ -1,495 +0,0 @@
-= Importing CSV Data into Neo4j
-:level: Intermediate
-:page-level: Intermediate
-:author: Jennifer Reif
-:category: import-export
-:tags: data-import, import-csv, graph-import, load-csv, admin-import, kettle
-:description: This article demonstrates different approaches to importing CSV data into Neo4j and solutions to potential issues that might arise during the process.
-:page-pagination:
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-import/csv-import/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Before importing data, you should be familiar with what a link:/developer/graph-database/[graph database] is, how to construct a xref:guide-data-modeling.adoc[property graph data model], and some basics of the link:/developer/cypher[Cypher query language].
-All of these skills are a part of the data import process.
-
-[role=expertise {level}]
-{level}
-
-CSV is a file of comma-separated values, often viewed in Excel or some other spreadsheet tool.
-There can be other types of values as the delimiter, but the most standard is the comma.
-Many systems and processes today already convert their data into CSV format for file outputs to other systems, human-friendly reports, and other needs.
-It is a standard file format that humans and systems are already familiar with using and handling.
-
-Giving Neo4j the ability to read and load a CSV file helps reduces the friction of getting data from various formats and systems into Neo4j.
-
-== Ways to Import CSV Files
-
-There are a few different approaches to get CSV data into Neo4j, each with varying criteria and functionality.
-The option you choose will depend on the data set size, as well as your degree of comfort with various tools.
-
-Let us see some of the ways Neo4j can read and import CSV files.
-
-1. `LOAD CSV` Cypher command: this command is a great starting point and handles small- to medium-sized data sets (up to 10 million records).  _Works with any setup, including AuraDB._
-2. `neo4j-admin` bulk import tool: command line tool useful for straightforward loading of large data sets.  _Works with Neo4j Desktop, Neo4j EE Docker image and local installations._
-3. Kettle import tool: maps and executes steps for the data process flow and works well for very large data sets, especially if developers are already familiar with using this tool.  _Works with any setup, including AuraDB._
-
-We will take a brief view of each one of these tools, how they operate, and how to get started with a general use case.
-More documentation and information for each will also be included for help on more complex scenarios.
-Data quality can also be an issue for any type of data import to any system, so we will cover a few of these potential difficulties and how to solve them.
-
-:cta-header: Try it out on AuraDB
-include::./_includes/aura_cta.adoc[]
-
-[#import-load-csv]
-== LOAD CSV command with Cypher
-
-The `LOAD CSV` clause is part of the Cypher query language.
-Our link:/docs/cypher-manual/current/[Cypher manual^] contains a page devoted to its usage, and a variety of Neo4j's blogs, videos, solutions, and other material utilizes this command.
-It is simple to use and widely applicable.
-`LOAD CSV` is not just your basic data ingestion mechanism because it combines multiple aspects into a single operation.
-
-* Supports loading / ingesting CSV data from a URI
-* Directly maps input data into complex graph/domain structure
-* Handles data conversion
-* Supports complex computations
-* Creates or merges entities, relationships, and structure
-
-
-
---
-[NOTE]
-For better control, you can run `LOAD CSV` commands with `cypher-shell` instead of in the browser.
-More information is in the manual page on link:/docs/operations-manual/3.5/tools/cypher-shell/[Cypher shell^].
---
-
-=== Reading CSV Files
-
-`LOAD CSV` can handle local and remote files, and there is some syntax associated with each.
-This can be an easy thing to miss and end up with an access error, so we will try to clarify the rules here.
-
-*Local files* may be loaded using a `file:///` prefix before the file name.  +
-The the local file approach will not work with link:{aura_signup}[AuraDB] since it is cloud-based.
-
-Neo4j security has a default setting that local files can only be read from the Neo4j import directory, which is different based on your operating system.
-File locations for each OS are listed in our {opsmanual}/configuration/file-locations[Neo4j Operations Manual^].
-We recommend putting files in Neo4j's _import_ directory, as it keeps the environment secure.
-However, if you need to access files in other locations, you can find out which setting to alter in our {cyphermanual}/clauses/load-csv/#query-load-csv-introduction[manual^].
-Default import folder paths shown in link:/developer/kb/import-csv-locations/[this article^].
-
-.Examples
-[source, cypher, role= nocopy noplay]
-----
-//Example 1 - file directly placed in import directory (import/data.csv)
-LOAD CSV FROM "file:///data.csv"
-
-//Example 2 - file placed in subdirectory within import directory (import/northwind/customers.csv)
-LOAD CSV FROM "file:///northwind/customers.csv"
-----
-
-*Web-hosted files* can be referenced directly with their URL, like `+https://host/path/data.csv+`.
-However, permissions must be set so that an external source can read the file.
-For more information about access related to online file imports, see this link:/developer/kb/import-csv-locations/[knowledge base article^].
-
-.Examples
-[source, cypher, role= nocopy noplay]
-----
-//Example 1 - website
-LOAD CSV FROM 'https://data.neo4j.com/northwind/customers.csv'
-
-//Example 2 - Google
-LOAD CSV WITH HEADERS FROM 'https://docs.google.com/spreadsheets/d//export?format=csv'
-----
-
-=== Important Tips for LOAD CSV
-
-There are a few things to keep in mind with `LOAD CSV` and a few helpful tips for handling the variety of data scenarios you are likely to encounter.
-
-* All data from the CSV file is read as a string, so you need to use `toInteger()`, `toFloat()`, `split()` or similar functions to convert values.
-* Check your Cypher import statement for typos. Labels, property names, relationship-types, and variables are *case-sensitive*.
-* The cleaner the data, the easier the load. Try to handle complex cleanup/manipulation before load.
-
-=== Converting Data Values with LOAD CSV
-
-Cypher has some scrubbing and conversion capabilities to help with data cleanup.
-These are extremely useful for handling missing data or splitting a field into multiple values for the graph.
-
-First, remember that Neo4j does not store null values.
-Null or empty fields in a CSV files can be skipped or replaced with default values in `LOAD CSV`.
-
-Suppose we have this CSV file:
-
-.companies.csv
-[source]
-----
-Id,Name,Location,Email,BusinessType
-1,Neo4j,San Mateo,contact@neo4j.com,P
-2,AAA,,info@aaa.com,
-3,BBB,Chicago,,G
-----
-
-[NOTE]
-The default location for CSV files for import is the *import* directory for your Neo4j instance.
-
-Here are some examples of importing this data.
-
-.Examples
-[source,cypher,role=noplay]
-----
-//skip null values
-LOAD CSV WITH HEADERS FROM 'file:///companies.csv' AS row
-WITH row WHERE row.Id IS NOT NULL
-MERGE (c:Company {companyId: row.Id});
-
-// clear data
-MATCH (n:Company) DELETE n;
-
-//set default for null values
-LOAD CSV WITH HEADERS FROM 'file:///companies.csv' AS row
-MERGE (c:Company {companyId: row.Id, hqLocation: coalesce(row.Location, "Unknown")})
-
-// clear data
-MATCH (n:Company) DELETE n;
-
-//change empty strings to null values (not stored)
-LOAD CSV WITH HEADERS FROM 'file:///companies.csv' AS row
-MERGE (c:Company {companyId: row.Id})
-SET c.emailAddress = CASE trim(row.Email) WHEN "" THEN null ELSE row.Email END
-----
-
-Next, if you have a field in the CSV that is a list of items that you want to split, you can use the Cypher `split()` function to separate arrays in a cell.
-
-Suppose we have this CSV file:
-
-.employees.csv
-[source]
-----
-Id,Name,Skills,Email
-1,Joe Smith,Cypher:Java:JavaScript,joe@neo4j.com
-2,Mary Jones,Java,mary@neo4j.com
-3,Trevor Scott,Java:JavaScript,trevor@neo4j.com
-----
-
-.Example
-[source,cypher,role=noplay]
-----
-LOAD CSV WITH HEADERS FROM 'file:///employees.csv' AS row
-MERGE (e:Employee {employeeId: row.Id, email: row.Email})
-WITH e, row
-UNWIND split(row.Skills, ':') AS skill
-MERGE (s:Skill {name: skill})
-MERGE (e)-[r:HAS_EXPERIENCE]->(s)
-----
-
-Conditional conversions can be achieved with `CASE`.
-You saw one example of this when we were checking for null values or empty strings, but let us look at another example.
-
-.Example
-[source,cypher,role=noplay]
-----
-// clear data
-MATCH (n:Company) DELETE n;
-
-//set businessType property based on shortened value in CSV
-LOAD CSV WITH HEADERS FROM 'file:///companies.csv' AS row
-WITH row WHERE row.Id IS NOT NULL
-WITH row,
-(CASE row.BusinessType
- WHEN 'P' THEN 'Public'
- WHEN 'R' THEN 'Private'
- WHEN 'G' THEN 'Government'
- ELSE 'Other' END) AS type
-MERGE (c:Company {companyId: row.Id, hqLocation: coalesce(row.Location, "Unknown")})
-SET c.emailAddress = CASE trim(row.Email) WHEN "" THEN null ELSE row.Email END
-SET c.businessType = type
-RETURN *
-----
-
-=== Optimizing LOAD CSV for Performance
-
-Often, there are ways to improve performance during data load, which are especially helpful when dealing with large amounts of data or complex loading.
-
-To improve inserting or updating unique entities into your graph (using `MERGE` or `MATCH` with updates), you can create indexes and constraints declared for each of the labels and properties you plan to merge or match on.
-
---
-[NOTE]
-For best performance, always `MATCH` and `MERGE` on a single label with the indexed primary-key property.
---
-
-Suppose we use the above *companies.csv* file, and we now have a file that contains people and which companies they work for:
-
-.people.csv
-[source]
-----
-employeeId,Name,Company
-1,Bob Smith,1
-2,Joe Jones,3
-3,Susan Scott,2
-4,Karen White,1
-----
-
-You should also separate node and relationship creation into separate processing.
-For instance, instead of the following:
-
-[source,cypher,role= nocopy noplay]
-----
-MERGE (e:Employee {employeeId: row.employeeId})
-MERGE (c:Company {companyId: row.companyId})
-MERGE (e)-[r:WORKS_FOR]->(c)
-----
-
-You can write it like this:
-
-[source,cypher,role=noplay]
-----
-// clear data
-MATCH (n)
-DETACH DELETE n;
-// load Employee nodes
-LOAD CSV WITH HEADERS FROM 'file:///people.csv' AS row
-MERGE (e:Employee {employeeId: row.employeeId, name: row.Name})
-RETURN count(e);
-// load Company nodes
-LOAD CSV WITH HEADERS FROM 'file:///companies.csv' AS row
-WITH row WHERE row.Id IS NOT NULL
-WITH row,
-(CASE row.BusinessType
- WHEN 'P' THEN 'Public'
- WHEN 'R' THEN 'Private'
- WHEN 'G' THEN 'Government'
- ELSE 'Other' END) AS type
-MERGE (c:Company {companyId: row.Id, hqLocation: coalesce(row.Location, "Unknown")})
-SET c.emailAddress = CASE trim(row.Email) WHEN "" THEN null ELSE row.Email END
-SET c.businessType = type
-RETURN count(c);
-// create relationships
-LOAD CSV WITH HEADERS FROM 'file:///people.csv' AS row
-MATCH (e:Employee {employeeId: row.employeeId})
-MATCH (c:Company {companyId: row.Company})
-MERGE (e)-[:WORKS_FOR]->(c)
-RETURN *;
-----
-
-This way, the load is only doing one piece of the import at a time and can move through large amounts of data quickly and efficiently, reducing heavy processing.
-
-When the amount of data being loaded is too much to fit into memory, there are a couple of different approaches you can use to combat running out of memory during the data load.
-
-1. Batch the import into sections with `PERIODIC COMMIT`.
-This clause can be added before the `LOAD CSV` clause to tell Cypher to only process so many rows of the file before clearing memory and transaction state.
-For more information, see the link:/docs/cypher-manual/current/query-tuning/using/#query-using-periodic-commit-hint[manual page^] on `PERIODIC COMMIT`.
-+
-.Example
-[source,cypher, role= nocopy noplay]
-----
-:auto USING PERIODIC COMMIT 500
-LOAD CSV WITH HEADERS FROM 'file:///data.csv' AS row
-...
-----
-
-2. Avoid the EAGER operator.
-Some statements pull in more rows than what is necessary, adding extra processing up front.
-To avoid this, you can run `PROFILE` on your queries to see if they use EAGER loading and either modify queries or run multiple passes on the same file, so it does not do this.
-More information about EAGER loading and how to avoid can be found in https://markhneedham.com/blog/2014/10/23/neo4j-cypher-avoiding-the-eager/[Mark's blog post^].
-
-3. Adjust configuration for the database on heap and memory to avoid page-faults.
-To help handle larger volumes of transactions, you can increase some configuration settings for the database and restart the instance for them to take effect. Usually, you can create or update 1M records in a single transaction per 2 GB of heap. In `neo4j.conf`:
-* `dbms.memory.heap.initial_size` and `dbms.memory.heap.max_size`: set to at least 4G.
-* `dbms.memory.pagecache.size`: ideally, value large enough to keep the whole database in memory.
-
-==== LOAD CSV Resources
-* link:/developer/desktop-csv-import/[HowTo: Import CSV in Neo4j Desktop]
-* link:/docs/cypher-manual/current/clauses/load-csv/[Cypher Manual: LOAD CSV^]
-* link:/developer/guide-importing-data-and-etl/[Example: Import Northwind Data Set]
-* link:https://youtu.be/Eh_79goBRUk[Video: LOAD CSV in the Real World^]
-* link:https://graphacademy.neo4j.com/courses/importing-data/[Importing CSV Data into Neo4j^]
-
-[#batch-importer]
-== Bulk Importer For Large Datasets
-
-`LOAD CSV` is great for importing small- or medium-sized data (up to 10M records).
-For datasets larger than this, you can use the command line bulk importer.
-The `neo4j-admin import` tool allows you to import CSV data to an empty database by specifying node files and relationship files.
-
-Suppose you want to this tool it to import order data into Neo4j. Here are the CSV files.
-Notice that some of the include headers and some will have separate header files.
-If you want to perform the import, you place them in the *import* folder for your Neo4j instance.
-
-.customers.csv
-[source]
-----
-customerId:ID(Customer), name
-23, Delicatessen Inc
-42, Delicious Bakery
-----
-
-.products.csv
-[source]
-----
-productId:ID(Product), name, price, :LABEL
-11,Chocolate,10,Product;Food
-----
-
-.orders_header.csv
-[source]
-----
-orderId:ID(Order),date,total,customerId:IGNORE
-----
-
-.customer_orders_header.csv
-[source]
-----
-:END_ID(Order),date:IGNORE,total:IGNORE,:START_ID(Customer)
-----
-
-.orders1.csv
-[source]
-----
-1041,2020-05-10,130,23
-----
-
-.orders2.csv
-[source]
-----
-1042,2020-05-12,20,42
-----
-
-.order_details.csv
-[source]
-----
-:START_ID(Order),amount,price,:END_ID(Product)
-1041,13,130,11
-1042,2,20,11
-----
-
-The tool is located in `/bin/neo4j-admin` and you run it in a terminal window where you have navigated to the *import* folder for your Neo4j instance.
-
-Here is an example of importing the above CSV files in Neo4j 4.x. You must specify the name of the database. In this case we specify *orders*.
-
-[source, shell]
-----
-../bin/neo4j-admin import --database orders
-     --nodes=Customer=customers.csv
-     --nodes=products.csv
-     --nodes=Order="orders_header.csv,orders1.csv,orders2.csv"
-     --relationships=CONTAINS=order_details.csv
-     --relationships=ORDERED="customer_orders_header.csv,orders1.csv,orders2.csv"
-     --trim-strings=true
-----
-
-[NOTE]
-You must specify the parameters to this script on a *single* line. Line feeds are shown here for readability.
-
-When you execute this command, it creates a new database named *orders*.
-
-The repeated `--nodes` and `--relationships` parameters are groups of multiple (potentially split) CSV files of the same entity, i.e. with the same column structure.
-
-All files per group are treated as if they could be concatenated as a single large file.
-A *header row* in the first file of the group or in a separate, single-line file is required.
-Placing the header in a separate file can make it easier to handle and edit than having it in a multi-gigabyte text file.
-Compressed files are also supported.
-
-* The `--id-type=STRING` indicates that all `:ID` columns contain alphanumeric values (there is an optimization for numeric-only IDs).
-* The `customers.csv` is imported directly as nodes with the `:Customer` label and the properties are taken directly from the file.
-* `Product` nodes follow the same pattern where the node-labels are taken from the `:LABEL` column.
-* The `Order` nodes are taken from 3 files - one header and two content files.
-* Line item relationships typed `:CONTAINS` are created from `order_details.csv`, relating orders with the contained products via their IDs.
-* Orders are connected to customers by using the order CSV files again, but this time with a different header, which :IGNORE's the non-relevant columns.
-
-The column names are used for property-names of your nodes and relationships.
-There is specific markup on specific columns, which we will explain.
-
-* `name:ID` - global id column used to look up the node later reconnecting.
-** if the property name is left off, it will be not stored (temporary), which is what the `--id-type` refers to.
-** if you have repeated IDs across entities, you have to provide the entity (id-group) in parentheses like `:ID(Order)`.
-** if your IDs are globally unique, you can leave that off.
-* `:LABEL` - label column for nodes. Multiple labels can be separated by delimiter.
-* `:START_ID`, `:END_ID` - relationship file columns referring to the node ids. For id-groups, use `:END_ID(Order)`.
-* `:TYPE` - column to specify relationship-type.
-* All other columns are treated as properties but skipped if empty or annotated with `:IGNORE`.
-* Type conversion is possible by suffixing the name with indicators like `:INT`, `:BOOLEAN`, etc.
-
-For more details on this header format and the tool, see the documentation in the {opsmanual}/tools/neo4j-admin-import/[Neo4j Manual^] and the accompanying {opsmanual}/tutorial/neo4j-admin-import/[tutorial^].
-
-There is also a lesson in the link:https://neo4j.com/graphacademy/training-importing-data-40/enrollment/[Importing Data with Neo4j 4.x: Using neo4j-admin] that covers using the neo4j-admin import tool.
-
-[#data-load-quality]
-== CSV Data Quality
-
-Real-world data is messy.
-Any time you work with data, you will see some values that need cleaned up or transformed before you move it to another system.
-Small syntax errors, format descriptions, consistency or correct quoting, and even differing assumptions on data requirements or standards can easily cause hours of cleanup down the road.
-
-We will highlight some of the data quality issues easily missed when loading data from other systems into Neo4j and try to help avoid problems with data import and cleanup.
-
-=== Common Pitfalls
-
-*Headers are inconsistent with data (missing, too many columns, different delimiter in header)*
-Verify headers match the data in the file.
-Adjusting formatting, delimiters, columns, etc. at this stage will save a great deal of time later.
-
-*Extra or missing quotes throughout file*
-Standalone double or single quotes in the middle of non-quoted text or non-escaped quotes in quoted text can cause issues reading the file for loading.
-It is best to either escape or remove stray quotes.
-Documentation for proper escaping is in the xref:cypher:style-guide.adoc#cypher-metacharacters[Cypher style guide] and a link:/developer/kb/parsing-of-quotes-for-load-csv-and-or-import/[knowledgebase article^].
-
-*Special or Newline characters in file*
-When dealing with any special characters in a file, ensure they are quoted or remove them.
-For newline characters in quoted or unquoted fields, either add quotes for these or remove them.
-
-*Inconsistent line breaks*
-One thing that computers do not handle well is inconsistent data.
-Ensure line breaks are consistent throughout.
-We recommend choosing the Unix style for compatibility with Linux systems (common format for import tools).
-
-*Binary zeros, BOM byte order mark (2 UTF-8 bytes) at beginning of file, or other non-text characters*
-Any unusual characters or tool-specific formatting (Excel or Word) are sometimes hidden in application tools, but become easily apparent in basic editors.
-If you come across these types of characters in your file, it is best to remove them entirely.
-
-=== Tools
-
-As mentioned above, certain applications have special formatting to make documents look nice, but this hidden extra code is not handled by regular file readers and scripts.
-Other times, it is hard to find small syntax changes or make broad adjustments for files with a lot of data.
-
-For handling these types of situations or general data cleanup, there are a number of tools that help you check and validate your CSV data files.
-
-Basic tools, such as hexdump, vi, emacs, UltraEdit, and Notepad++ work well for handling shortcut-based commands for editing and manipulating files.
-However, there are also other more efficient or user-friendly options available that assist in data cleanup and formatting.
-
-* link:https://csvkit.readthedocs.io/en/latest/[CSVKit^] - a set of Python tools that provides statistics (csvstat), search (csvgrep), and more for your CSV files.
-
-* link:http://csvlint.io/[CSVLint^] - an online service to validate CSV files.
-You can upload the file or provide an URL to load it.
-
-* link:https://www.papaparse.com/[Papa Parse^] - a comprehensive Javascript library for CSV parsing that allows you to stream CSV data and provides good, human-readable error reporting on issues.
-
-* link:/developer/desktop-csv-import/#inspect-files[Cypher] - what Cypher sees is what will be imported, so you can use that to your advantage.
-Using `LOAD CSV` without creating graph structure will just output samples, counts, or distributions to make it possible to detect incorrect header column counts, delimiters, quotes, escapes, or header name spellings.
-
-[source, cypher, role= nocopy noplay]
-----
-// assert correct line count
-LOAD CSV FROM "file-url" AS line
-RETURN count(*);
-
-// check first 5 line-sample with header-mapping
-LOAD CSV WITH HEADERS FROM "file-url" AS line
-RETURN line
-LIMIT 5;
-----
-
-[#import-csv-resources]
-== CSV Import Resources
-
-* {opsmanual}/tools/neo4j-admin-import/[Manual: Import Tool^]
-* {opsmanual}/tutorial/neo4j-admin-import/#tutorial-neo4j-admin-import[Manual: Import Tool Tutorial^]
-* link:/developer/kb/?tag=load-csv[Knowledgebase Articles: LOAD CSV^]
-* link:https://github.com/neo4j-contrib/northwind-neo4j[GitHub project: Northwind CSV files^]
-* {opsmanual}/configuration/file-locations[Manual: Neo4j File Locations^]
-* link:/developer/kb/import-csv-locations/[Knowledgebase: Default Import Folder Path^].
diff --git a/modules/ROOT/pages/guide-import-json-rest-api.adoc b/modules/ROOT/pages/guide-import-json-rest-api.adoc
deleted file mode 100644
index da21135a..00000000
--- a/modules/ROOT/pages/guide-import-json-rest-api.adoc
+++ /dev/null
@@ -1,170 +0,0 @@
-= Importing JSON Data from a REST API into Neo4j
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: import-export
-:tags: data-import, graph-import, import-json, pagination, api
-:description: This article demonstrates some techniques for loading data from JSON-based REST APIs into Neo4j.
-:page-pagination:
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-import/json-rest-api-import/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Before importing data from a JSON-based REST API you should have installed the link:/developer/neo4j-apoc[APOC^] library.
-
-[role=expertise {level}]
-{level}
-
-[#import-json]
-== Importing JSON Data into Neo4j
-
-There are a plethora of JSON-based Web APIs that we can import into Neo4j, and we can use one of the https://neo4j.com/docs/labs/apoc/current/import/load-json[Load JSON^] procedures to retrieve data from these APIs and turn it into map values ready for Cypher to consume.
-
-The APOC user guide provides a worked example showing how to https://neo4j.com/docs/labs/apoc/current/import/load-json/#_load_json_stackoverflow_example[import data from StackOverflow into Neo4j^].
-
-[#strava-api]
-== The Strava API
-
-Strava is an application used by runners and cyclists to record their activities and share them with their friends.
-This data is available to users via a https://developers.strava.com/[JSON-based REST API^].
-
-Before we start calling the API, we need to https://www.strava.com/settings/api[create an application^].
-We will then be provided with an access token that we will need to use in all our requests to the API.
-
-We can create a parameter in the link:/developer/neo4j-browser/[Neo4j Browser] or Cypher shell by executing the following command:
-
-[source, cypher]
-----
-:params {stravaToken: "Bearer "}
-----
-
-****
-[IMPORTANT]
-Don't forget to replace `` with the token for your Strava application.
-****
-
-[#paginated-endpoint]
-== Working with a paginated endpoint
-
-We are interested in importing the activities for the https://developers.strava.com/docs/reference/#api-Activities-getLoggedInAthleteActivities[athlete who is logged in^].
-That endpoint takes the following parameters:
-
-image::{img}api.png[role="popup-link"]
-
-We're interested in `per_page` (where we can define the number of activities returned per call to the endpoint) and `after` (where we can tell the API to only return results after a provided epoch timestamp).
-
-Let's imagine that we have more activities that we can return in one request to the API.
-We'll need to paginate to retrieve all our activities and import them into Neo4j.
-
-Before we paginate the API, let's first learn how to import one page worth of activities into Neo4j.
-The following query will return activities starting from the earliest timestamp:
-
-[source, cypher]
-----
-WITH 0 AS after
-
-WITH 'https://www.strava.com/api/v3/athlete/activities?after=' + after AS uri
-CALL apoc.load.jsonParams(uri, {Authorization: $stravaToken}, null)
-YIELD value
-
-CREATE (run:Run {id: value.id})
-SET run.distance = toFloat(value.distance),
-    run.startDate = datetime(value.start_date_local),
-    run.elapsedTime = duration({seconds: value.elapsed_time})
-----
-
-We create a node with the label `Run` for each activity and set a few properties, as well.
-The most interesting one for this example is `startDate` which we will pass to the `after` parameter later on.
-
-This query will load the first 30 activities, but what if we want to get the next 30?
-We can change the first line of the query to find the most recent timestamp of any of our `Run` nodes and then pass that to the API.
-If there aren't any `Run` nodes, then we can use a value of 0 like in the query below.
-
-[source, cypher]
-----
-OPTIONAL MATCH (run:Run)
-WITH run ORDER BY run.startDate DESC LIMIT 1
-WITH coalesce(run.startDate.epochSeconds, 0) AS after
-
-WITH 'https://www.strava.com/api/v3/athlete/activities?after=' + after AS uri
-CALL apoc.load.jsonParams(uri, {Authorization: $stravaToken}, null)
-YIELD value
-
-CREATE (run:Run {id: value.id})
-SET run.distance = toFloat(value.distance),
-    run.startDate = datetime(value.start_date_local),
-    run.elapsedTime = duration({seconds: value.elapsed_time})
-----
-
-We could continue to run this query manually, but it's about time that we automated it.
-
-[#auto-pagination]
-== Automated API pagination
-
-One way to do this is by using a scripting language and creating a loop inside which we make calls to that endpoint until we run out of activities to retrieve.
-If we're a bit creative, we can achieve the same outcome with the https://neo4j.com/docs/labs/apoc/current/graph-updates/periodic-execution/#periodic-commit[`apoc.periodic.commit`^] procedure.
-
-From the APOC documentation, this is the description of the periodic iterate procedure:
-
-****
-It is useful to run a query repeatedly in separate transactions until it doesn’t process and generates any results anymore.
-So you can iterate in batches over elements that don’t fulfil a condition and update them so that they do afterwards.
-****
-
-In our case, the exit condition will be when we receive less than 30 activities from the API.
-Let's first update our query to return a value of `0` if less than 30 activities are returned and the actual count if it's 30.
-
-[source, cypher]
-----
-OPTIONAL MATCH (run:Run)
-WITH run ORDER BY run.startDate DESC LIMIT 1
-WITH coalesce(run.startDate.epochSeconds, 0) AS after
-
-WITH 'https://www.strava.com/api/v3/athlete/activities?after=' + after AS uri
-CALL apoc.load.jsonParams(uri, {Authorization: $stravaToken}, null)
-YIELD value
-
-CREATE (run:Run {id: value.id})
-SET run.distance = toFloat(value.distance),
-    run.startDate = datetime(value.start_date_local),
-    run.elapsedTime = duration({seconds: value.elapsed_time})
-
-RETURN CASE WHEN count(*) < 30 THEN 0 ELSE count(*) END AS count
-----
-
-All that's left to do now is wrap the whole thing in periodic commit.
-We'll call `apoc.periodic.commit` method with two arguments: the first is the Cypher statement to run until the `RETURN` clause returns 0, and the second are parameters that are passed to the Cypher statement.
-
-[source, cypher]
-----
-call apoc.periodic.commit("
-  OPTIONAL MATCH (run:Run)
-  WITH run ORDER BY run.startDate DESC LIMIT 1
-  WITH coalesce(run.startDate.epochSeconds, 0) AS after
-
-  WITH 'https://www.strava.com/api/v3/athlete/activities?after=' + after AS uri
-  CALL apoc.load.jsonParams(uri, {Authorization: $stravaToken}, null)
-  YIELD value
-
-  CREATE (run:Run {id: value.id})
-  SET run.distance = toFloat(value.distance),
-      run.startDate = datetime(value.start_date_local),
-      run.elapsedTime = duration({seconds: value.elapsed_time})
-
-  RETURN CASE WHEN count(*) < 30 THEN 0 ELSE count(*) END AS count
-", {stravaToken: $stravaToken})
-----
-
-This query will now send multiple commits to the API until we have loaded all our activities.
-
-[#import-api-resources]
-== Resources
-* https://neo4j.com/docs/labs/apoc/current/import/load-json/#_load_json_stackoverflow_example[APOC Documentation: StackOverflow JSON Data Example^]
-* https://neo4j.com/docs/labs/apoc/current/import/load-json[APOC Documentation: Load JSON^]
diff --git a/modules/ROOT/pages/guide-importing-data-and-etl.adoc b/modules/ROOT/pages/guide-importing-data-and-etl.adoc
deleted file mode 100644
index ebf6e596..00000000
--- a/modules/ROOT/pages/guide-importing-data-and-etl.adoc
+++ /dev/null
@@ -1,850 +0,0 @@
-ifndef::env-guide[]
-= Tutorial: Import Relational Data Into Neo4j
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: import-export
-:tags: data-import, graph-import, northwind-graph, relational-graph, load-csv
-:description: This guide will teach you the process for exporting data from a relational database (PostgreSQL) and importing into a graph database (Neo4j). You will learn how to take data from the relational system and to the graph by translating the schema and using import tools.
-:page-pagination:
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/appendix/tutorials/guide-import-relational-and-etl/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-endif::[]
-ifdef::env-guide[]
-== Guide: Import Relational Data into Neo4j
-endif::[]
-ifndef::env-guide[]
-.Goals
-[abstract]
-{description}
-This Tutorial uses a specific data set, but the principles in this Tutorial can be applied and reused with any data domain.
-
-.Prerequisites
-[abstract]
-You should have a basic understanding of the link:/developer/graph-database/#property-graph[property graph model] and know how to link:/developer/guide-data-modeling[model data as a graph].
-
-:cta-header: This tutorial can be followed using an AuraDB Instance
-include::./_includes/aura_cta.adoc[]
-
-Alternatively, you can:
-
-* link:/sandbox/?ref=guide-importing-data-and-etl[Start a Blank Neo4j Sandbox^]
-* link:/download[download^] and install link:/developer/neo4j-desktop/[Neo4j Desktop].
-
-[role=expertise {level}]
-{level}
-endif::[]
-ifdef::env-guide[]
-
-This Guide uses a specific data set, but the principles in this Guide can be applied and reused with any data domain.
-
-You should have a basic understanding of the
-https://neo4j.com/developer/graph-database/#property-graph[property graph model^]
-and know how to
-https://neo4j.com/developer/guide-data-modeling[model data as a graph^].
-endif::[]
-
-[#about-domain]
-== About the Data Domain
-
-In this guide, we will be using the
-ifndef::env-guide[]
-link:{github}/data/northwind[NorthWind dataset^],
-endif::[]
-ifdef::env-guide[]
-https://github.com/neo4j-contrib/developer-resources/tree/gh-pages/data/northwind[NorthWind dataset^],
-endif::[]
-an often-used SQL dataset.
-This data depicts a product sale system - storing and tracking customers, products, customer orders, warehouse stock, shipping, suppliers, and even employees and their sales territories.
-Although the NorthWind dataset is often used to demonstrate SQL and relational databases, the data also can be structured as a graph.
-
-An entity-relationship diagram (ERD) of the Northwind dataset is shown below.
-
-image::{img}Northwind_diagram.jpg[width=600]
-
-First, this is a rather large and detailed model.
-We can scale this down a bit for our example and choose the entities that are most critical for our graph - in other words, those that might benefit most from seeing the connections.
-For our use case, we really want to optimize the relationships with orders - what products were involved (with the categories and suppliers for those products), which employees worked on them and those employees' managers.
-
-Using these business requirements, we can narrow our model down to these essential entities.
-
-image::{img}Northwind_diagram_focus.jpg[width=600]
-
-[#northwind-graph-model]
-== Developing a Graph Model
-
-The first thing you will need to do to get data from a relational database into a graph is to translate the relational data model to a graph data model.
-Determining how you want to structure tables and rows as nodes and relationships may vary depending on what is most important to your business needs.
-
-ifndef::env-guide[]
-[NOTE]
---
-For more information on adapting your graph model to different scenarios, check out our xref:modeling-designs.adoc[modeling designs] guide.
---
-endif::[]
-ifdef::env-guide[]
-[NOTE]
---
-For more information on adapting your graph model to different scenarios, check out our https://neo4j.com/developer/modeling-designs/[modeling designs^] guide.
---
-endif::[]
-When deriving a graph model from a relational model, you should keep a couple of general guidelines in mind.
-
-. A _row_ is a _node_.
-. A _table name_ is a _label name_.
-. A _join or foreign key_ is a _relationship_.
-
-With these principles in mind, we can map our relational model to a graph with the following steps:
-
-=== Rows to Nodes, Table names to labels
-
-. Each row on our `Orders` table becomes a node in our graph with `Order` as the label.
-. Each row on our `Products` table becomes a node with `Product` as the label.
-. Each row on our `Suppliers` table becomes a node with `Supplier` as the label.
-. Each row on our `Categories` table becomes a node with `Category` as the label.
-. Each row on our `Employees` table becomes a node with `Employee` as the label.
-
-=== Joins to relationships
-
-. Join between `Suppliers` and `Products` becomes a relationship named `SUPPLIES` (where supplier supplies product).
-. Join between `Products` and `Categories` becomes a relationship named `PART_OF` (where product is part of a category).
-. Join between `Employees` and `Orders` becomes a relationship named `SOLD` (where employee sold an order).
-. Join between `Employees` and itself (unary relationship) becomes a relationship named `REPORTS_TO` (where employees have a manager).
-. Join with join table (`Order Details`) between `Orders` and `Products` becomes a relationship named `CONTAINS` with properties of `unitPrice`, `quantity`, and `discount` (where order contains a product).
-
-If we draw our translation out on the whiteboard, we have this graph data model.
-
-image::{img}northwind_graph_simple.svg[width=600]
-
-Now, we can, of course, decide that we want to include the rest of the entities from our relational model, but for now, we will keep to this smaller graph model.
-
-=== How does the Graph Model Differ from the Relational Model?
-
-* There are no nulls. Non-existing value entries (properties) are just not present.
-* It describes the relationships in more detail. For example, we know that an employee SOLD an order rather than having a foreign key relationship between the Orders and Employees tables. We could also choose to add more metadata about that relationship, should we wish.
-* Either model can be more normalized. For example, addresses have been denormalized in several of the tables, but could have been in a separate table. In a future version of our graph model, we might also choose to separate addresses from the `Order` (or `Supplier` or `Employee`) entities and create separate `Address` nodes.
-
-[#export-csv]
-== Exporting Relational Tables to CSV
-
-Thankfully, this step has already been done for you with the Northwind data you will use later on in this Guide.
-
-However, if you are working with another data domain, you will need to take the data from the relational tables and put it in another format for loading to the graph.
-A common format that many systems can handle a flat file of comma-separated values (CSV).
-
-Here is an example script we already ran to export the northwind data into CSV files for you.
-
-ifndef::env-guide[]
-.export_csv.sql
-endif::[]
-----
-include::example$export_csv.sql[]
-----
-
-If you want to create the CSV files yourself using your own northwind RDBMS, you can run this script against your  RDBMS with the command `psql -d northwind < export_csv.sql`.
-
-*Note*: You need not run this script unless you want to execute it against your own northwind RDBMS.
-
-[#import-with-cypher]
-== Importing the Data using Cypher
-
-You will use Cypher's
-ifndef::env-guide[]
-{cyphermanual}/clauses/load-csv/[LOAD CSV^]
-endif::[]
-ifdef::env-guide[]
-https://neo4j.com/docs/cypher-manual/current/clauses/load-csv/[LOAD CSV^]
-endif::[]
-command to transform the contents of the CSV file into a graph structure.
-
-ifndef::env-guide[]
-When you use `LOAD CSV` to create nodes and relationships in the database, you have two options for where the CSV files reside:
-
-* In the *import* folder for the Neo4j instance that you can manage.
-* From a publicly-available location such as an S3 bucket or a github location. You must use this option if you are using Neo4j AuraDB or Neo4j Sandbox.
-
-If you want to use the CSV files for your Neo4j instance that you manage, you can copy the CSV files from
-link:{github}/data/northwind//northwind.zip[northwind zip from github]
-and place them in the *import* folder for your Neo4j DBMS.
-endif::[]
-
-We have already placed these CSV files in Gihub for your access to them.
-
-You use use Cypher's `LOAD CSV` statement to read each file and add Cypher clauses after it to take the row/column data and transform it to the graph.
-
-Next you will execute Cypher code to:
-
-. Load the nodes from the CSV files.
-. Create the indexes and constraint for the data in the graph.
-. Create the relationships between the nodes.
-
-ifndef::env-guide[]
-=== Creating Order nodes
-endif::[]
-ifdef::env-guide[]
-== Creating Order nodes
-endif::[]
-
-Execute this Cypher block to create the Order nodes in the database:
-
-[source, cypher]
-----
-// Create orders
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/orders.csv' AS row
-MERGE (order:Order {orderID: row.OrderID})
-  ON CREATE SET order.shipName = row.ShipName;
-----
-
-ifndef::env-guide[]
-If you have placed the CSV files in to the *import* folder, you should use this code syntax to load the CSV files from a local directory:
-
-
-----
-// Create orders
-LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row
-MERGE (order:Order {orderID: row.OrderID})
-  ON CREATE SET order.shipName = row.ShipName;
-----
-endif::[]
-
-This code creates 830 Order nodes in the database.
-
-You can view some of the nodes in the database by executing this code:
-
-[source, cypher]
-----
-MATCH (o:Order) return o LIMIT 5;
-----
-
-The graph view is:
-
-image::{img}import-guide-Orders.svg[width=300]
-
-The table view contains these values for the node properties:
-
-[format="csv", options="header"]
-|===
-o
-"{""shipName"":Vins et alcools Chevalier,""orderID"":10248}"
-"{""shipName"":Toms Spezialitäten,""orderID"":10249}"
-"{""shipName"":Hanari Carnes,""orderID"":10250}"
-"{""shipName"":Victuailles en stock,""orderID"":10251}"
-"{""shipName"":Suprêmes délices,""orderID"":10252}"
-|===
-
-You might notice that you have not imported all of the field columns in the CSV file.
-With your statements, you can choose which properties are needed on a node, which can be left out, and which might need imported to another node type or relationship.
-You might also notice that you used the
-ifndef::env-guide[]
-{cyphermanual}/clauses/merge/[`MERGE` keyword^],
-instead of
-{cyphermanual}/clauses/create/[`CREATE`^].
-endif::[]
-ifdef::env-guide[]
-https://neo4j.com/docs/cypher-manual/current/clauses/merge/[`MERGE` keyword^],
-instead of
-https://neo4j.com/docs/cypher-manual/current/clauses/create/[`CREATE`^].
-endif::[]
-Though we feel pretty confident there are no duplicates in our CSV files, we can use `MERGE` as good practice for ensuring unique entities in our database.
-
-ifndef::env-guide[]
-=== Creating Product nodes
-endif::[]
-ifdef::env-guide[]
-== Creating Product nodes
-endif::[]
-
-Execute this code to create the Product nodes in the database:
-
-[source, cypher]
-----
-// Create products
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/products.csv' AS row
-MERGE (product:Product {productID: row.ProductID})
-  ON CREATE SET product.productName = row.ProductName, product.unitPrice = toFloat(row.UnitPrice);
-----
-
-This code creates 77 Product nodes in the database.
-
-You can view some of these nodes in the database by executing this code:
-
-[source, cypher]
-----
-MATCH (p:Product) return p LIMIT 5;
-----
-
-The graph view is:
-
-image::{img}import-guide-Products.svg[width=300]
-
-The table view contains these values for the node properties:
-
-[format="csv", options="header"]
-|===
-p
-"{""unitPrice"":18.0,""productID"":1,""productName"":Chai}"
-"{""unitPrice"":19.0,""productID"":2,""productName"":Chang}"
-"{""unitPrice"":10.0,""productID"":3,""productName"":Aniseed Syrup}"
-"{""unitPrice"":22.0,""productID"":4,""productName"":Chef Anton's Cajun Seasoning}"
-"{""unitPrice"":21.35,""productID"":5,""productName"":Chef Anton's Gumbo Mix}"
-|===
-
-ifndef::env-guide[]
-=== Creating Supplier nodes
-endif::[]
-ifdef::env-guide[]
-== Creating Supplier nodes
-endif::[]
-
-Execute this code to create the Supplier nodes in the database:
-[source, cypher]
-----
-// Create suppliers
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/suppliers.csv' AS row
-MERGE (supplier:Supplier {supplierID: row.SupplierID})
-  ON CREATE SET supplier.companyName = row.CompanyName;
-----
-
-This code creates 29 Supplier nodes in the database.
-
-You can view some of these nodes in the database by executing this code:
-
-[source, cypher]
-----
-MATCH (s:Supplier) return s LIMIT 5;
-----
-
-The graph view is:
-
-image::{img}import-guide-Suppliers.svg[width=300]
-
-The table view contains these values for the node properties:
-
-[format="csv", options="header"]
-|===
-s
-"{""supplierID"":1,""companyName"":Exotic Liquids}"
-"{""supplierID"":2,""companyName"":New Orleans Cajun Delights}"
-"{""supplierID"":3,""companyName"":Grandma Kelly's Homestead}"
-"{""supplierID"":4,""companyName"":Tokyo Traders}"
-"{""supplierID"":5,""companyName"":Cooperativa de Quesos 'Las Cabras'}"
-|===
-
-ifndef::env-guide[]
-=== Creating Employee nodes
-endif::[]
-ifdef::env-guide[]
-== Creating Employee nodes
-endif::[]
-
-Execute this code to create the Supplier nodes in the database:
-
-[source, cypher]
-----
-// Create employees
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/employees.csv' AS row
-MERGE (e:Employee {employeeID:row.EmployeeID})
-  ON CREATE SET e.firstName = row.FirstName, e.lastName = row.LastName, e.title = row.Title;
-----
-
-This code creates 9 Employee nodes in the database.
-
-You can view some of these nodes in the database by executing this code:
-
-[source, cypher]
-----
-MATCH (e:Employee) return e LIMIT 5;
-----
-
-The graph view is:
-
-image::{img}import-guide-Employees.svg[width=300]
-The table view contains these values for the node properties:
-
-[format="csv", options="header"]
-|===
-e
-"{""lastName"":Davolio,""firstName"":Nancy,""employeeID"":1,""title"":Sales Representative}"
-"{""lastName"":Fuller,""firstName"":Andrew,""employeeID"":2,""title"":Vice President, Sales}"
-"{""lastName"":Leverling,""firstName"":Janet,""employeeID"":3,""title"":Sales Representative}"
-"{""lastName"":Peacock,""firstName"":Margaret,""employeeID"":4,""title"":Sales Representative}"
-"{""lastName"":Buchanan,""firstName"":Steven,""employeeID"":5,""title"":Sales Manager}"
-|===
-
-ifndef::env-guide[]
-=== Creating Category nodes
-endif::[]
-ifdef::env-guide[]
-== Creating Category nodes
-endif::[]
-
-[source, cypher]
-----
-// Create categories
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/categories.csv' AS row
-MERGE (c:Category {categoryID: row.CategoryID})
-  ON CREATE SET c.categoryName = row.CategoryName, c.description = row.Description;
-----
-
-This code creates 8 Category nodes in the database.
-
-You can view some of these nodes in the database by executing this code:
-
-[source, cypher]
-----
-MATCH (c:Category) return c LIMIT 5;
-----
-
-The graph view is:
-
-image::{img}import-guide-Categories.svg[width=300]
-
-The table view contains these values for the node properties:
-
-[format="csv", options="header"]
-|===
-c
-"{""description"":Soft drinks, coffees, teas, beers, and ales,""categoryName"":Beverages,""categoryID"":1}"
-"{""description"":Sweet and savory sauces, relishes, spreads, and seasonings,""categoryName"":Condiments,""categoryID"":2}"
-"{""description"":Desserts, candies, and sweet breads,""categoryName"":Confections,""categoryID"":3}"
-"{""description"":Cheeses,""categoryName"":Dairy Products,""categoryID"":4}"
-"{""description"":Breads, crackers, pasta, and cereal,""categoryName"":Grains/Cereals,""categoryID"":5}"
-|===
-
-ifdef::env-guide[]
-[NOTE]
---
-For very large commercial or enterprise datasets, you may find out-of-memory errors, especially on smaller machines.
-To avoid these situations, you can prefix the statement with the `:auto USING PERIODIC COMMIT` query hint to commit data in batches.
-This practice is not standard recommendation for smaller datasets, but is only recommended when memory issues are threatened.
-More information on this query hint can be found in the https://neo4j.com/docs/cypher-manual/current/query-tuning/using/#query-using-periodic-commit-hint[manual^].
---
-endif::[]
-
-ifndef::env-guide[]
-[NOTE]
---
-For very large commercial or enterprise datasets, you may find out-of-memory errors, especially on smaller machines.
-To avoid these situations, you can prefix the statement with the `:auto USING PERIODIC COMMIT` query hint to commit data in batches.
-This practice is not standard recommendation for smaller datasets, but is only recommended when memory issues are threatened.
-More information on this query hint can be found in the {cyphermanual}/query-tuning/using/#query-using-periodic-commit-hint[manual^].
---
-endif::[]
-
-== Creating the indexes and constraints for the data in the graph
-
-After the nodes are created, you need to create the relationships between them.
-Importing the relationships will mean looking up the nodes you just created and adding a relationship between those existing entities.
-To ensure the lookup of nodes is optimized, you will create indexes for any node properties used in the lookups (often the id or another unique value).
-
-We also want to create a constraint (also creates an index with it) that will disallow orders with the same id from getting created, preventing duplicates.
-Finally, as the indexes are created after the nodes are inserted, their population happens asynchronously, so we call `db.awaitIndexes()` to block until they are populated.
-
-Execute this code block:
-
-[source, cypher]
-----
-CREATE INDEX product_id FOR (p:Product) ON (p.productID);
-CREATE INDEX product_name FOR (p:Product) ON (p.productName);
-CREATE INDEX supplier_id FOR (s:Supplier) ON (s.supplierID);
-CREATE INDEX employee_id FOR (e:Employee) ON (e.employeeID);
-CREATE INDEX category_id FOR (c:Category) ON (c.categoryID);
-CREATE CONSTRAINT order_id ON (o:Order) ASSERT o.orderID IS UNIQUE;
-CALL db.awaitIndexes();
-----
-
-After you execute this code, you can execute this code to view the indexes (and constraint) in the database:
-
-[source, cypher]
-----
-CALL db.indexes();
-----
-
-You should see these indexes (and constraint) in the database:
-
-[format="csv", options="header"]
-|===
-id,name,state,populationPercent,uniqueness,type,entityType,labelsOrTypes,properties,provider
-5,category_id,ONLINE,100.0,NONUNIQUE,BTREE,NODE,[Category],[categoryID],native-btree-1.0
-4,employee_id,ONLINE,100.0,NONUNIQUE,BTREE,NODE,[Employee],[employeeID],native-btree-1.0
-6,order_id,ONLINE,100.0,UNIQUE,BTREE,NODE,[Order],[orderID],native-btree-1.0
-1,product_id,ONLINE,100.0,NONUNIQUE,BTREE,NODE,[Product],[productID],native-btree-1.0
-2,product_name,ONLINE,100.0,NONUNIQUE,BTREE,NODE,[Product],[productName],native-btree-1.0
-3,supplier_id,ONLINE,100.0,NONUNIQUE,BTREE,NODE,[Supplier],[supplierID],native-btree-1.0
-|===
-
-
-== Creating the relationships between the nodes
-
-Next you will create relationships:
-
-. Between orders and  employees.
-. Between products and suppliers and between products and categories.
-. Between employees.
-
-ifndef::env-guide[]
-=== Creating relationships between orders and employees
-endif::[]
-ifdef::env-guide[]
-== Creating relationships between orders and employees
-endif::[]
-
-With the initial nodes and indexes in place, you can now create the relationships for orders to products and orders to employees.
-
-Execute this code block:
-
-[source, cypher]
-----
-// Create relationships between orders and products
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/orders.csv' AS row
-MATCH (order:Order {orderID: row.OrderID})
-MATCH (product:Product {productID: row.ProductID})
-MERGE (order)-[op:CONTAINS]->(product)
-  ON CREATE SET op.unitPrice = toFloat(row.UnitPrice), op.quantity = toFloat(row.Quantity);
-----
-
-This code creates 2155 relationships in the graph.
-
-You can view some of them by executing this code:
-
-[source, cypher]
-----
-MATCH (o:Order)-[]-(p:Product)
-RETURN o,p LIMIT 10;
-----
-
-Your graph view should look something like this:
-
-image::{img}import-guide-CONTAINS.svg[width=400]
-
-Then, execute this code block:
-
-[source, cypher]
-----
-// Create relationships between orders and employees
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/orders.csv' AS row
-MATCH (order:Order {orderID: row.OrderID})
-MATCH (employee:Employee {employeeID: row.EmployeeID})
-MERGE (employee)-[:SOLD]->(order);
-----
-
-This code creates 830 relationships in the graph.
-
-You can view some of them by executing this code:
-
-[source, cypher]
-----
-MATCH (o:Order)-[]-(e:Employee)
-RETURN o,e LIMIT 10;
-----
-
-Your graph view should look something like this:
-
-image::{img}import-guide-SOLD.svg[width=400]
-
-ifndef::env-guide[]
-=== Creating relationships between products and suppliers and between products and categories
-endif::[]
-ifdef::env-guide[]
-== Creating relationships between products and suppliers and between products and categories
-endif::[]
-
-Next, create relationships between products, suppliers, and categories:
-
-Execute this code block:
-
-[source, cypher]
-----
-// Create relationships between products and suppliers
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/products.csv
-' AS row
-MATCH (product:Product {productID: row.ProductID})
-MATCH (supplier:Supplier {supplierID: row.SupplierID})
-MERGE (supplier)-[:SUPPLIES]->(product);
-----
-
-This code creates 77 relationships in the graph.
-
-You can view some of them by executing this code:
-
-[source, cypher]
-----
-MATCH (s:Supplier)-[]-(p:Product)
-RETURN s,p LIMIT 10;
-----
-
-Your graph view should look something like this:
-
-image::{img}import-guide-SUPPLIES.svg[width=400]
-
-Then, execute this code block:
-
-[source, cypher]
-----
-// Create relationships between products and categories
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/products.csv
-' AS row
-MATCH (product:Product {productID: row.ProductID})
-MATCH (category:Category {categoryID: row.CategoryID})
-MERGE (product)-[:PART_OF]->(category);
-----
-
-This code creates 77 relationships in the graph.
-
-You can view some of them by executing this code:
-
-[source, cypher]
-----
-MATCH (c:Category)-[]-(p:Product)
-RETURN c,p LIMIT 10;
-----
-
-Your graph view should look something like this:
-
-image::{img}import-guide-PART_OF.svg[width=400]
-
-ifndef::env-guide[]
-=== Creating relationships between employees
-endif::[]
-ifdef::env-guide[]
-== Creating relationships between employees
-endif::[]
-
-Lastly, you will create the 'REPORTS_TO' relationship between employees to represent the reporting structure:
-
-Execute this code block:
-
-[source, cypher]
-----
-// Create relationships between employees (reporting hierarchy)
-LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/employees.csv' AS row
-MATCH (employee:Employee {employeeID: row.EmployeeID})
-MATCH (manager:Employee {employeeID: row.ReportsTo})
-MERGE (employee)-[:REPORTS_TO]->(manager);
-----
-
-This code creates 8 relationships in the graph.
-
-You can view some of them by executing this code:
-
-[source, cypher]
-----
-MATCH (e1:Employee)-[]-(e2:Employee)
-RETURN e1,e2 LIMIT 10;
-----
-
-Your graph view should look something like this:
-
-image::{img}import-guide-REPORTS_TO.svg[width=400]
-
-Next, you will query the resulting graph to find out what it can tell us about our newly-imported data.
-
-[#query-northwind]
-== Querying the Graph
-
-We might start with a couple of general queries to verify that our data matches the model we designed earlier in the guide.
-Here are some example queries.
-
-Execute this code block:
-
-[source,cypher]
-----
-//find a sample of employees who sold orders with their ordered products
-MATCH (e:Employee)-[rel:SOLD]->(o:Order)-[rel2:CONTAINS]->(p:Product)
-RETURN e, rel, o, rel2, p LIMIT 25;
-----
-
-Execute this code block:
-
-[source, cypher]
-----
-//find the supplier and category for a specific product
-MATCH (s:Supplier)-[r1:SUPPLIES]->(p:Product {productName: 'Chocolade'})-[r2:PART_OF]->(c:Category)
-RETURN s, r1, p, r2, c;
-----
-
-Once you are comfortable that the data aligns with our data model and everything looks correct, you can start querying to gather information and insight for business decisions.
-
-ifndef::env-guide[]
-=== Which Employee had the Highest Cross-Selling Count of 'Chocolade' and Another Product?
-endif::[]
-ifdef::env-guide[]
-== Which Employee had the Highest Cross-Selling Count of 'Chocolade' and Another Product?
-endif::[]
-
-Execute this code block:
-
-[source, cypher]
-----
-MATCH (choc:Product {productName:'Chocolade'})<-[:CONTAINS]-(:Order)<-[:SOLD]-(employee),
-      (employee)-[:SOLD]->(o2)-[:CONTAINS]->(other:Product)
-RETURN employee.employeeID as employee, other.productName as otherProduct, count(distinct o2) as count
-ORDER BY count DESC
-LIMIT 5;
-----
-
-Looks like employee No. 4 was busy, though employee No. 1 also did well!
-Your results should look something like this:
-
-[format="csv", options="header"]
-|===
-employee,otherProduct,count
-4,Gnocchi di nonna Alice,14
-4,Pâté chinois,12
-1,Flotemysost,12
-3,Gumbär Gummibärchen,12
-1,Pavlova,11
-|===
-
-ifndef::env-guide[]
-=== How are Employees Organized? Who Reports to Whom?
-endif::[]
-ifdef::env-guide[]
-== How are Employees Organized? Who Reports to Whom?
-endif::[]
-
-Execute this code block:
-
-[source, cypher]
-----
-MATCH (e:Employee)<-[:REPORTS_TO]-(sub)
-RETURN e.employeeID AS manager, sub.employeeID AS employee;
-----
-
-Your results should look something like this:
-
-[format="csv", options="header"]
-|===
-manager,employee
-2,3
-2,4
-2,5
-2,1
-2,8
-5,9
-5,7
-5,6
-|===
-
-Notice that employee No. 5 has people reporting to them but also reports to employee No. 2.
-
-Next, let's investigate that a bit more.
-
-ifndef::env-guide[]
-=== Which Employees Report to Each Other Indirectly?
-endif::[]
-ifdef::env-guide[]
-== Which Employees Report to Each Other Indirectly?
-endif::[]
-
-Execute this code block:
-
-[source, cypher]
-----
-MATCH path = (e:Employee)<-[:REPORTS_TO*]-(sub)
-WITH e, sub, [person in NODES(path) | person.employeeID][1..-1] AS path
-RETURN e.employeeID AS manager, path as middleManager, sub.employeeID AS employee
-ORDER BY size(path);
-----
-
-Your results should look something like this:
-
-[format="dsv", options="header"]
-|===
-manager:middleManager:employee
-2:[]:3
-2:[]:4
-2:[]:5
-2:[]:1
-2:[]:8
-5:[]:9
-5:[]:7
-5:[]:6
-2:[5]:9
-2:[5]:7
-2:[5]:6
-|===
-
-ifndef::env-guide[]
-=== How Many Orders were Made by Each Part of the Hierarchy?
-endif::[]
-ifdef::env-guide[]
-== How Many Orders were Made by Each Part of the Hierarchy?
-endif::[]
-
-Execute this code block:
-
-[source, cypher]
-----
-MATCH (e:Employee)
-OPTIONAL MATCH (e)<-[:REPORTS_TO*0..]-(sub)-[:SOLD]->(order)
-RETURN e.employeeID as employee, [x IN COLLECT(DISTINCT sub.employeeID) WHERE x <> e.employeeID] AS reportsTo, COUNT(distinct order) AS totalOrders
-ORDER BY totalOrders DESC;
-----
-
-Your results should look something like this:
-
-[format="dsv", options="header"]
-|===
-employee:reportsTo:totalOrders
-2:[8,1,5,6,7,9,4,3]:830
-5:[6,7,9]:224
-4:[]:156
-3:[]:127
-1:[]:123
-8:[]:104
-7:[]:72
-6:[]:67
-9:[]:43
-|===
-
-
-== What's Next?
-
-If you followed along with each step through this guide, then you might want to explore the data set with more queries and try to answer additional questions you came up with for the data.
-You may also want to apply these same principles to your own or another data set for analysis.
-
-If you used this as a process flow to apply to a different data set or you would like to do that next, feel free to start at the top and work through this guide again with another domain.
-The steps and processes still apply (though, of course, the data model, queries, and business questions will need adjusted).
-
-If you have data that needs additional cleansing and manipulation than what is covered in this guide, the
-ifndef::env-guide[]
-link:/labs/apoc/[APOC library^]
-endif::[]
-ifdef::env-guide[]
-https://neo4j.com/labs/apoc/[APOC library^]
-endif::[]
-may be able to help.
-It contains hundreds of procedures and functions for handling large amounts of data, translating values, cleaning messy data sources, and more!
-
-If you are interested in doing a one-time initial dump of relational data to Neo4j, then the
-ifndef::env-guide[]
-link:/labs/etl-tool/[Neo4j ETL Tool^]
-endif::[]
-ifdef::env-guide[]
-https://neo4j.com/labs/etl-tool/[Neo4j ETL Tool^]
-endif::[]
-might be what you are looking for.
-The application is designed with a point-and-click user interface with the goal of fast, simple relational-to-graph loads that help new and existing users gain faster value from seeing their data as a graph without Cypher, import procedures, or other code.
-
-[#import-northwind-resources]
-== Resources
-ifndef::env-guide[]
-* link:{github}/data/northwind[Northwind SQL, CSV and Cypher data files^], also as link:{github}/data/northwind/northwind.zip[zip^] file
-* {cyphermanual}/clauses/load-csv/[LOAD CSV^]: Cypher's command for importing CSV files
-* link:/labs/apoc/[APOC library^]: Neo4j's utility library
-* link:/labs/etl-tool/[Neo4j ETL Tool^]: Loading relational data without code
-* xref:data-import.adoc[Importing Data with Neo4j]
-* xref:guide-data-modeling.adoc[Graph Data Modeling]
-endif::[]
-ifdef::env-guide[]
-* https://github.com/neo4j-contrib/developer-resources/tree/gh-pages/data/northwind[Northwind SQL, CSV and Cypher data files^], also as https://github.com/neo4j-contrib/developer-resources/tree/gh-pages/data/northwind/northwind.zip[zip^] file
-* https://neo4j.com/docs/cypher-manual/current/clauses/load-csv/[LOAD CSV^]: Cypher's command for importing CSV files
-* https://neo4j.com/labs/apoc/[APOC library^]: Neo4j's utility library
-* https://neo4j.com/labs/etl-tool//[Neo4j ETL Tool^]: Loading relational data without code
-* https://neo4j.com/developer/data-import/[Importing Data with Neo4j^]
-* https://neo4j.com/developer/data-modeling/guide-data-modeling[Graph Data Modeling^]
-endif::[]
diff --git a/modules/ROOT/pages/guide-orchestration.adoc b/modules/ROOT/pages/guide-orchestration.adoc
deleted file mode 100644
index ab29367e..00000000
--- a/modules/ROOT/pages/guide-orchestration.adoc
+++ /dev/null
@@ -1,65 +0,0 @@
-= Neo4j on Orchestration Tools
-:level: Intermediate
-:page-level: Intermediate
-:author: David Allen
-:category: cloud
-:tags: cloud, orchestration, mesosphere, kubernetes, deployment
-:description: This guide explains how to run Neo4j on orchestration frameworks such as Mesosphere DC/OS and Kubernetes.
-:page-deprecated-title: the Neo4j Operations Manual
-:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/kubernetes/
-
-// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with the orchestration framework on which you want to deploy.
-
-[#neo4j-orch]
-Orchestration systems are systems for automating the deployment, scaling, and management of containerized applications.
-You can follow the guides below to run Neo4j on some popular orchestration systems.
-
-[#mesospehere]
-== Mesosphere DC/OS
-
-https://docs.mesosphere.com/[Mesosphere DC/OS^] is built around the long established link:http://mesos.apache.org/[Apache Mesos^] project.
-Mesos is used by companies such as Twitter, Apple, Netflix, or Airbnb to manage their production clusters.
-
-Neo4j is https://github.com/neo4j-contrib/neo4j-dcos[available as a Universe application^] and can be installed via the https://mesosphere.github.io/marathon/[Marathon container orchestration platform^].
-
-You can read more in the link:/blog/neo4j-mesosphere-dc-os/[release blog post^] or watch the presentation (below) that Johannes Unterstein gave at the link:/developer/online-meetup/[Neo4j online meetup^].
-
-++++
-
    
-
-
    
-++++
-
-[#kubernetes]
-== Kubernetes
-
-Kubernetes is an open source system for automating deployment, scaling, and management of containerized applications that builds on 15 years of experience of running production workloads at Google.
-
-Neo4j core servers can be installed using the StatefulSet abstraction, which was introduced as a beta feature in v1.5.0.
-Read replicas can be installed using the deployment abstraction.
-
-For users who wish to run Neo4j in kubernetes clusters, Neo4j provides a https://github.com/neo4j-contrib/neo4j-helm[helm chart^], which permits easy installation using standard `kubectl` and `helm` tools.
-These kubernetes tools and approaches all take advantage of the standard Neo4j docker container mentioned above, but add clustering and coordination capabilities.
-
-For a walkthrough of how to use the helm chart to get started quickly, please consult our link:/blog/kubernetes-deploy-neo4j-clusters/[blog post^].
-
-You can also watch https://www.youtube.com/watch?v=GSfam8De7K0[Dippy Aggarawal's GraphConnect^] talk on orchestration systems.
-
-++++
-
    
-
-
    
-++++
-
-[#orchestration-resources]
-== Questions?
-
-You can ask questions and connect with other people launching Neo4j in the cloud through the https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^].
\ No newline at end of file
diff --git a/modules/ROOT/pages/guide-performance-tuning.adoc b/modules/ROOT/pages/guide-performance-tuning.adoc
deleted file mode 100644
index a62e0c7e..00000000
--- a/modules/ROOT/pages/guide-performance-tuning.adoc
+++ /dev/null
@@ -1,90 +0,0 @@
-= Neo4j Performance Tuning
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:category: performance
-:tags: administration, tuning, memory, page-cache, heap, transaction, log
-:description: This guide gives an overview on memory configuration, logical log configuration and Linux open files configuration.
-:page-deprecated-title: the Neo4j Operations Manual
-:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/performance/
-
-// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should know how to link:/download[download] and install Neo4j on your system.
-If you are a developer you should be accustomed to the graph data model and have written your Neo4j application.
-
-[role=expertise {level}]
-{level}
-
-[#memory-config]
-== Memory Configuration guidelines
-
-An in-depth description on this topic is available in the link:{opsmanual}/performance/#memory-tuning[Neo4j Operations Manual].
-
-There are three types of memory to consider: _OS Memory_, _Page Cache_ and _Heap Space_.
-
-=== OS Memory Sizing
-
-Some memory must be reserved for all activities on the server that are *not* Neo4j related.
-One Gigabyte `1G` is good enough for most cases when Neo4j is the only server running on that machine.
-
-Please make sure that the configured memory doesn't exceed available RAM, otherwise the OS starts to swap to disk which heavily affects operation performance.
-
-=== Page Cache Sizing
-
-image::{img}neo4j_memory_usage.jpg[width=300,float=right]
-
-The page cache is used to cache the Neo4j data as stored on disk.
-Ensuring that most of the graph data from disk is cached in memory will help avoid costly disk access.
-You can determine the total memory needed by summing up the sizes of the `$NEO4J_HOME/data/graph.db/neostore.\*.db` files and adding e.g. 20% for growth.
-
-To estimate the necessary page-cache size for a dataset to be imported, it is useful to run an import with a fraction (e.g. 1/100th) of the data and then multiply the resulting store-size by that fraction (x 100).
-
-The size of the page cache, e.g. `5G` is defined by the parameter `dbms.memory.pagecache.size` in the file
-`$NEO4J_HOME/conf/neo4j.conf`.  By default, if not configured, Neo4j will use 50% of the available RAM in the system for its page cache.
-
-=== Heap Sizing
-
-The size of the available heap memory is an important aspect for the performance of Neo4j.
-
-Generally speaking, it is beneficial to configure a large enough heap space to sustain concurrent operations.
-For many setups, a heap size between 8G and 16G is large enough to run Neo4j reliably.
-
-The heap memory size is determined by the parameters in `$NEO4J_HOME/conf/neo4j.conf`, namely `dbms.memory.heap.initial_size` and `dbms.memory.heap.max_size` providing the heap size as a number and a unit, for example `16G`.
-It is recommended to set these two parameters to the same size.
-
-For a more thorough discussion on this topic, refer to the link:{opsmanual}/performance/memory-configuration/#heap-sizing[heap memory configuration section in the Neo4j Operations Manual].
-That section also contains information about heap memory distribution and gabarge collection tuning.
-
-[#logical-logs]
-== Logical Logs
-
-Logical transaction logs in Neo4j are used in scenarios when the database needs to be recovered after a unclean shutdown.
-They are also used for online backup operations, especially for incremental backups.
-
-These transaction log files are rotated after surpassing a certain size (25 Mb in size).
-The amount of log files or the used space can be configured.
-
-It is recommended that the `dbms.tx_log.rotation.retention_policy` parameter is set to `7 days`
-
-[#open-files]
-== Number of Open Files
-
-The usual default of `1024` for the open file limit is often not enough, especially when many indexes are used or a server installation sees too many connections (network sockets also count against that limit).
-Users are therefore encouraged to increase that limit to a realistic value of `40000` or more, depending on usage patterns.
-Setting this value via the `ulimit` command is only possible for the current session.
-
-To set the value system wide you have to follow the link:{opsmanual}/installation/linux/tarball/#linux-open-files[instructions^] for your platform.
-
-[#tuning-resources]
-== Resources
-* link:{opsmanual}[Operations,role=manual]
-* link:{opsmanual}/performance/[Configuration & Performance,role=manual]
-// * http://maxdemarzi.com/2013/11/25/scaling-up/[Scaling Up Neo4j,role=blog]
-* link:/support[Neo4j Professional Support]
diff --git a/modules/ROOT/pages/guide-sizing-and-hardware-calculator.adoc b/modules/ROOT/pages/guide-sizing-and-hardware-calculator.adoc
deleted file mode 100644
index ada9504f..00000000
--- a/modules/ROOT/pages/guide-sizing-and-hardware-calculator.adoc
+++ /dev/null
@@ -1,87 +0,0 @@
-= Neo4j's Hardware Requirements
-:level: Advanced
-:page-level: Advanced
-:author: Neo4j
-:category: performance
-:tags: administration, sizing, hardware, calculator
-:description: This guide explains Neo4j sizing and hardware calculation tips and best practices.  It details what you need to make Neo4j fly.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should know how to link:/download[download] and install Neo4j on your system.
-You should be accustomed to the graph data model and have written your Neo4j application.
-You should know that Neo4j employs its own persistence and caching implementation, have seen the files on disk (`data/graph.db`), and have glanced at `messages.log`.
-
-[role=expertise {level}]
-{level}
-
-[#neo4j-architecture]
-== Neo4j's Architecture
-
-Neo4j is a native graph database; it handles the graph data records down to the disk/filesystem level.
-On top of the memory mapped filesystem, Neo4j employs additional caches for loaded Node and Relationship-Records.
-
-[#disk-storage]
-== Disk Storage
-
-Neo4j's storage is organized in record-based files per data structure - nodes, relationships, properties, labels, and so on.
-Each node and relationship record block is directly addressable by its id.
-
-The blocks have different sizes to accommodate the different type of data they contain:
-
-* Node records: labels, pointer to first relationship, pointer to first property block.
-* Relationship records: relationship-type, pointers to: start-node, end-node, first property block, next-relationship and previous relationship for both start and end-nodes
-* Property records: up to four property entries, each with a property-key id, and an encoded / compressed property value or pointer to string or array storage list
-
-Nodes occupy 15B of space, relationships occupy 31B of space and properties occupy 41B of space.
-
-An example disk space calculation is:
-
-----
-10,000    Nodes x 14B = 140kB
-1,000,000 Rels  x 33B = 31.5MB
-2,010,000 Props x 41B = 78.6MB
-
-Total  is 110.2MB
-----
-
-[#cache-config]
-== Low Level Cache
-
-The low level cache requirements are the same as for the disk space.
-Its configuration (memory mapped regions per file), is done in `conf/neo4j.properties`.
-You should try to memory-map as much data as your RAM allows (allow for 6 to 16GB heap depending on availability and use-cases).
-
-[#optimize-perf]
-== Optimizing
-
-In optimizing a graph database solution for performance, we should bear the following guidelines in mind:
-
-* Utilize the filesystem cache as much as possible, and map store files in their entirety into this cache
-* Have as much RAM available as possible
-* Tune the JVM heap and keep an eye on GC behaviors
-* Consider using fast disks—SSDs or enterprise flash hardware to bring up the bottom line when disk access becomes inevitable
-
-[#performance-opt]
-== Performance Optimization
-There are three areas in which we can optimize for performance:
-
-* Keep the object cache to a reasonable size (2 to 16 GB, about 50% of the heap)
-* Increase the file cache (from 2 GB all the way up to 200 GB or more in exceptional circumstances)
-* Increase the percentage of the store mapped into the filesystem cache
-* Invest in faster disks: SSDs or enterprise flash hardware
-
-[#hardware-sizing]
-== Hardware sizing calculator
-
-++++
-
    
-
     
    
-
     Hardware Sizing Calculator → 
    
-
     
    
-
    
-++++
diff --git a/modules/ROOT/pages/haskell.adoc b/modules/ROOT/pages/haskell.adoc
deleted file mode 100644
index 6d5b307b..00000000
--- a/modules/ROOT/pages/haskell.adoc
+++ /dev/null
@@ -1,51 +0,0 @@
-= Haskell: Neo4j Community Driver
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: haskell
-:category: drivers
-:tags: haskell, app-development, applications
-:description: If you are a Haskell developer, this guide provides an overview of options for connecting from Haskell to Neo4j.
-
-.Goals
-[abstract]
-{description}
-
-include::./_includes/common-language-prereq.adoc[]
-
-[role=expertise {level}]
-{level}
-
-include::./_includes/community-driver-notice.adoc[]
-
-[#neo4j-haskell]
-== Neo4j for Haskell Developers
-
-image::{img}haskell.png[float=right]
-
-[#hasbolt-driver]
-== Hasbolt
-
-Haskell driver for Neo4j 3+ (BOLT protocol).
-
-The package covers:
-
-* Data serialization and deserialization
-* Nodes, relationships and paths support
-* Cypher queries and responses
-* Authentification
-
-The code was tested with neo4j versions 3.0 and 3.1.
-
-The https://github.com/neo4j-examples/neo4j-movies-haskell-bolt[example movie database application^] uses https://github.com/scotty-web/scotty[Scotty^] as a web framework and comes with detailed documentation, including deployment instructions to Heroku.
-
-[cols="1,4"]
-|===
-| icon:user[] Author         | https://twitter.com/zmactep[Pavel Yakovlev^]
-| icon:gift[] Package        | https://hackage.haskell.org/package/hasbolt
-| icon:github[] Source       | https://github.com/zmactep/hasbolt
-
-| icon:play-circle[] Example | {examples}/neo4j-movies-haskell-bolt
-| icon:book[] Docs           | https://github.com/zmactep/hasbolt#readme
-| icon:book[] Blog           | https://neo4j.com/blog/hasbolt-haskell-neo4j-bolt-driver/
-|===
diff --git a/modules/ROOT/pages/importTickets.cypher b/modules/ROOT/pages/importTickets.cypher
deleted file mode 100644
index a35d4b45..00000000
--- a/modules/ROOT/pages/importTickets.cypher
+++ /dev/null
@@ -1,30 +0,0 @@
-// tag::params[]
-:param params => ({ url: {url}, mall: {mall}});
-// end::params[]
-
-// tag::query[]
-
-CALL apoc.periodic.iterate(
-  "CALL apoc.load.json($url)
-   YIELD value
-   WHERE value.mall = $mall
-   RETURN value
-   LIMIT 20000",
-  "CREATE (t:Ticket {id: value._id, datetime: datetime(value.date)})
-   MERGE (c:Client {id: value.client})
-   CREATE (c)-[:PURCHASED]->(t)
-   WITH value, t
-   UNWIND value.items as item
-   CREATE (t)-[:HAS_TICKETITEM]->(ti:TicketItem {
-     product: item.desc,
-     netAmount: item.net_am,
-     units: item.n_unit
-   })
-   MERGE (p:Product {description: item.desc})
-   CREATE (ti)-[:FOR_PRODUCT]->(p)",
-  { batchSize: 10000,
-    iterateList: true,
-    parallel: false,
-    params: $params }
-);
-// end::query[]
diff --git a/modules/ROOT/pages/import_csv.cypher b/modules/ROOT/pages/import_csv.cypher
deleted file mode 100644
index 0340bd0e..00000000
--- a/modules/ROOT/pages/import_csv.cypher
+++ /dev/null
@@ -1,77 +0,0 @@
-// tag::nodes[]
-// Create orders
-LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row
-MERGE (order:Order {orderID: row.OrderID}) 
-  ON CREATE SET order.shipName = row.ShipName;
-
-// Create products
-LOAD CSV WITH HEADERS FROM 'file:///products.csv' AS row
-MERGE (product:Product {productID: row.ProductID}) 
-  ON CREATE SET product.productName = row.ProductName, product.unitPrice = toFloat(row.UnitPrice);
-
-// Create suppliers
-LOAD CSV WITH HEADERS FROM 'file:///suppliers.csv' AS row
-MERGE (supplier:Supplier {supplierID: row.SupplierID}) 
-  ON CREATE SET supplier.companyName = row.CompanyName;
-
-// Create employees
-LOAD CSV WITH HEADERS FROM 'file:///employees.csv' AS row
-MERGE (e:Employee {employeeID:row.EmployeeID}) 
-  ON CREATE SET e.firstName = row.FirstName, e.lastName = row.LastName, e.title = row.Title;
-
-// Create categories
-LOAD CSV WITH HEADERS FROM 'file:///categories.csv' AS row
-MERGE (c:Category {categoryID: row.CategoryID}) 
-  ON CREATE SET c.categoryName = row.CategoryName, c.description = row.Description;
-// end::nodes[]
-
-// tag::indexes[]
-CREATE INDEX product_id FOR (p:Product) ON (p.productID);
-CREATE INDEX product_name FOR (p:Product) ON (p.productName);
-CREATE INDEX supplier_id FOR (s:Supplier) ON (s.supplierID);
-CREATE INDEX employee_id FOR (e:Employee) ON (e.employeeID);
-CREATE INDEX category_id FOR (c:Category) ON (c.categoryID);
-// end::indexes[]
-
-// tag::constraints[]
-CREATE CONSTRAINT order_id ON (o:Order) ASSERT o.orderID IS UNIQUE;
-// end::constraints[]
-
-schema await
-
-// tag::rels_orders[]
-// Create relationships between orders and products
-LOAD CSV WITH HEADERS FROM 'file:///orders.csv' AS row
-MATCH (order:Order {orderID: row.OrderID})
-MATCH (product:Product {productID: row.ProductID})
-MERGE (order)-[op:CONTAINS]->(product)
-  ON CREATE SET op.unitPrice = toFloat(row.UnitPrice), op.quantity = toFloat(row.Quantity);
-
-// Create relationships between orders and employees
-LOAD CSV WITH HEADERS FROM "file:///orders.csv" AS row
-MATCH (order:Order {orderID: row.OrderID})
-MATCH (employee:Employee {employeeID: row.EmployeeID})
-MERGE (employee)-[:SOLD]->(order);
-// end::rels_orders[]
-
-// tag::rels_products[]
-// Create relationships between products and suppliers
-LOAD CSV WITH HEADERS FROM "file:///products.csv" AS row
-MATCH (product:Product {productID: row.ProductID})
-MATCH (supplier:Supplier {supplierID: row.SupplierID})
-MERGE (supplier)-[:SUPPLIES]->(product);
-
-// Create relationships between products and categories
-LOAD CSV WITH HEADERS FROM "file:///products.csv" AS row
-MATCH (product:Product {productID: row.ProductID})
-MATCH (category:Category {categoryID: row.CategoryID})
-MERGE (product)-[:PART_OF]->(category);
-// end::rels_products[]
-
-// tag::rels_employees[]
-// Create relationships between employees (reporting hierarchy)
-LOAD CSV WITH HEADERS FROM "file:///employees.csv" AS row
-MATCH (employee:Employee {employeeID: row.EmployeeID})
-MATCH (manager:Employee {employeeID: row.ReportsTo})
-MERGE (employee)-[:REPORTS_TO]->(manager);
-// end::rels_employees[]
\ No newline at end of file
diff --git a/modules/ROOT/pages/in-production.adoc b/modules/ROOT/pages/in-production.adoc
deleted file mode 100644
index 8fe66ad7..00000000
--- a/modules/ROOT/pages/in-production.adoc
+++ /dev/null
@@ -1,59 +0,0 @@
-= Neo4j Administration
-:author: Jennifer Reif
-:neo4j-version: 4.0
-:category: operations
-:tags: administration, production, architecture, performance, deployment
-:page-deprecated-title: the Neo4j Operations Manual
-:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/
-
-// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#deploy-neo4j]
-Are you preparing to deploy Neo4j into production and handle management and operation of the system?
-This section features guides and tutorials to help you understand how to deploy, maintain, and optimize Neo4j.
-
-When running Neo4j in production, we want to maximize the processes and configuration for scalability, monitoring, and day-to-day operations.
-We will cover how to run Neo4j in various environments, tune performance, operate databases, configure security, set up an architecture, and more.
-
-[IMPORTANT]
-====
-https://neo4j.com/cloud/aura?ref=developer-administration[Relax by running Neo4j in production on AuraDB.^]
-====
-
-[#maximize-features]
-== Taking advantage of the latest features
-
-Each new version of Neo4j offers new features and capabilities, as well as adjustments and improvements to existing functionality.
-Operating in the changing environment requires some knowledge and training of the features themselves, as well as how they apply to your particular environment.
-
-Various guides throughout this section will cover topics on key operational features and how they work.
-
-xref:manage-multiple-databases.adoc[Manage Multiple Databases] +
-xref:multi-tenancy-worked-example.adoc[Multi-Tenancy: A Worked Example] +
-xref:neo4j-fabric-sharding.adoc[Sharding Graph Data]
-
-[#performance-tuning]
-== Performance Tuning
-
-Running business critical or even demanding applications in production requires a robust system.
-It probably also includes some optimizations to configurations, architecture, or queries in order to maximize speed and reliability benefits for the entire system.
-
-From learning tips and best practices calculating the recommended hardware configuration for your Neo4j deployment to troubleshooting performance issues and improving performance, there are guides in this section to tackle many of these common problems.
-
-link:/developer/guide-performance-tuning/[Neo4j Performance Tuning]
-
-[#architecture-deployment]
-== Architecture & Deployment
-
-Deployment options for databases are often flexible to handle varying environments and operating requirements, and Neo4j is no exception.
-The deployment chosen will depend on existing system architecture, scalability needs, access locations and regions, and integration needs with other systems.
-
-Some of these guides cover information on handling different types of deployments, including how to deal with Neo4j in database clusters and how to containerize Neo4j with Docker.
-
-[unstyled]
-- xref:docker.adoc[Running Neo4j in Docker]
-- xref:docker-run-neo4j.adoc[How-To: Run Neo4j in Docker]
-- link:/docs/operations-manual/current/clustering/[Set up a Neo4j Cluster]
-
-Public and/or private cloud provider deployment options are also possible.
-Cloud-hosted offerings and how to deploy Neo4j to infrastructure or platform providers is documented and referenced in the xref:guide-cloud-deployment.adoc[cloud] and xref:aura-cloud-dbaas.adoc[Neo4j AuraDB] sections.
diff --git a/modules/ROOT/pages/integration.adoc b/modules/ROOT/pages/integration.adoc
deleted file mode 100644
index a4c9dfad..00000000
--- a/modules/ROOT/pages/integration.adoc
+++ /dev/null
@@ -1,19 +0,0 @@
-= Neo4j Integration
-:author: Neo4j
-:category: integrations
-:tags: integrations, tools, libraries
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/integration-tools/integration/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#neo4j-integration]
-Neo4j is supported by a rich ecosystem of libraries, tools, drivers, and guides provided by partners, users, and community contributors.
-We want to give an overview about what's available and link to the original sources.
-
-Neo4j Connectors are integrations provided by Neo4j and are supported for existing Enterprise customers.
-
-* link:https://neo4j.com/docs/spark/current/[Neo4j Connector for Apache Spark]
-* link:https://neo4j.com/docs/kafka/[Kafka Connect Neo4j Connector] (also known as Neo4j-Streams)
-* link:https://neo4j.com/bi-connector/[Neo4j Connector for Business Intelligence]
-
diff --git a/modules/ROOT/pages/intro-videos.adoc b/modules/ROOT/pages/intro-videos.adoc
deleted file mode 100644
index 77fa7cf8..00000000
--- a/modules/ROOT/pages/intro-videos.adoc
+++ /dev/null
@@ -1,107 +0,0 @@
-= Introduction to Graph Databases Video Series
-:author: Neo4j
-:category: documentation
-:tags: introduction, graph, videos, resources
-:page-pagination: next
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#intro-videos]
-
-
-Hello aspiring graphistas,
-
-Welcome to the **Intro to Graph Databases** video series.
-We've created this series to teach people of all levels how graph databases work and when they're best applied.
-
-If you're already familiar with SQL and NoSQL databases, you may skip some of the beginning content (or watch at 1.5x as many of us do!).
-
-Hope you enjoy and stay in touch on the link:https://community.neo4j.com/[Community Site^] if you have any questions or problems during your learning journey.
-If you'd prefer courses that mix video content with writtent content, exercises and quizes, please check out link:/graphacademy/online-training[Neo4j GraphAcademy online training^].
-
-Cheers, +
-- Michael Hunger +
-Caretaker General for the Neo4j Community
-
-
-[#episode-one]
-== Episode 1: Evolution of Databases
-++++
-
    
-
    
-Episode 1 video
-
    
-
    
-Takes you through the evolution of databases, as the shape of our data and volume of data has changed - from RDBMS to key value, to document DBs, to Neo4j.
-
    
-
    
-++++
-
-
-[#episode-two]
-== Episode 2: Properties of Graph DBs and Usecases
-++++
-
    
-
    
-Episode 2 video
-
    
-
    
-Introduces the three properties which make graph databases loved by the developer community and big companies like eBay - intuitiveness, speed, and agility.
-
    
-
    
-++++
-
-[#episode-three]
-== Episode 3: Property Graph Model
-++++
-
    
-
    
-Episode 3 video
-
    
-
    
-Teaches how the property graph represents data, starting with a basic example: Dan Loves Ann. Discusses nodes, relationships, and properties on both nodes and relationships. Labels, directionality, and Cypher CREATE statements are also introduced.
-
    
-
    
-++++
-
-[#episode-four]
-== Episode 4: (RDBMS+SQL) to (Graphs+Cypher)
-++++
-
    
-
    
-Episode 4 video
-
    
-
    
-Ready to build your application with Neo4j? This video introduces you to the three key steps: creating the model, loading data, querying.  Much of the episode covers moving from RDBMS to Graphs.  As David Meza from NASA says, "I love Neo4j because I can explore relationships faster than you can say SQL JOIN.
-
    
-
    
-++++
-
-[#episode-five]
-== Episode 5: Cypher, the Graph Query Language
-++++
-
    
-
    
-Episode 5 video
-
    
-
    
-This episode of the Intro to Graph Databases Series introduces the viewer to the evolution of developer surfaces for Neo4j, reviews the Property Graph model and then dives into creating, querying and updating data in the graph.  It also touches upon creating uniqueness constraints and the differences between CREATE and MERGE.
-
    
-
    
-++++
-
-[#episode-six]
-== Episode 6: Continuing with Cypher
-++++
-
    
-
    
-Episode 6 video
-
    
-
    
-This episode of the Intro to Graph Databases Series dives a little deeper on Cypher, the graph query language. It discusses key elements of the Cypher syntax, including comparison operators, aggregation functions and boolean operators. The video also explains how aggregates are done differently in Cypher vs SQL and covers high level filtering techniques via the WHERE clause.
-
    
-
    
-++++
diff --git a/modules/ROOT/pages/java-driver-spring-boot-starter.adoc b/modules/ROOT/pages/java-driver-spring-boot-starter.adoc
deleted file mode 100644
index 0a075c44..00000000
--- a/modules/ROOT/pages/java-driver-spring-boot-starter.adoc
+++ /dev/null
@@ -1,24 +0,0 @@
-= Neo4j Java Driver Spring Boot Starter
-:doctype: book
-:level: Intermediate
-:page-level: Intermediate
-:manualIncludeDir: https://raw.githubusercontent.com/neo4j/neo4j-java-driver-spring-boot-starter/master/
-:gh_base: https://github.com/neo4j/neo4j-java-driver-spring-boot-starter
-:author: Neo4j
-:programming-language: java
-:category: drivers
-:tags: java, spring, spring-boot, app-development, applications
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/java/spring-data-neo4j/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[NOTE]
-====
-The Spring Boot starter is now superseded by the Neo4j Java Driver auto config starting with Spring Boot 2.4.
-For Spring Boot 2.3. we will still provide support with this starter.
-====
-
-include::{manualIncludeDir}README.adoc[tags=properties]
-
-include::{manualIncludeDir}docs/manual/getting-started.adoc[lines=3..-1]
diff --git a/modules/ROOT/pages/java-frameworks.adoc b/modules/ROOT/pages/java-frameworks.adoc
deleted file mode 100644
index 9d09f5bb..00000000
--- a/modules/ROOT/pages/java-frameworks.adoc
+++ /dev/null
@@ -1,186 +0,0 @@
-= Quarkus, Helidon, Micronaut
-:level: Intermediate
-:page-level: Intermediate
-:author: Gerrit Meier
-:programming-language: java
-:category: drivers
-:tags: quarkus, helidon, micronaut, app-development, applications
-:description: For Java developers who use Quarkus, Helidon or Micronaut and want to take advantage of a pre-configured Java driver instance. This page should give an overview of the existing support for the driver in other Java frameworks. Please consult the linked documentations for more information.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/java/java-frameworks/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-[role=expertise {level}]
-{level}
-
-[#qhm-summary]
-== Driver integration
-The goal with all three integrations is to provide support for getting a managed instances of the Neo4j driver.
-Like in the link:/developer/spring-data-neo4j/#adding-config[Spring Framework], you can provide the driver properties to an _application.properties_ file (or yaml) to configure your application.
-In the end you will have an injectable driver instance that can be used with 
-
-[source,java]
-----
-@Inject
-Driver driver;
-----
-
-in the business operation code base.
-
-Additional to the managed driver bean creation, the integrations also expose health metrics for the driver and connection to your Neo4j instance.
-
-[#quarkus-integration]
-== Quarkus
-
-In an existing Quarkus application you need to add the `quarkus-neo4j` dependency to your project.
-
-[source,xml]
-----
-
-    io.quarkus
-    quarkus-neo4j
-
-----
-
-Additionally configure the basic connection parameters as needed.
-
-[source,properties]
-.Quarkus application.properties
-----
-quarkus.neo4j.uri = bolt://localhost:7687
-quarkus.neo4j.authentication.username = neo4j
-quarkus.neo4j.authentication.password = secret
-----
-
-If you want to make use of the health check, the additional `quarkus-smallrye-health` dependency is needed.
-
-[source,xml]
-----
-
-    io.quarkus
-    quarkus-smallrye-health
-
-----
-
-For metrics support, you would either need _MicroMeter_ (recommended by Quarkus) or _SmallRye Metrics_ (only if you really need MicroProfile specification) dependencies declared.
-
-[source,xml]
-.MicroMeter (Prometheus) dependency
-----
-
-    io.quarkus
-    quarkus-micrometer-registry-prometheus
-
-----
-
-The metrics for Neo4j have to be manually enabled in the _application.properties_.
-
-[source,properties]
-----
-quarkus.neo4j.pool.metrics-enabled = true
-----
-
-[#helion-integration]
-== Helidon
-
-In a Helidon-based application you need to declare the Neo4j Java driver dependency in your Maven _pom.xml_.
-
-[source,xml]
-----
-
-    io.helidon.integrations.neo4j
-    helidon-integrations-neo4j
-    ${helidon.version}
-
-----
-
-Providing the essential connection parameters will give you a managed instance of the Java driver.
-
-[source,properties]
-.Helidon application.properties
-----
-neo4j.uri = bolt://localhost:7687
-neo4j.authentication.username = neo4j
-neo4j.authentication.password = secret
-# Enable metrics
-neo4j.pool.metricsEnabled = true
-----
-
-If you want to use the health and metrics system, you have to also declare those dependencies provided by the Helidon framework.
-
-[source,xml]
-----
-
-        io.helidon.integrations.neo4j
-        helidon-integrations-neo4j-health
-        ${helidon.version}
-    
-    
-        io.helidon.integrations.neo4j
-        helidon-integrations-neo4j-metrics
-        ${helidon.version}
-
-----
-
-Now you can put together the configuration
-
-[source,java]
-.Configuration with metrics and health
-----
-Neo4JSupport neo4j = Neo4JSupport.builder()
-        .config(config)
-        .helper(Neo4JMetricsSupport.create())
-        .helper(Neo4JHealthSupport.create())
-        .build();
-
-Routing.builder()
-        .register(health)
-        .register(metrics)
-        .register(movieService)
-        .build();
-----
-
-and get the managed driver bean.
-
-
-[#micronaut-integration]
-== Micronaut
-
-To enable the Neo4j Driver support in Micronaut, the _micronaut-neo4j-bolt_ dependency needs to get declared.
-
-[source,xml]
-----
-
-    io.micronaut.neo4j
-    micronaut-neo4j-bolt
-
-----
-
-Adding the needed connection parameters to the _application.properties_.
-
-[source,properties]
-.Micronaut application.properties
-----
-neo4j.uri = bolt://localhost:7687
-neo4j.username = neo4j
-neo4j.password = secret
-----
-
-The module will automatically add its information to the built-in _/health_ endpoint.
-
-
-[#qhm-resources]
-== Resources
-
-[cols="1,4"]
-|===
-| icon:book[] Quarkus Documentation | https://quarkus.io/guides/neo4j[Neo4j integration^], https://quarkus.io/guides/neo4j#configuration-reference[Configuration properties], https://quarkus.io/guides/[Guide^]
-| icon:book[] Helidon Documentation | https://helidon.io/docs/v2/[Reference^], https://blogs.oracle.com/javamagazine/fast-flexible-data-access-in-java-using-the-helidon-microservices-platform#anchor_7[Helidon Neo4j^]
-| icon:book[] Micronaut Documentation | https://micronaut-projects.github.io/micronaut-neo4j/latest/guide/[Neo4j integration^], https://docs.micronaut.io/latest/guide/[Guide^]
-| icon:play-circle[] Examples | https://github.com/michael-simons/neo4j-from-the-jvm-ecosystem[Quarkus, Helidon, Micronaut examples^]
-|===
diff --git a/modules/ROOT/pages/java-procedures.adoc b/modules/ROOT/pages/java-procedures.adoc
deleted file mode 100644
index 10a4cf8e..00000000
--- a/modules/ROOT/pages/java-procedures.adoc
+++ /dev/null
@@ -1,39 +0,0 @@
-= Procedures and Functions
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: java
-:category: drivers
-:tags: java, procedures, extensions, java-api
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/java/java-procedures/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#custom-proc-func]
-== User-Defined Procedures and Functions
-
-link:/developer/procedures-functions[User Defined *Procedures* and *Functions*^] are available within Cypher and encapsulate dedicated functionality.
-
-Just by annotating methods of a Java class and deploying the resulting jar file into your Neo4j installation, you can make new functionality easily available within the query language.
-
-To implement your procedures or functions you would use the Neo4j Embedded Java API.
-Besides an object-oriented API to the graph database, working with `Node`, `Relationship`, and `Path` objects, it also offers highly customizable, high-speed traversal- and graph-algorithm implementations.
-
-We don't provide code examples for the Java API on this page, because they are covered in detail in the link:/docs/java-reference/current[Java developers manual^].
-
-Neo4j uses that functionality itself for built-in procedures for meta-data, cluster-, query- and user-management and more.
-
-Several libraries already provide new capabilities using procedures and functions, here is an example from the link:/developer/neo4j-apoc/[APOC] library.
-
-[source,cypher]
-----
-MATCH (start:City {name: 'Berlin'}),(end:City {name: 'Malmö'})
-CALL apoc.algo.dikjstra(start, end, "ROUTE","distance") yield path, weight
-RETURN path
-ORDER BY weight ASC LIMIT 10
-----
-
-To get you started we provided a https://github.com/neo4j-examples/neo4j-procedure-template[template project^] and documentation in the link:/docs/java-reference/current/#procedures[Java developer manual^].
-
-More useful examples for user-defined procedures can be found in our link:/developer/procedures-functions/[procedure & function guide].
diff --git a/modules/ROOT/pages/java-third-party.adoc b/modules/ROOT/pages/java-third-party.adoc
deleted file mode 100644
index 43601738..00000000
--- a/modules/ROOT/pages/java-third-party.adoc
+++ /dev/null
@@ -1,139 +0,0 @@
-= Third-party libraries
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: java
-:category: drivers
-:tags: java, third-party, groovy-grails, scala, app-development, applications
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/java/java-third-party/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-include::./_includes/community-driver-notice.adoc[]
-
-[#neo4j-jdbc]
-=== Using Neo4j Server with JDBC
-
-Being a Java developer you're probably familiar with JDBC as a way to work with relational databases, either directly or via abstractions like Spring's `JDBCTemplate` or MyBatis.
-Many tools for ETL, Report Generators, Business Intelligence, and data management also use JDBC drivers to interact with relational databases.
-
-As Cypher, like SQL, is a textual parameterizable query language that can return tabular results, it was possible for us to support the JDBC APIs and make a http://github.com/neo4j-contrib/neo4j-jdbc[Neo4j-JDBC^] driver available.
-
-The driver supports the new binary "Bolt" protocol of Neo4j 3.x, the transactional http endpoint and Neo4j embedded connections.
-
-[source,text/x-java]
-----
-// Connect
-Connection con = DriverManager.getConnection("jdbc:neo4j:bolt://localhost");
-
-// Querying
-try (Statement stmt = con.createStatement()) {
-    ResultSet rs = stmt.executeQuery("MATCH (n:User) RETURN n.name");
-	while (rs.next()) {
-		System.out.println(rs.getString("n.name"));
-	}
-}
-con.close();
-----
-
-More details on how to use it can be found with our example project for {examples}/movies-java-jdbc[Java-JDBC^].
-There, we implement the backend as a minimal Java webapp that leverages Neo4j-JDBC to connect to Neo4j Server.
-
-:jdbc-tools-link: https://github.com/neo4j-contrib/neo4j-jdbc/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22
-
-We tested the driver with many link:{jdbc-tools-link}[enterprise tools^] to ensure that it works in a variety of environments.
-Just grab the https://github.com/neo4j-contrib/neo4j-jdbc/releases/latest[JAR of the latest release^] and add it to your tool of choice.
-
-[cols="1,4"]
-|===
-| icon:user[] Authors | Developers from http://www.larus-ba.it/neo4j/en/[Larus BA Italy^] and Neo4j
-| icon:gift[] Package | https://github.com/neo4j-contrib/neo4j-jdbc/releases/latest
-| icon:github[] Source | https://github.com/neo4j-contrib/neo4j-jdbc
-| icon:book[] Docs | https://github.com/neo4j-contrib/neo4j-jdbc/blob/master/README.adoc
-| icon:play-circle[] Example | {examples}/movies-java-jdbc
-| icon:book[] Blog Post | http://neo4j.com/blog/couchbase-jdbc-integrations-neo4j-3-0/
-|===
-
-[#java-jcypher]
-=== JCypher
-
-JCypher provides seamlessly integrated Java access to Neo4j at different levels of abstraction:
-
-At the topmost level of abstraction, JCypher allows you to *map complex business domains* to graph databases.
-You can take an arbitrarily complex graph of domain objects or POJOs and store it in a straightforward way into Neo4j for later retrieval. 
-You do not need to modify your domain object classes in any way, there is also no need for annotations. 
-JCypher provides a default mapping out of the box.
-
-At the same level of abstraction, *Domain Queries* provide the power and expressiveness of queries on a graph database, 
-while being formulated on domain objects or on types of domain objects, respectively.
-The true power of Domain Queries comes from the fact, that the graph of domain objects is backed by a graph database.
-
-At the next lower level of abstraction – access to graph databases – is provided based on a generic graph model.
-While simple, the model allows you to easily navigate and manipulate graphs. 
-
-At the bottom level of abstraction, a *native Java DSL* in the form of a fluent Java API allows you to intuitively and comfortably formulate queries against Neo4j.
-
-:maven-jcypher: http://search.maven.org/#search|gav|1|g%3A%22net.iot-solutions.graphdb%22%20AND%20a%3A%22jcypher%22
-
-[cols="1,4"]
-|===
-| icon:user[] Author | https://github.com/Wolfgang-Schuetzelhofer[Wolfgang Schützelhofer^]
-| icon:gift[] Package | link:{maven-jcypher}[http://maven.org^]
-| icon:github[] Source | http://github.com/Wolfgang-Schuetzelhofer/jcypher
-| icon:book[] Docs | http://jcypher.iot-solutions.net/
-| icon:book[] Article | https://neo4j.com/blog/jcypher-focus-on-your-domain-model-not-how-to-map-it-to-the-database/
-|===
-
-[#neo4j-grails]
-=== Groovy & Grails: Neo4j Grails Plugin
-
-image::{img}grails.png[width=200,float=right]
-
-The goal of GORM for Neo4j is to provide a 'as-complete-as-possible' GORM implementation that maps domain classes and instances to the Neo4j nodespace. The following features are supported:
-
-* Marshalling from Neo4j Nodes to Groovy types and back again
-* Support for GORM dynamic finders, criteria and named queries
-* Session-managed transactions
-* Access to Neo4j's traversal capabilities
-* Access the Neo4j graph database via the Bolt driver
-[cols="1,4"]
-|===
-| icon:user[] Authors | Stefan Armbruster, Graeme Rocher
-| icon:gift[] Package | http://www.grails.org/plugin/neo4j
-| icon:github[] Source | https://github.com/grails/grails-data-mapping/tree/master/grails-datastore-gorm-neo4j
-| icon:book[] Docs | http://grails.github.io/grails-data-mapping/latest/neo4j/
-|===
-
-[#neo4j-scala]
-=== Scala: neotypes
-
-image::{img}scala.png[width=200,float="right"]
-
-Scala lightweight, type-safe, asynchronous driver (not opinionated on side-effect implementation) for neo4j.
-The project aims to provide seamless integration with most popular scala infrastructures such as lightbend (Akka, Akka-http, Lagom, etc), typelevel (cats, http4s, etc), twitter (finch, etc)...
-
-[cols="1,4"]
-|===
-| icon:user[] Author | https://twitter.com/dimafeng[Dmitry Fedosov^]
-| icon:github[] Source | https://github.com/neotypes/neotypes
-| icon:book[] Docs | https://neotypes.github.io/neotypes/docs.html
-| icon:book[] Article | http://dimafeng.com/2018/12/27/neotypes-1/
-| icon:play-circle[] Example | https://github.com/neotypes/examples
-|===
-
-[#ogm-hibernate]
-=== JPA: Hibernate OGM
-
-Hibernate Object/Grid Mapper (http://hibernate.org/ogm[OGM^]) with Neo4j Support.
-
-:maven-hibernate-ogm: http://search.maven.org/#search|gav|1|g%3A%22org.hibernate.ogm%22%20AND%20a%3A%22hibernate-ogm-neo4j%22
-[cols="1,4"]
-|===
-| icon:user[] Authors | Davide D'Alto, Gunnar Moelling, Emmanuel Bernard
-| icon:gift[] Package | link:{maven-hibernate-ogm}[http://maven.org^]
-| icon:github[] Source | https://github.com/hibernate/hibernate-ogm/tree/master/neo4j
-| icon:book[] Docs | http://docs.jboss.org/hibernate/ogm/5.0/reference/en-US/html_single/#ogm-neo4j
-| icon:book[] Article | http://in.relation.to/Bloggers/QueryImprovementsAndBetterNeo4jSupportHibernateOGM410Beta6IsOut[Blog], http://in.relation.to/Bloggers/HibernateOGM410Beta5IsOutJPQLQueriesForNeo4jAndMore[JPL Queries^]
-| icon:play-circle[] Example | https://github.com/TimmyStorms/hibernate-ogm-neo4j-example
-|===
diff --git a/modules/ROOT/pages/java.adoc b/modules/ROOT/pages/java.adoc
deleted file mode 100644
index 80fa62b5..00000000
--- a/modules/ROOT/pages/java.adoc
+++ /dev/null
@@ -1,62 +0,0 @@
-= Using Neo4j from Java
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: java
-:category: drivers
-:tags: java, project, official-driver, app-development, applications
-:description: If you are a Java developer, this guide provides an overview of options for connecting to Neo4j.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/java/java-intro/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-While this guide is not comprehensive, it will introduce the different APIs and link to the relevant resources.
-
-include::./_includes/common-language-prereq.adoc[]
-When developing with Neo4j, please use Java 8 or 11 and your favorite IDE.
-
-[role=expertise {level}]
-{level}
-
-[#neo4j-java]
-== Neo4j for Java Developers
-
-image::{img}java.jpg[float=right,width=200]
-
-Neo4j provides drivers which allow you to make a connection to the database and develop applications
-which create, read, update, and delete information from the graph.
-
-For Community Edition and Enterprise Edition, you can also extend Neo4j by implementing *user defined procedures for Cypher* in Java or other JVM languages.
-
-// ****
-// [NOTE]
-// Please note the minimum version compatibilities: +
-// Neo4j Server 3.x - Java 8 +
-// Neo4j Server 4.x - Java 11
-// ****
-
-include::./_includes/sample_project.adoc[]
-
-[.discrete.ad]
-== link:https://graphacademy.neo4j.com/?ref=guides[Learn with GraphAcademy^]
-
-image::https://graphacademy.neo4j.com/courses/app-java/badge/[float=left]
-
-[.discrete]
-=== link:https://graphacademy.neo4j.com/courses/app-java/?ref=guides[Building Neo4j Applications with Java^]
-
-In this free course, we walk through the steps to integrate Neo4j into your Java projects.
-You will learn about the Neo4j Java Driver, how sessions and transactions work, how to query Neo4j and process results.
-
-link:https://graphacademy.neo4j.com/courses/app-java/?ref=guides[Learn more^,role=button]
-
-
-
-[#java-driver]
-== Neo4j Java Driver
-
-include::./_includes/java-driver.adoc[]
diff --git a/modules/ROOT/pages/javascript.adoc b/modules/ROOT/pages/javascript.adoc
deleted file mode 100644
index f5227e19..00000000
--- a/modules/ROOT/pages/javascript.adoc
+++ /dev/null
@@ -1,110 +0,0 @@
-= Neo4j from JavaScript
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: javascript
-:category: drivers
-:tags: javascript, official-driver, app-development, applications
-:description: If you are a JavaScript developer, this guide provides an overview of options for connecting to Neo4j.
-// :page-ad-overline-link: https://graphacademy.neo4j.com/
-// :page-ad-overline: Neo4j GraphAcademy
-// :page-ad-title: Building Neo4j Applications with Node.js
-// :page-ad-description: Learn how to interact with Neo4j from your Node.js app using the Neo4j Python Driver
-// :page-ad-link: https://graphacademy.neo4j.com/courses/app-nodejs/
-// :page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/neo4j-javascript/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-While this guide is not comprehensive it will introduce the different Drivers and link to the relevant resources.
-
-include::./_includes/common-language-prereq.adoc[]
-
-[role=expertise {level}]
-{level}
-
-[#neo4j-javascript]
-== Neo4j for JavaScript Developers
-
-image::{img}nodejs.png[float=right,width=300]
-
-Neo4j provides drivers which allow you to make a connection to the database and develop applications
-which create, read, update, and delete information from the graph.
-
-You can use the official driver for JavaScript (neo4j-driver) or connect via HTTP using the `request` module or any of our community drivers.
-
-[.discrete.ad]
-== link:https://graphacademy.neo4j.com/?ref=guides[Learn with GraphAcademy^]
-
-image::https://graphacademy.neo4j.com/courses/app-nodejs/badge/[float=left]
-
-[.discrete]
-=== link:https://graphacademy.neo4j.com/courses/app-nodejs/?ref=guides[Building Neo4j Applications with Node.js^]
-
-In this free course, we walk through the steps to integrate Neo4j into your Node.js projects.
-You will learn about the Neo4j JavaScript Driver, how sessions and transactions work and how to query Neo4j from an existing application.
-
-link:https://graphacademy.neo4j.com/courses/app-nodejs/?ref=guides[Learn more^,role=button]
-
-
-image::https://graphacademy.neo4j.com/courses/app-typescript/badge/[float=left]
-
-[.discrete]
-=== link:https://graphacademy.neo4j.com/courses/app-typescript/?ref=guides[Building Neo4j Applications with TypeScript^]
-
-link:https://graphacademy.neo4j.com/courses/app-typescript/?ref=guides[Building Neo4j Applications with TypeScript^] course is a shorter course, with an estimated duration of two hours, aimed at TypeScript developers that are interested in learning the TypeScript specific features which add type-checking to any interactions you have with a Neo4j instance.
-
-link:https://graphacademy.neo4j.com/courses/app-typescript/?ref=guides[Learn Neo4j & TypeScript^,role=button]
-
-
-[#javascript-driver]
-== Neo4j Javascript Driver
-
-include::./_includes/javascript-driver.adoc[]
-
-include::./_includes/sample_project.adoc[]
-
-include::./_includes/community-driver-notice.adoc[]
-
-[#js-http-endpoint]
-== Using the HTTP-Endpoint directly
-
-You can use something as simple as the `request` node-module to send queries to and receive responses from Neo4j.
-The endpoint protocol and formats are explained in detail in the link:/docs/http-api/current/introduction/[Neo4j Manual^].
-It enables you do to much more, e.g. sending many statements per request or keeping transactions open across multiple requests.
-
-Here is a very simple example:
-
-.Simple Function Accessing the Remote Endpoint
-[source, javascript]
-----
-var r=require("request");
-var txUrl = "http://localhost:7474/db/neo4j/tx/commit"; // neo4j is the database name
-function cypher(query,params,cb) {
-  r.post({uri:txUrl,
-          json:{statements:[{statement:query,parameters:params}]}},
-         function(err,res) { cb(err,res.body)})
-}
-----
-
-.Running the Function
-[source, javascript]
-----
-var query="MATCH (n:User) RETURN n, labels(n) as l LIMIT {limit}"
-var params={limit: 10}
-var cb=function(err,data) { console.log(JSON.stringify(data)) }
-
-cypher(query,params,cb)
-
-{"results":[
-  {"columns":["n","l"],
-   "data":[
-     {"row":[{"name":"Aran"},["User"]]}
-    ]
-  }],
- "errors":[]}
-----
diff --git a/modules/ROOT/pages/js-movie-app.adoc b/modules/ROOT/pages/js-movie-app.adoc
deleted file mode 100644
index 5fc61fa4..00000000
--- a/modules/ROOT/pages/js-movie-app.adoc
+++ /dev/null
@@ -1,641 +0,0 @@
-= Getting Started with Neo4j and Express
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:category: documentation
-:tags: node, express, javascript, react
-:description: This course provides an overview on everything that you need to build a Neo4j application with the link:https://en.wikipedia.org/wiki/JavaScript[JavaScript programming language^].
-The Express framework is used as the backend, and the React framework as front-end.
-:redirect: https://graphacademy.neo4j.com/courses/app-nodejs/
-
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should have `lts/erbium` installed on your system.
-This project uses link:https://github.com/nvm-sh/nvm[nvm], so having NVM will make your life easier. 
-You should have cloned https://github.com/neo4j-examples/neo4j-movies-template[Neo4j Movie Template] onto your machine.
-
-
-[#javascript-movie-course]
-This course provides an overview of what you need to know to write an Express application using a Neo4j database. 
-To keep concepts simple, the application is an IMDB clone with basic account authentication and movie recommendation functionality. 
-
-[#neo4j-javascript]
-== Before We Start - Why Neo4j?
-
-Neo4j is the world's most popular _graph database_.
-Graph databases offer a number of advantages:
-
-* Neo4j provides a schemaless representation of both entities and relationships between entities.
-* Relationships between entities are *traversed* rather than *joined*. Traversals explore the local subgraph, meaning that query times stay the same even as your database grows.
-* Because of the traversal paradigm, we can think in terms of the complex relationships in our data, without worrying as much about how to model it.
-
-Neo4j makes it easy to create nodes and relationships in an intuitive way, but you can also always change the structure of your database with a query.
-
-[#intro-neo4j]
-== Introduction to Neo4j
-
-Connected information is everywhere in our world.
-Neo4j was built to efficiently store, handle, and query highly-connected elements in your data model.
-With a powerful and flexible data model, you can represent your real-world, variably-structured information without a loss of fidelity.
-The property graph model is easy to understand and work with, especially for object-oriented and relational developers.
-
-The property graph model consists of:
-
-**Nodes**, which have:
-
-* _properties_: schemaless key/value pairs
-* _labels_: describe and group nodes much like tables group rows, except nodes can also have multiple labels
-
-**Relationships**, which connect two nodes directionally and have:
-
-* _properties_: schemaless key/value pairs
-* A _type_: describes how it connects the two nodes
-
-While relationships are directional, querying relationships in either direction has no associated performance cost.
-
-image::{img}model.png[][JavaScript Movie App Model]
-
-=== Cypher
-
-Cypher is Neo4j's built-in query language.
-Cypher queries look like the following code block:
-
-[source, cypher,role=nocopy,noplay]
-----
-MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
-RETURN p, m.title
-----
-
-The `MATCH` clause is the most common starting point for Cypher queries.
-It defines a pattern to search the database for, and returns one result for each match.
-With the `RETURN` clause, we would end up returning a table such as:
-
-.Result of Cypher query
-[width="99%", options="header"]
-|=============================
-| p                | m.title
-| {name: "Denise"} | "Toy Story"
-| {name: "Denise"} | "Animal House"
-|=============================
-
-As you can see here, we can return entire entities in our database rather than just their properties.
-
-This is very handy, but it would also be nice to avoid the duplication of our `Person` node.
-So, you can perform the same match but instead use the `collect` function to aggregate the values:
-
-[source, cypher,role=nocopy,noplay]
-----
-MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
-RETURN p, collect(m.title)
-----
-
-.Result of Cypher aggregation query
-[width="99%", options="header"]
-|=============================
-| p                | m.title
-| {name: "Denise"} | ["Toy Story", "Animal House"]
-|=============================
-
-While it's possible to get started using Neo4j without learning Cypher, it is a very powerful tool to query a Neo4j database and is worth learning.
-Also, since this project works by making Cypher queries to Neo4j, it is helpful to understand Cypher as your queries get more complex.
-Here is are links to learn more:
-
-* https://graphacademy.neo4j.com/courses/neo4j-fundamentals/[Neo4j Fundamentals^]
-* https://graphacademy.neo4j.com/courses/cypher-fundamentals/[Cypher Fundamentals^]
-* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^]
-* https://graphacademy.neo4j.com/courses/cypher-intermediate-queries/[Cypher Intermediate Queries^]
-
-
-
-[#project-setup]
-== The Neo4j Movie Express/React App
-
-Let’s jump right into it. 
-You’re a JavaScript developer interested in Neo4j and want to build a web app, microservice, or mobile app. 
-You’ve already read up on Neo4j, played around with some datasets, and learned enough Cypher to get started. 
-Now you’re looking for a demo app or template to start putting those skills into practice.
-
-If you haven't already, clone the https://github.com/neo4j-examples/neo4j-movies-template[Neo4j Movie Template] onto your machine.
-This tutorial post will walk you through rating a movie on a sample movie rating application, from the initial setup to viewing the list of movies you’ve rated.
-The following video will walk briefly walk you through the various features of the movie app.
-
-++++
-
-++++
-
-=== The Database
-
-This project uses a classic Neo4j dataset: the movie database. It includes `Movie`, `Actor`, `Director`, and `Genre` nodes, connected by relationships as described below:
-
-[source, cypher, role=nocopy,noplay]
-----
-(:Movie)-[:HAS_GENRE]→(:Genre)
-(:Actor)-[:ACTED_IN]→(:Movie)
-(:Director)-[:DIRECTED]→(:Movie)
-----
-
-Additionally, users can create accounts, log in, and add their ratings to movies:
-
-[source, cypher, role=nocopy,noplay]
-----
-(:User)-[:RATES]->(:Movie)
-----
-
-==== The API
-
-The Express portion of the application interfaces with the database and presents data to the React.js front-end via a RESTful API. 
-You can find the Express API in the `/api` directory in the repo.
-
-==== The Front-End
-
-The front-end, built in React.js, consumes the data provided by the Express API and presents it through some views to the end user, including:
-
-* Home page
-* Movie detail page
-* Actor and Director detail page
-* User detail page
-* Sign-up and Login pages
-
-You can find the front-end code in the `web` directory. 
-
-=== Setup
-
-To get the project running, clone the repo and follow along with these instructions, which are be recapped in the video:
-
-++++
-
-++++
-
-If you haven't already, https://github.com/neo4j-examples/neo4j-movies-template[clone the repo].
-
-Your app will need a database, and the easiest way to access a database that's already full of data is by connecting directly to the “Recommendations” database in Neo4j Sandbox. 
-
-Log in to Neo4j Sandbox by visiting https://sandbox.neo4j.com/, either using social authentication or your email and password. 
-
-After logging in to Neo4j Sandbox, tap “New Project” and select “Recommendations,” then tap the blue “Launch Project” button to start the database you will be connecting to. 
-
-In order to connect to the database from the environment from which you’ll be running the app (presumably your local machine), you’ll need credentials. You can find those under the “Connection details” and/or the “Connect via drivers” tab.
-
-You’ll need to copy+paste the credentials in the driver section to connect to the database from your local machine.
-For example, if the driver line contains the following:
-
-[source,javascript,role=nocopy]
-----
-const driver = neo4j.driver("bolt://52.72.13.205:47929", neo4j.auth.basic("neo4j", "knock-cape-reserve"))
-----
-
-Then, in your text editor, open and/or create `api/.env` and enter the appropriate information into the variables: `DATABASE_USERNAME`, `DATABASE_PASSWORD`, and `DATABASE_URL`. Then save the file.
-
-----
-SECRET_KEY="super secret guy"
-MOVIE_DATABASE_USERNAME="neo4j"
-MOVIE_DATABASE_PASSWORD="height-restrictions-calculators"
-MOVIE_DATABASE_URL="bolt://52.91.202.103:32814"
-----
-
-To start the Express API, run:
-
-[source,shell]
-----
-cd api
-nvm use
-npm install
-node app.js
-----
-
-Verify that the endpoints are running as expected by taking a look at the docs at: `http://localhost:5000/docs`
-
-=== Start the React.js Front-End
-
-With the database and backend running, open a new terminal tab or window and move to the project’s `/web` subdirectory. 
-Run `nvm use` to ensure you’re using the node version specified for this project.
-If you don’t have the recommended version of node installed, follow the prompt to install the recommended version. 
-After verifying you are using the recommended user, run:
-
-[source,shell]
-----
-npm install 
-cp src/config/settings.example.js src/config/settings.js
-npm start 
-----
-
-Navigate to view the app at `http://localhost:3000/`
-
-Click on a movie poster to see its corresponding movie detail page.
-
-Click on a cast or crew member to see that person’s profile, which includes biographical information, related people, and more movies the person has acted in, directed, written, or produced:
-
-== Going Through The Endpoints
-
-=== Genres
-
-Diving down from route to model, a call from the user would travel through the route set in `api/app.js`
-
-.api/app.js
-[source,javascript,role=nocopy]
-----
-api.get("/genres", routes.genres.list);
----- 
-
-The information from the call would be routed by `api/routes/genres.js` in and out of the the Genre model in `api/models/genres.js`, sending the response, an array of `Genres`, back to the user. 
-
-.api/models/genres.js
-[source,javascript,role=nocopy]
-----
-const _ = require('lodash');
-const Genre = require('../models/neo4j/genre');
-
-const getAll = function(session) {
-  return session.readTransaction(txc =>
-      txc.run('MATCH (genre:Genre) RETURN genre')
-    ).then(_manyGenres);
-};
-
-const _manyGenres = function (result) {
-  return result.records.map(r => new Genre(r.get('genre')));
-};
-
-module.exports = {
-  getAll: getAll
-};
-----
-
-
-=== Beyond the `/Genres` Endpoint
-
-Of course, an app that just shows movie genres isn’t very interesting.
-Take a look at the routes and models used to build the home page, `Movie` detail page, and `Person` detail page.
-
-=== The User Model
-
-Aside from creating themselves and authenticating with the app, Users can rate Movies with the `:RATED` relationship, illustrated below.
-
-==== User Properties
-
-`password`: The hashed version of the user’s chosen password
-`api_key`: The user’s API key, which the user uses to authenticate requests
-`username`: The user’s chosen username
-
-==== :RATED Properties
-
-`rating`: an integer rating between 1 and 5, with 5 being love it and 1 being hate it
-
-==== Users Can Create Accounts
-
-Before a User can rate a Movie, the user has to exist, i.e. someone has to sign up for an account. 
-The sign-up process will create a node in the database with a User label, along with the properties necessary for logging in and maintaining a session.
-
-The registration endpoint is located at `/api/v0/register`.
-The app automatically submits a request to the register endpoint when a user fills out the “Create an Account” form and taps “Create Account.”
-Assuming you have the API running, you can test requests either by using the interactive docs at `/3000/docs/` or by using cURL.
-
-Naturally, you should replace the placeholder fields throughout with your chosen username and password.
-
-*Example: Create a New User*
-
-.Request
-[source,shell]
-----
-curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{ "username": "Mary Jane", "password": "SuperPassword"}' 'http://localhost:5000/api/v0/register'
-----
-
-.Response
-----
-{
-   "id":"e1e157a2-1fb5-416a-b819-eb75c480dfc6",
-   "username":"Mary333 Jane",
-   "avatar":{
-      "full_size":"https://www.gravatar.com/avatar/b2a02..."
-   }
-}
-----
-
-
-*Example: Try to Create a New User but Username is Already Taken*
-
-.Request
-[source,shell]
-----
-curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d  '{ "username": "Mary Jane", "password": "SuperPassword"}' 'http://localhost:5000/api/v0/register'
-----
-
-.Response
-----
-{
-   "username":"username already in use"
-}
-----
-
-Similar to calling the `Genre` endpoint, when a user creates an account, the user flows through endpoint through the register User route to the User model and back.  
-
-.api/app.js
-[source,javascript,role=nocopy]
-----
-api.post("/register", routes.users.register);
-----
-
-.api/routes/users.js
-[source,javascript,role=nocopy]
-----
-exports.register = function (req, res, next) {
-  const username = _.get(req.body, 'username');
-  const password = _.get(req.body, 'password');
-
-  if (!username) {
-    throw {username: 'This field is required.', status: 400};
-  }
-  if (!password) {
-    throw {password: 'This field is required.', status: 400};
-  }
-
-  Users.register(dbUtils.getSession(req), username, password)
-    .then(response => writeResponse(res, response, 201))
-    .catch(next);
-};
-----
-
-
-==== Users Can Log In
-
-Now that users are able to register for an account, we can define the view that allows them to login to the site and start a session.
-
-The registration endpoint is located at `/api/v0/login`.
-The app submits a request to the login endpoint when a user fills in the username and password text boxes and taps “Create Account.”
-Assuming you have the API running, you can test requests either by using the interactive docs at `/5000/docs/` or by using cURL.
-
-
-*Example: Login*
-
-.Request
-[source,shell]
-----
-curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{"username": "Mary Jane", "password": "SuperPassword"}' 'http://localhost:5000/api/v0/login'
-----
-
-.Response
-[source,shell]
-----
-{
-  "token":"5a85862fb28a316ea6a1"
-}
-----
-
-*Example: Wrong Password*
-
-.Request
-[source,shell]
-----
-curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{ "username": "Mary Jane", "password": "SuperPassword"}' 'http://localhost:5000/api/v0/register'
-----
-
-.Response
-----
-{
-   "username":"username already in use"
-}
-----
-
-*Example: See Myself*
-
-.Request
-[source,shell]
-----
-curl -X GET --header 'Accept: application/json' --header 'Authorization: Token 5a85862fb28a316ea6a1' 'http://localhost:5000/api/v0/users/me'
-----
-
-.Response
-----
-{
-  "id": "94a604f7-3eab-4f28-88ab-12704c228936",
-  "username": "Mary Jane",
-  "avatar": {
-    "full_size": "https://www.gravatar.com/avatar/c2eab..."
-  }
-}
-----
-
-The code here is analogous to that of `/register`.
-There is a similar form to fill out, where a user types in their username and password.
-With the given username, a `User` is initialized.
-The password they filled out in the form is then verified against the hashed password that was retrieved from the corresponding :User node in the database.
-If the verification is successful, the program will return a token.
-The user is then directed to an authentication page, from which they can navigate through the app, view their user profile, and rate movies. 
-
-*Example: Users Can Rate Movies*
-
-Once a user has logged in and navigated to a page that displays movies, they can select a star rating for any movie in the page or remove any of their previous movie ratings.
-
-The user can access their previous ratings and the respective movies that were rated through both their user profile and the movie detail page in question.
-
-*Example: Rate a Movie*
-
-.Request
-[source,shell]
-----
-curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' --header 'Authorization: Token ce40f63e79344f017a48b205db27aeaa301ae2b6' -d '{"rating":4}' 'http://localhost:5000/api/v0/movies/15602/rate'
-----
-
-.Response
-----
-{}
-----
-
-The user sends who they are, what movie they want to rate, and what their rating is through the endpoint. 
-
-.api/app.js
-[source,javascript,role=nocopy]
-----
-api.post("/movies/:id/rate", routes.movies.rateMovie);
-----
-
-The information is routed through `api/routes/movies.js`:
-
-.api/routes/movies.js
-[source,javascript,role=nocopy]
-----
-exports.rateMovie = function (req, res, next) {
-  loginRequired(req, res, () => {
-    const rating = Number(_.get(req.body, 'rating'));
-    if (Number.isNaN(rating) || rating < 0 || rating >= 6) {
-      throw {rating: 'Rating value is invalid', status: 400};
-    }
-
-    Movies.rate(dbUtils.getSession(req), req.params.id, req.user.id, rating)
-      .then(response => writeResponse(res, {}))
-      .catch(next);
-  });
-};
-----
-
-The rating is applied at the `api/models/movies.js`
-
-.api/models/movies.js
-[source,javascript,role=nocopy]
-----
-const rate = function (session, movieId, userId, rating) {
-  return session.writeTransaction(txc =>
-    txc.run(
-      'MATCH (u:User {id: $userId}),(m:Movie {tmdbId: $movieId}) \
-      MERGE (u)-[r:RATED]->(m) \
-      SET r.rating = $rating \
-      RETURN m',
-      {
-        userId: userId,
-        movieId: movieId,
-        rating: parseInt(rating)
-      }
-    )
-  );
-};
-
-... 
-
-// export exposed functions
-module.exports = {
-...
-  rate: rate,
-...
-};
-----
-
-*Example: See All of My Ratings*
-
-.Request
-[source,shell]
-----
-curl -X GET --header 'Accept: application/json' --header 'Authorization: Token ce40f63e79344f017a48b205db27aeaa301ae2b6' 'http://localhost:5000/api/v0/movies/rated'
-----
-
-.Response
-----
-[
-  {
-    "id": "15602",
-    "title": "Grumpier Old Men",
-    "summary": "John and Max resolve to save their beloved bait shop from turning into an Italian restaurant, just as its new female owner catches Max's attention.",
-    "released": "1995-12-22",
-    "duration": 101,
-    "rated": 6.6,
-    "tagline": "John and Max resolve to save their beloved bait shop from turning into an Italian restaurant, just as its new female owner catches Max's attention.",
-    "poster_image": "https://image.tmdb.org/t/p/w440_and_h660_face/1FSXpj5e8l4KH6nVFO5SPUeraOt.jpg",
-    "my_rating": 4
-  }
-]
-----
-
-== The React Front-end  
-
-++++
-
-++++
-
-You can take a look at the React front-end code at in the `/web/` subdirectory. 
-The React front-end is very simple, and is composed of the following parts, as described in the video above.
-
-* Home Page
-* Authentication Page
-* Movie Detail Page
-* Person (Actor, Director) Page
-* User Profile Page
-
-=== Home Page
-
-The home page is a relatively simple page making calls to two endpoints: the "movies by genre endpoint" and the "movie by ID" endpoint.
-
-The "Featured Movies" portion at the top calls three hard-coded movies. 
-
-[source,javascript,role=nocopy]
-----
-renderFeatured() {
-    const {movies} = this.props;
-
-    return (
-      
    
-        
    Featured Movies
    
-        
      
-          { _.compact(movies.featured).map(f => {
-            return (
-              
    • 
-                
-                  
-                
-              
      • 
-            );
-          })}
-        
    
-      
    
-    );
-  }
-----
-
-[source,javascript,role=nocopy]
-----
-static getFeaturedMovies() {
-    return Promise.all([
-        axios.get(`${apiBaseURL}/movies/13380`),
-        axios.get(`${apiBaseURL}/movies/15292`),
-        axios.get(`${apiBaseURL}/movies/11398`)
-    ]);
-}
-----
-
-=== Movie and Person Detail
-
-The Movie and Person detail are visually very similar pages - both with a poster image on the left and carousels on the bottom. 
-However, the Movie page is different depending on whether or not the user is authenticated, as the authenticated user is able to mark their rating on each movie. 
-
-=== User Profile
-
-The User Profile page allows the user to re-rate or un-rate their movies, and view more movie recommendations based on those ratings. 
-
-== Deployment
-
-include::./_includes/using_aura.adoc[]
-
-=== Deplyoying the Backend and Front-end with Heroku
-
-You will have to create two apps on Heroku: one for the backend and one for the front-end. 
-
-Starting with the backend, create a new app on Heroku. 
-On your local machine, add the Heroku repo as a remote. 
-On **Heroku > Settings > Config Vars**, add the credentials to connect to the database hosted Neo4j AuraDB (or the sandbox if you haven't migrated to AuraDB). 
-
-
-Then, create another Heroku app for the front-end. 
-Add another git remote pointed to the Heroku app dedicated to the front-end app. 
-Under **Heroku > Settings > Config Vars**, add the environment variables for the `REACT_APP_API_BASE_URL` and `REACT_APP_PROXY_URL` fields. 
-
-Under **Heroku > Settings > Buildpacks**, add `mars/create-react-app` to load dependencies. 
-
-Check out the `Makefile` in the root directory of the project and edit it so it matches below the content below.
-It contains the commands needed to deploy the project. 
-You can run `deploy-api` to deploy the Express API and `deploy-web` to run deployment on the React site. 
-
-.Makefile
-----
-deploy-api:
-    git branch -f heroku-api
-    git branch -D heroku-api
-    git subtree split --prefix api -b heroku-api
-    git push heroku-api heroku-api:master --force
-
-deploy-web:
-    git branch -f heroku-web
-    git branch -D heroku-web
-    git subtree split --prefix web -b heroku-web
-    git push heroku-web heroku-web:master --force
-----
-
-++++
-
-++++
-
-== Next Steps
-
-Fork the repo and hack away! Find directors that work with multiple genres, or find people who tend to work with each other frequently as writer-director pairs.
-Did you find a way to improve the template or the Python driver? Create a GitHub Issue and/or submit a pull request.
diff --git a/modules/ROOT/pages/language-guides.adoc b/modules/ROOT/pages/language-guides.adoc
deleted file mode 100644
index c4b611cc..00000000
--- a/modules/ROOT/pages/language-guides.adoc
+++ /dev/null
@@ -1,133 +0,0 @@
-= Drivers & Language Guides
-:level: Intermediate
-:page-level: Intermediate
-:author: Jennifer Reif
-:category: drivers
-:tags: programming-languages, applications, bolt, app-development
-:page-ad-overline-link: https://neo4j.com/cloud/aura/?ref=developer-guides
-:page-ad-overline: Neo4j Aura
-:page-ad-title: Don't Have a Neo4j Instance? Launch one now.
-:page-ad-description: Free forever, no credit card required.
-:page-ad-link: https://neo4j.com/cloud/aura/?ref=developer-guides
-:page-ad-underline-role: button
-:page-ad-underline: Start on AuraDB
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#neo4j-app-dev]
-== Neo4j Language Guides
-These guides and tutorials are designed to provide detailed examples of how to integrate Neo4j with your preferred programming language.
-Neo4j *officially supports the drivers for .Net, Java, JavaScript, Go, and Python* for the binary Bolt protocol.
-Our community contributors provide drivers for all major programming languages for all protocols and APIs.
-In this section, we provide an introduction and a consistent example application for several languages and Neo4j drivers.
-
-
-
-[#graphacademy]
-== Learn with GraphAcademy
-
-We have a range of link:https://graphacademy.neo4j.com/categories/developer/?ref=guides[Neo4j Developer courses^] that teach you to build connect to Neo4j using each of our officially supported Drivers.
-
-link:https://graphacademy.neo4j.com/courses/app-java/?ref=guides[image:https://graphacademy.neo4j.com/courses/app-java/badge/[Building Neo4j Applications with Java, width=120]^]
-link:https://graphacademy.neo4j.com/courses/app-nodejs/?ref=guides[image:https://graphacademy.neo4j.com/courses/app-nodejs/badge/[Building Neo4j Applications with Node.js, width=120]^]
-link:https://graphacademy.neo4j.com/courses/app-python/?ref=guides[image:https://graphacademy.neo4j.com/courses/app-python/badge/[Building Neo4j Applications with Python, width=120]^]
-link:https://graphacademy.neo4j.com/courses/app-dotnet/?ref=guides[image:https://graphacademy.neo4j.com/courses/app-dotnet/badge/[Building Neo4j Applications with .NET, width=120]^]
-link:https://graphacademy.neo4j.com/courses/app-go/?ref=guides[image:https://graphacademy.neo4j.com/courses/app-go/badge/[Building Neo4j Applications with Go, width=120]^]
-
-
-
-[#connect-neo4j]
-== How to connect to Neo4j?
-
-If you've created an link:{aura_signup}[AuraDB instance^], or link:/download/[installed] and started Neo4j as a server on your system,
-you can already work interactively with the database via the built-in Neo4j Browser, which you will find in the AuraDB console, or if you're running locally, on http://localhost:7474[localhost:7474].
-
-To build an application, you want to connect to Neo4j from your technology stack.
-Fortunately it is very easy using a driver which connects to Neo4j via Bolt or Http.
-
-
-[#bolt-protocol]
-== The binary Bolt Protocol
-
-Starting with Neo4j 3.0 we support a binary protocol called Bolt.
-It is based on the PackStream serialization and supports the Cypher type system, protocol versioning, authentication and TLS via certificates.
-For Neo4j Clusters, Bolt provides smart client routing with load balancing and failover.
-
-The binary protocol is enabled in Neo4j by default, so you can use any language driver that supports it.
-
-Neo4j officially provides drivers for link:/developer/dotnet/[.NET], link:/developer/java/[Java], link:/developer/spring-data-neo4j/[Spring], link:/developer/javascript/[JavaScript], link:/developer/go/[Go], and link:/developer/python/[Python].
-
-// You can find detailed information about the official drivers in the {drivermanual}[Neo4j Driver Manual^].
-
-_For more details on the protocol implementation, see the https://github.com/neo4j-contrib/boltkit[implementers documentation^]._
-
-[#neo4j-drivers]
-== All Neo4j Drivers
-
-Thanks to the Neo4j contributor community, there are additionally drivers for almost every popular programming language,
-most of which mimic existing database driver idioms and approaches.
-Get started with your stack now, see the dedicated page for more detail.
-
-[cols="5*",width=100]
-|===
-| link:/developer/java/[icon:check-circle-o[] Java]
-| link:/developer/spring-data-neo4j/[icon:check-circle-o[] Spring]
-| link:/developer/neo4j-ogm/[icon:check-circle-o[] Neo4j-OGM]
-| link:/developer/dotnet/[icon:check-circle-o[] .NET]
-| link:/developer/javascript/[icon:check-circle-o[] JavaScript]
-| link:/developer/python/[icon:check-circle-o[] Python]
-| link:/developer/go/[icon:check-circle-o[] Go]
-| link:/developer/ruby/[Ruby]
-| link:/developer/php/[PHP]
-| link:/developer/erlang-elixir/[Erlang/Elixir]
-| link:/developer/perl/[Perl]
-| link:/developer/c/[C/C++]
-| link:/developer/clojure/[Clojure]
-| link:/developer/haskell/[Haskell]
-| link:/developer/r/[R]
-|===
-
-If you are new to development, we recommend the https://www.jetbrains.com/products.html[Jetbrains IDE^] for a good developer experience, which also comes with a link:/blog/jetbrains-ide-plugin-graph-database/[Neo4j Database plugin^].
-
-include::example-project.adoc[tags=intro-app-project,lines=21..35]
-
-link:/developer/example-project/[Read More^]
-
-[#http-api]
-== Using the HTTP API
-
-[NOTE]
---
-The HTTP API is available in Community Edition and Enterprise Edition, but not in AuraDB
---
-
-If you want to access Neo4j programmatically, you can also use the HTTP-API, which allow you to:
-
-* POST one or more Cypher statements with parameters
-* Keep transactions open over multiple requests
-* Choose different result formats
-
-Let's look at one of the underlying remote API endpoint that Neo4j offers to execute queries.
-These APIs can be then used directly via a HTTP library or a driver for your language.
-
-A simple HTTP Cypher request, executable in the Neo4j Browser would look like this:
-
-[source, json]
-----
-:POST /db/data/transaction/commit {"statements":[
-      {"statement":"CREATE (p:Person {firstName: $name}) RETURN p",
-       "parameters":{"name":"Daniel"}}
-    ]}
-----
-
-Some of the language drivers use the HTTP API under the hood, but make them available in a more convenient way.
-
-[.ad]
-== link:https://graphacademy.neo4j.com/[Learn with GraphAcademy^]
-
-Learn everything you need to know to build an application on top of Neo4j with free, hands-on courses from link:https://graphacademy.neo4j.com/[Neo4j GraphAcademy^].
-
-link:https://graphacademy.neo4j.com/?ref=guides[Learn more^,role=button]
-link:https://graphacademy.neo4j.com/categories/?ref=guides[or view courses by category^]
diff --git a/modules/ROOT/pages/life-sciences-and-healthcare.adoc b/modules/ROOT/pages/life-sciences-and-healthcare.adoc
deleted file mode 100644
index a3b4d906..00000000
--- a/modules/ROOT/pages/life-sciences-and-healthcare.adoc
+++ /dev/null
@@ -1,366 +0,0 @@
-= Neo4j Life Sciences and Healthcare Network
-
-image:{img}icon-guide-theory.png[float=left]
-
-== Neo4j Use Cases in Life Sciences and Healthcare
-
-If you work in biology, biochemistry, pharmaceuticals, healthcare and other life sciences, you know that you work with highly-connected information. Unfortunately, many scientists still use relational databases and spreadsheets as their daily tools.
-
-
-Here we want to present you with an alternative. Managing, storing and querying connected information is natural to a graph database like Neo4j. Learn how your research and practitioner colleagues utilized Neo4j to draw new insights or just be more efficient in their daily work.
-
-
-It started a while time ago in 2012 with a http://neo4j.com/blog/graph-databases-in-life-sciences-workshop/[workshop at the University of Ghent] bringing together people from the field with graph database experts.
-
-Following that fruitful exchange we started the http://groups.google.com/group/neo4j-biotech[Neo4j-Biotech Google Group] to encourage sharing and collaboration on that topic. If you are not yet a member, please join today.
-
-Now we want to take it to the next level by providing you with a platform to present your projects and papers both here and on our blog, and giving you the opportunity to connect with other Neo4j users in your field.
-
-
-If you are taking your first steps towards using a graph database, we offer support to jumpstart your efforts.
-// a dedicated Accelerator Program
-
-////
-Create a good offering to help them get started, problem solving etc
-Incentive to reach out to us
-Link to papers
-Publication of blog posts on neo4j.com
-benefits of using a graph database over e.g. a relational databases
-examples e.g. for data models
-Note email group, neo4j-contact
-Talk about the planned workshop for 2017
-////
-
-toc::[]
-
-=== Why use a Graph Database
-
-++++
-
    
-
-
    
-++++
-
-
-// === How to share your work/paper
-
-=== Graph Databases in Life and Health Sciences Workshop: Berlin, 21 June 2017
-
-We are very pleased to announce our second workshop for researchers interested in sharing and learning about Graph Databases in Life and Health Sciences.
-
-We are inviting researchers, practitioners and developers to present and attend.
-
-More details, as well as registration information, can be found https://www.eventbrite.com/e/neo4j-life-health-sciences-day-berlin-tickets-33238223421[here].
-
-
-// If you want to participate or submit a talk or paper, please let us know via devrel@neo4j.com.
-
-
-In our http://neo4j.com/blog/graph-databases-in-life-sciences-workshop/[past workshop in Ghent], we had topics covering
-
-* Neo4j in metaproteonomics
-* Graph databases in cancer research
-* Project collaboration networks and recommendations
-* Detailed studies of citation graphs
-* Connecting protein databases in a large graph model
-* "Reactome" database of human protein interaction pathways
-
-=== Life Sciences and Healthcare Accelerator Program
-
-
-The Neo4j Life Sciences and Healthcare Accelerator Program is designed to help researchers and practitioners in life sciences and healthcare-related sciences make sense of their data using Neo4j. Whether you are analyzing genome data, combining protein databases, investigating drug interactions or supporting practitioners with research or clinical information processing we want to help you find insights in connected (meta-)data.
-
-
-If you are accepted into the program, you will receive 1-on-1 assistance from Neo4j engineers to help you with data modeling, data import, writing Cypher queries or anything else that we can to make you successful with Neo4j.
-
-
-To get started just tell us about your project and how you think we might be able to help you.
-
-
-https://goo.gl/forms/T9wkIZVms1XE7kNE3[Apply Here,role="button apply"]
-
-
-=== Featured Projects
-
-
-==== The Hetnet Awakens - Understanding Disease Through Data Integration and Open Science
-
-_Daniel Himmelstein_
-
-Daniel Himmelstein’s Thesis Seminar for his PhD in Biological & Medical Informatics at UCSF.
-
-Here are the https://slides.com/dhimmel/thesis-seminar[slides] and an http://blog.dhimmel.com/phd-exhibit[online adaptation of the PhD Exhibit].
-Daniel was also interviewed on our http://blog.bruggen.com/2016/08/podcast-interview-with-daniel.html[Graphistania Podcast] and created a fun http://neo4j.com/graphgist/c4eab62c-7f5e-4e17-8f75-811d65d83127[Graph Gist as live documentation].
-
-++++
-
    
-
-
    
-++++
-
-
-==== Proteomics and Graph Databases, the symbiosis of associations
-
-_Alejandro Brenes Murillo_
-
-The proteome is the entire set of proteins that are produced or modified by an organism. It is an element that varies with time, stress, environmental conditions or distinct requirements that a cell might have. Join this talk by Alejandro Brenes Murillo to see how graph databases can be useful for proteome analysis.
-
-
-At the Lamond Lab in the University of Dundee, scientists are interested in modelling and understanding protein behaviour under different conditions and dimensions of analysis.
-
-
-In order to achieve this goal, they use graph databases to integrate and model the proteomics data, and study its effect on a specific proteome. The dimensions of analysis are multiple, yet be it turnover, localisation, cell cycle, protein complexes or biological response to stimuli, discovering the behaviour of proteins is key to understanding how organisms function, and how disease affects them.
-
-image::{img}alejandro_proteomics.jpg[link="https://skillsmatter.com/skillscasts/9246-proteomics-and-graph-databases-the-symbiosis-of-associations",width=560,window="_blank"]
-
-
-==== Big Data in Genomics: How Neo4j helps to develop new drugs
-
-_Martin Preusse_
-
-Biomedical research generates vast amounts of data. New experimental technologies like DNA sequencing, metabolomics and proteomics drive the fast growth of available information and lead to a better understanding of the molecular organization of life.
-
-
-But with big data comes a big question: How do we transform unstructured data into actionable knowledge? In the case of biomedical research, the key problem is to integrate the large pile of highly heterogenous data and use it for personalized therapies and drug development. Graph databases are an ideal way to represent biomedical knowledge and offer the necessary flexibility to keep up with scientific progress. A well-designed data model and Cypher queries can deliver in seconds what previously took days of manual analysis.
-
-image::{img}preusse_genomics.jpg[link="https://skillsmatter.com/skillscasts/7302-big-data-in-genomics-how-neo4j-helps-to-develop-new-drugs",width=560,window="_blank"]
-
-==== Building a Repository of Biomedical Ontologies with Neo4j
-
-_Simon Jupp_
-
-In this lightning talk from GraphConnect Europe 2016, Simon Jupp of the European Bioinformatics Institute discusses the application they built to track ontologies. He also discusses why they chose Neo4j over various RDF and semantic web technologies, and provides some example queries.
-
-++++
-
    
-
-
    
-++++
-
-==== Data Management in Systems Biology & Medicine
-
-_Irina Balaur, EISBM_
-
-An Integrative Framework for Data Management in Systems Biology and Medicine: Strategies for personalised medicine involve integration of large amounts of biomedical data, specific to multiple spatial and temporal scales, (including molecular data and patient clinical data). We have been developing a graph-database approach implemented in Neo4j to facilitate management (integration, exploration, visualisation, interpretation) of diverse types of biological and biomedical data.
-
-
-++++
-
    
-
-
    
-++++
-
-====  Graphs Are Feeding The World
-
-_Tim Williamson, Data Scientist, Monsanto_
-
-Presentation at GraphConnect SF 2015.
-
-++++
-
    
-
-
    
-++++
-
-
-==== Graph Databases in Life Sciences: Bringing Biology Back to Its Nature
-
-_Thilo Muth_
-
-
-Today's life science research is about genes, proteins, metabolites, relationships, interactions and biological networks. Data storing and mining brings a huge potential for biologists, however classical storage formats such as SQL and Excel involve various issues, such as scalability and performance problems with data growth, complexity and accessibility. Finally, most of the storage models are far from biological reality: Graph databases and Neo4j meet the need in life sciences for an appropriate data and database model.
-
-++++
-
    
-
-
    
-++++
-
-
-==== Open Tree Of Life
-
-image::opentree-final-logo.png[float="left"]
-
-*The tree of life* links all biodiversity through a shared evolutionary history.
-This project will produce the first online, comprehensive first-draft tree of all 1.8 million named species, accessible to both the public and scientific communities.
-
-Assembly of the tree will incorporate previously-published results, with strong collaborations between computational and empirical biologists to develop, test and improve methods of data synthesis.
-
-This initial tree of life will not be static; instead, we will develop tools for scientists to update and revise the tree as new data come in.
-Early release of the tree and tools will motivate data sharing and facilitate ongoing synthesis of knowledge.
-
-
-*Biological research* of all kinds, including studies of ecological health, environmental change, and human disease, increasingly depends on knowing how species are related to each other.
-
-Yet there is no single resource that unites knowledge of the tree of life.
-Instead, only small parts of the tree are individually available, generally as printed figures in journal articles.
-
-This project will provide the global community of scientists who study the tree of life with a means to share and combine their results, and will enable large-scale studies of Earth’s biodiversity.
-It will also create a resource where students, educators and citizens can go to explore and learn about life’s evolutionary history.
-
-Read more on the http://blog.opentreeoflife.org/tag/neo4j/[OpenTreeOfLife Blog]
-
-++++
-
    
-
-
    
-
    0606 - Open Tree of Life and Neo4j from Neo Technology on Vimeo.
    
-++++
-
-
-////
-=== Project Catalogue
-
-
-[cols="2,1,3",opts="header"]
-|===
-| Name
-| Contact
-| Description
-
-
-| http://knowing-health.com/[Startup: KNOWING – Turning Big Data into Personalized Therapies]
-| Martin Preusse
-| Developing a software that can be used for result integration of any type of research data as well as be used to harvest data to identify biomarker and drug targets for personalized therapies.
-
-|===
-
-////
-
-=== Publications
-
-[[publications]]
-[cols="3,1,3,3",options="header"]
-|===
-| Title
-| Year
-| Authors
-| Affiliation
-
-| https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx237[The Proteins API: accessing key integrated protein and genome information]
-| 2017
-| A. Nightingale, R. Antunes, E. Alpi, B. Bursteinas, L. Gonzales, W. Liu, J. Luo, G. Qi, E. Turner, and M. Martin
-| EMBL-EBI, Wellcome Genome Campus, UK
-
-| http://biorxiv.org/content/biorxiv/early/2016/05/26/055525.full.pdf[Knowledge.Bio: A Web application for exploring, building and sharing webs of biomedical relationships mined from PubMed]
-| 2016
-| R. Bruskiewich, K. Huellas-Bruskiewicz, F. Ahmed, R. Kaliyaperumal, M. Thompson, E. Schultes, K. M. Hettne, A. I. Su, and B. M. Good
-| Department of Human Genetics, Leiden University Medical Center, The Netherlands
-
-| https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btw731/2557691/Recon2Neo4j-applying-graph-database-technologies[Recon2Neo4j: Applying graph database technologies for managing comprehensive genome-scale networks]
-| 2016
-| I. Balaur, A. Mazein, M. Saqi, A. Lysenko, C. J. Rawlings, and C. Auffray
-| European Institute for Systems Biology and Medicine (EISBM), France
-
-| https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1394-x[STON: exploring biological pathways using the SBGN standard and graph databases]
-| 2016
-| V. Touré, A. Mazein, D. Waltemath, I. Balaur, M. Saqi, R. Henkel, J. Pellet, and C. Auffray
-| European Institute for Systems Biology and Medicine (EISBM), France
-
-| https://www.ncbi.nlm.nih.gov/pubmed/26998997[miTALOS v2: Analyzing Tissue Specific microRNA Function]
-| 2016
-| M. Preusse, F. J. Theis, and N. S. Mueller
-| Institute of Computational Biology, Helmholtz Zentrum München, Germany
-
-| https://www.ncbi.nlm.nih.gov/pubmed/27196054[An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations]
-| 2016
-| J. Mullen, S. J. Cockell, P. Woollard, and A. Wipat
-| Newcastle University, United Kingdom
-
-| https://www.ncbi.nlm.nih.gov/pubmed/26708334[HitWalker2: visual analytics for precision medicine and beyond]
-| 2016
-| D. Bottomly, S. K. McWeeney, and B. Wilmot
-| Knight Cancer Institute, Oregon Health and Science University, USA
-
-| https://www.ncbi.nlm.nih.gov/pubmed/26657893[HRGRN: A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks]
-| 2016
-| X. Dai, J. Li, T. Liu, and P. X. Zhao
-| Plant Biology Division, The Samuel Roberts Noble Foundation, USA
-
-| https://www.ncbi.nlm.nih.gov/pubmed/27462371[Representing and querying disease networks using graph databases]
-| 2016
-| A. Lysenko, I. A. Roznovăţ, M. Saqi, A. Mazein, C. J. Rawlings, and C. Auffray
-| European Institute for Systems Biology and Medicine (EISBM), France
-
-| https://www.ncbi.nlm.nih.gov/pubmed/27587666[PanTools: representation, storage and exploration of pan-genomic data]
-| 2016
-| S. Sheikhizadeh, M. E. Schranz, M. Akdel, D. de Ridder, and S. Smit
-| Bioinformatics Group, Wageningen University, The Netherlands
-
-| https://www.ncbi.nlm.nih.gov/pubmed/27627442[EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer]
-| 2016
-| I. Balaur, M. Saqi, A. Barat, A. Lysenko, A. Mazein, C. J. Rawlings, H. J. Ruskin, and C. Auffray
-| European Institute for Systems Biology and Medicine (EISBM), France
-
-| http://www.ncbi.nlm.nih.gov/pubmed/26272981[cyNeo4j: connecting Neo4j and Cytoscape]
-| 2015
-| G. Summer, T. Kelder, K. Ono, M. Radonjic, S. Heymans, and B. Demchak
-| Center for Heart Failure Research (CARIM), University Hospital Maastricht, The Netherlands
-
-| https://www.thinkmind.org/download.php?articleid=dbkda_2015_9_50_90009[Towards Implementing Semantic Literature-Based Discovery with a Graph Database]
-| 2015
-| D. Hristovski, A. Kastrin, D. Dinevski, and T. C. Rindflesch
-| Faculty of Medicine, University of Ljubljana, Slovenia
-
-| http://dl.acm.org/citation.cfm?id=2918100[Using Neo4j for Mining Protein Graphs: A Case Study]
-| 2015
-| D. Hoksza and J. Jelinek
-| Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic
-
-| http://pubs.acs.org/doi/abs/10.1021/pr501246w[The MetaProteomeAnalyzer: A Powerful Open-Source Software Suite for Metaproteomics Data Analysis and Interpretation]
-| 2015
-| T. Muth, A. Behne, R. Heyer, F. Kohrs, D. Benndorf, M. Hoffmann, M. Lehtevä, U. Reichl, L. Martens, and E. Rapp
-| Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
-
-| https://www.ncbi.nlm.nih.gov/pubmed/26383775[SimiRa: A tool to identify coregulation between microRNAs and RNA-binding proteins]
-| 2015
-| M. Preusse, C. Marr, S. Saunders, D. Maticzka, H. Lickert, R. Backofen, and F. Theis
-| Helmholtz Zentrum München, Institute of Computational Biology, Germany
-
-| https://www.ncbi.nlm.nih.gov/pubmed/26262393[Constructing a Graph Database for Semantic Literature-Based Discovery]
-| 2015
-| D. Hristovski, A. Kastrin, D. Dinevski, and T. C. Rindflesch
-| Faculty of Medicine, University of Ljubljana, Slovenia
-
-| https://www.ncbi.nlm.nih.gov/pubmed/25708381[A systems biology approach toward understanding seed composition in soybean]
-| 2015
-| L. Li, M. Hur, J. Y. Lee, W. Zhou, Z. Song, N. Ransom, C. Y. Demirkale, D. Nettleton, M. Westgate, Z. Arendsee, V. Iyer, J. Shanks, B. Nikolau, and E. S. Wurtele
-| Department of Genetics, Development and Cell Biology, Iowa State University, USA
-
-| https://www.ncbi.nlm.nih.gov/pubmed/25754863[Combining computational models, semantic annotations and simulation experiments in a graph database]
-| 2015
-| R. Henkel, O. Wolkenhauer, and D. Waltemath
-| Department of Computer Science, University of Rostock, Germany
-
-| https://www.ncbi.nlm.nih.gov/pubmed/26305513[An alternative database approach for management of SNOMED CT and improved patient data queries]
-| 2015
-| W. S. Campbell, J. Pedersen, J. C. McClay, P. Rao, D. Bastola, and J. R. Campbell
-| University of Nebraska Medical Center, Department of Pathology and Microbiology, US
-
-| https://www.ncbi.nlm.nih.gov/pubmed/25520553[Semantically linking in silico cancer models]
-| 2014
-| D. Johnson, A. J. Connor, S. McKeever, Z. Wang, T. S. Deisboeck, T. Quaiser, and E. Shochat
-| Department of Computing, Imperial College London, London, UK
-
-| http://www.sciencedirect.com/science/article/pii/S1574954114001125[Global biotic interactions: An open infrastructure to share and analyze species-interaction datasets]
-| 2014
-| J. H. Poelen, J. D. Simons, and C. J. Mungall
-| Center for Coastal Studies Natural Resource Center, USA
-
-| http://bioinformatics.oxfordjournals.org/content/early/2013/10/21/bioinformatics.btt549.full[Are graph databases ready for bioinformatics?]
-| 2013
-| Christian Theil Have and Lars Juhl Jensen
-| Department of Metabolic Genetics, University of Copenhagen, Denmark
-
-|===
-
-++++
-
-
-
-
-++++
diff --git a/modules/ROOT/pages/live-chat.adoc b/modules/ROOT/pages/live-chat.adoc
deleted file mode 100644
index 89d986e8..00000000
--- a/modules/ROOT/pages/live-chat.adoc
+++ /dev/null
@@ -1,50 +0,0 @@
-= Neo4j Live Streams
-:slug: live-stream
-:section: Documentation and Resources
-:category: documentation
-:tags: live-stream, twitch, youtube, discord, community
-:page-player: https://player.twitch.tv/?channel=neo4j&muted=false&autoplay=true&parent=twitter.com&parent=cards-dev.twitter.com&parent=cards-frame.twitter.com
-
-// image::https://blog.twitch.tv/assets/uploads/03-glitch.jpg[width="300px",float="right"]
-++++
-
-
    
-
-++++
-
-Join the discussion
-++++
-
-
-
-
-
-
-
-++++
diff --git a/modules/ROOT/pages/manage-multiple-databases.adoc b/modules/ROOT/pages/manage-multiple-databases.adoc
deleted file mode 100644
index 5afa8baa..00000000
--- a/modules/ROOT/pages/manage-multiple-databases.adoc
+++ /dev/null
@@ -1,260 +0,0 @@
-= Managing Multiple Databases in Neo4j
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:neo4j-version: 4.0
-:category: cluster
-:tags: administration, multi-database, commands, cypher, system-database, manage-databases
-:description: In this guide, we will walk through how to manage multiple database within the Neo4j DBMS.
-:page-type: Tutorial
-:page-deprecated-title: the Neo4j Operations Manual
-:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/manage-databases/
-
-// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) downloaded and installed.
-It helps to have read the section on xref:graph-database.adoc[graph databases].
-
-[role=expertise {level}]
-{level}
-
-[#multi-db]
-In Neo4j (v4.0+), we can create and use more than one active database at the same time.
-This works in standalone and causal cluster scenarios and allows us to maintain multiple, separate graphs in one installation.
-
-When we create a database, Neo4j will initially create a system database and a default database.
-The system database is named `system` and contains the overall information that applies across databases - managing administration of individual databases (stopping and starting) and maintaining user privileges (security roles and privileges).
-The default database is named `neo4j` (can be changed) and is where we can store and query data in a graph and integrate with other applications and tools.
-We can also create additional databases, as needed, to store other graphs and different data that may be unrelated to any of our other databases.
-
-[#setup-db]
-== System setup
-
-If you haven't already, link:/download/[download^] Neo4j.
-
-We will need to have a database running and open link:/developer/neo4j-browser/[Neo4j Browser] to walk through this guide.
-If you are unsure how to create and start a database, step-by-step instructions for doing so in Neo4j Desktop are provided in link:/developer/neo4j-desktop[this guide].
-
-[#initial-db]
-== Reviewing the initial databases
-
-As mentioned earlier, when Neo4j is installed and an instance created, it will be initiated with two databases - a `system` database and a default (`neo4j`) database.
-Launching https://localhost:7474/[Neo4j Browser^] will automatically point us to the `neo4j` default database, shown by the `neo4j$` prompt in the command line.
-
-image::{img}4dot0_neo4j_db_prompt.jpg[role="popup-link"]
-
-If we want to see the system information (view, create/delete, manage databases), we will need to switch to the `system` database.
-We can do that with the `:use` command then telling it which database we want.
-
-Command: `:use system`
-
-.Results:
-image:{img}4dot0_use_db.jpg[role="popup-link"]
-
-Now we can run a command to see the databases created with the instance.
-The `SHOW DATABASES` command will display all databases in our instance (or databases across instances in a cluster) along with address, role, requested and current statuses, any errors, and which database is default.
-
-****
-[NOTE]
-Remember, at this point, we are only expecting the `system` database and the default (`neo4j`) database.
-****
-
-image::{img}4dot0_show_dbs.jpg[role="popup-link"]
-
-That looks as we expected.
-Now let's add another database to the list.
-
-[#create-new-db]
-== Creating a new database (Enterprise only)
-
-To add a database to our instance, we can use the `CREATE DATABASE` command.
-We are going to use an example called `movieGraph`, but you could choose any name for the database.
-
-image::{img}4dot0_create_new_db.jpg[role="popup-link"]
-
-****
-[NOTE]
-Database naming is not case-sensitive.
-Creating the database `movieGraph` will show `moviegraph` as the name in the system information, but you can change to the database using either `movieGraph` or `moviegraph`.
-Either will connect to the same database, and it will not allow you to create another database with any other combination of capitalized/lower-case letters (i.e. MovieGraph, moviegraph, mOvIeGrApH, etc).
-****
-
-While the result message does not seem convincing that our creation command worked, we can verify by running the `SHOW DATABASES` command again to see our new database show up in the list!
-
-image::{img}4dot0_show_created_db.jpg[role="popup-link"]
-
-That looks good.
-We can switch to our new database to start working with that one specifically (command is `:use movieGraph`).
-
-image::{img}4dot0_use_created_db.jpg[role="popup-link"]
-
-[#operate-movie-db]
-== Loading data and working with our movieGraph database
-
-Next, we will load some sample data into our `movieGraph` database and work with it.
-Before we do that, let us verify that our database is truly empty by viewing the schema using the `CALL db.schema.visualization()` procedure.
-
-image::{img}4dot0_check_schema.jpg[role="popup-link"]
-
-****
-[NOTE]
-Previous versions of Neo4j used the `CALL db.schema()` procedure, which was converted to the newer procedure shown above.
-****
-
-No node or relationship data is shown in our results, so the database is empty.
-Once we add data in a bit, we will see a data model visualization in the results.
-We can do another quick test by writing a Cypher query to return any nodes and relationships.
-
-Command: `MATCH (node)-[rel]-(other) RETURN node, rel, other`
-
-.Results:
-image:{img}4dot0_schema_test.jpg[role="popup-link"]
-
-All clear.
-Now let us add some data.
-
-[#load-movie-data]
-=== Load movie data
-
-We will use the small data set for movies that Neo4j users may already be familiar with.
-To load, type the command `:play movies` into the command line and execute it.
-A guide will display in the result pane.
-
-image::{img}4dot0_play_movies.jpg[role="popup-link"]
-
-We can navigate to the second slide by clicking the arrow on the right side of the pane, and a slide with a long Cypher query should appear.
-
-image::{img}4dot0_run_movie_query.jpg[role="popup-link"]
-
-The Cypher query on the right will be outlined with a grey dotted line.
-We can click that query, and it will copy/paste into the command line.
-Click to execute the query, which will return some results confirming that data was loaded.
-
-image::{img}4dot0_movie_query_results.jpg[role="popup-link"]
-
-****
-[NOTE]
-Colors and location of the graph within the result pane might vary.
-However, if the data is out-of-sync anytime throughout this guide, you can follow the steps to clean out the instance at the xref:manage-multiple-databases.adoc#multidb-cleanup[bottom of this guide] and try the data load again.
-****
-
-We can also check our schema procedure again to see how data is organized in the database (data model).
-Use the command `CALL db.schema.visualization()` again and execute it to see that we have `Person` nodes and `Movie` nodes that are connected by several different kinds of relationships.
-
-image::{img}4dot0_check_movie_schema_again.jpg[role="popup-link"]
-
-We can now run any queries against our movie data that we would like.
-For instance, using a generic query like the one below below will search for nodes that have any relationships to other nodes.
-
-[source,cypher]
-----
-MATCH (node)-[rel]->(other)
-RETURN node, rel, other
-----
-
-Though we can query and work with our movie data, we want to work with the multi-database feature and see that the data in one database is not accessible in the other database.
-To do that, we need to load some different data in another database.
-
-[#operate-neo4j-db]
-== Loading data and working with our neo4j database
-
-Let us go back to our default `neo4j` database and load the Northwind retail system data there.
-This way, when we look at our databases (`movieGraph` and `neo4j`), we will see two completely different sets of data.
-The `:use neo4j` command will switch us to that database and allow us to load there.
-
-image::{img}4dot0_use_neo4j_db.jpg[role="popup-link"]
-
-To confirm there is no data in this database currently, we can run the `CALL db.schema.visualization()` procedure against `neo4j`.
-
-image::{img}4dot0_check_neo4j_db_schema.jpg[role="popup-link"]
-
-Everything looks clear.
-We can also run the test query from above, if we want to confirm that way.
-Now we are ready to add some data.
-
-[#load-northwind-data]
-=== Loading northwind data
-
-We will use the Browser guide `:play northwind` that has built-in Cypher queries we can run to load retail suppliers, products, and product categories.
-
-image::{img}4dot0_play_northwind.jpg[role="popup-link"]
-
-Click the arrow on the right side of the result pane to get to the next slide in the guide, and there will be 3 load statements and 3 indexing statements.
-
-image::{img}4dot0_northwind_data_load.jpg[role="popup-link"]
-
-Execute all 6 of those to populate the nodes, then click the right arrow on the guide once more to progress to the next slide.
-On this slide, we have 2 more statements to find nodes and create relationships between them.
-
-image::{img}4dot0_northwind_load_rels.jpg[role="popup-link"]
-
-Click and run both statements, then we can check that our data loaded correctly by running the schema procedure again.
-We should expect `Supplier` nodes with a relationship to `Product` nodes with a relationship to `Category` nodes.
-
-image::{img}4dot0_check_neo4j_schema_again.jpg[role="popup-link"]
-
-****
-[NOTE]
-Colors and location of the graph within the result pane might vary.
-However, if the data is out-of-sync anytime throughout this guide, you can follow the steps to clean out the instance at the xref:manage-multiple-databases.adoc#multidb-cleanup[bottom of this guide] and try the data load again.
-****
-
-That is how we expected it!
-We can run our generic test query again to retrieve some of our Northwind data.
-
-[source,cypher]
-----
-MATCH (node)-[rel]->(other)
-RETURN node, rel, other
-----
-
-Notice that we do not see any of our movie database entities or relationships in the data model or in our test query.
-Those are in our `movieGraph` database and are completely separate.
-If those nodes and relationships existed in this graph, the test query would retrieve them because we do not specify any certain types of nodes and relationships in the search.
-
-We can do one more step to verify Northwind data is not in our `movieGraph` database either.
-
-[#navigate-multidb]
-== Navigating between datasets and databases
-
-Let us switch back to our `movieGraph` database one more time with the `:use movieGraph` command.
-Next, we run the familiar `CALL db.schema.visualization()` procedure to pull back our data model.
-
-image::{img}4dot0_check_movie_schema_again.jpg[role="popup-link"]
-
-We see that there is no Northwind data in this graph.
-That looks good.
-Our generic test query could be another verification method, as well.
-
-We can continue to operate each of these graphs separately and yet connect to the disparate data sets from the same Neo4j installation without separate instances.
-
-[#multidb-cleanup]
-== Cleaning out database within same instance
-
-One final administrative difference is how to completely clean out one database without impacting the entire instance with multiple databases.
-When dealing with a single instance and single database approach, users can delete the entire instance and start fresh.
-However, with multiple databases, we cannot do that unless we are comfortable losing everything from our other databases in that instance.
-
-The approach is similar to other DBMSs where we can drop and recreate the database, but retain everything else.
-Cypher's command for this is `CREATE OR REPLACE DATABASE `.
-This will create the database (if it does not already exist) or replace an existing database with a clean one.
-
-For example, when working through these examples, we may alter a load query incorrectly or accidentally add or delete data that we need.
-In this case, deleting all the data will not completely wipe indexes or the ghost entities for the data model.
-Instead, we can use the `CREATE OR REPLACE DATABASE` command and start over.
-
-Command: `CREATE OR REPLACE DATABASE neo4j`
-
-.Results:
-image:{img}4dot0_drop_recreate_db.jpg[role="popup-link"]
-
-[#resources]
-== Resources
-
-* link:/docs/operations-manual/current/manage-databases/[Documentation: Multi-database^]
diff --git a/modules/ROOT/pages/memory-management.adoc b/modules/ROOT/pages/memory-management.adoc
deleted file mode 100644
index 82abd952..00000000
--- a/modules/ROOT/pages/memory-management.adoc
+++ /dev/null
@@ -1,161 +0,0 @@
-= How To: Manage Memory in Neo4j
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: performance
-:tags: memory, configuration, transaction, limit, queries
-:description: In this guide, we will learn how to both measure and restrict memory usage in Neo4j.
-:page-deprecated-title: the Neo4j Operations Manual
-:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/performance/memory-configuration/
-
-// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Please have link:/download/[Neo4j^] (version 4.1 or later) downloaded and installed.
-Familiarity with the link:/developer/cypher-query-language/[Cypher query language] is required to understand the examples used in this guide.
-
-[role=expertise {level}]
-{level}
-
-In Neo4j (v4.1+), we can measure and restrict the amount of memory used by transactions running on the server.
-This feature can be used to avoid `OutOfMemory` exceptions when running our workloads and ensure fairness across databases and transactions.
-
-[#movies-dataset]
-== Movie dataset
-
-The examples in this guide are based on the built-in movies dataset.
-This can be loaded by running the `:play movies` command in the Neo4j Browser and following the instructions to import the dataset.
-We can see a sample of the graph in the Neo4j Browser visualization below:
-
-image::{img}memory-management-movies-graph.jpg[role="popup-link"]
-
-[#measure-memory-usage-tx]
-== Measuring memory usage of one transaction
-
-We can measure the memory usage of individual Cypher queries when link:{cyphermanual}/query-tuning/how-do-i-profile-a-query/[profiling them^] using the `PROFILE` clause.
-
-The following query takes all pairs of people and returns the longest path between the pair of nodes up to a maximum distance of 5 hops.
-
-[source,cypher]
-----
-PROFILE
-MATCH (p1:Person), (p2:Person)
-WHERE p1 <> p2
-MATCH path = (p1)-[*..5]-(p2)
-WITH p1, p2, path
-ORDER BY p1, p2, length(path) DESC
-WITH p1, p2, collect(path)[0] AS path
-RETURN p1.name, p2.name, [rel in relationships(path) | type(rel)];
-----
-
-We can run this query in the link:/developer/neo4j-browser/[Neo4j Browser], which returns a visual representation of the query plan that contains memory usage information.
-We can see the output from running this query in the image below:
-
-image::{img}query-plan-with-memory.png[role="popup-link",width=200]
-
-The total memory usage of 17,062,912 bytes is included on the `ProduceResults` operator.
-That memory usage is broken down across the following operators:
-
-* `OrderedAggregation` - 376,272 bytes
-* `Sort` - 16,687,456 bytes
-* `NodeByLabelScan` - 64 bytes
-
-Alternatively, we can run the query in the link:{opsmanual}/tools/cypher-shell/[Cypher Shell^], which returns the following output (truncated for brevity):
-
-[options="header"]
-|===
-| Plan      | Statement   | Version      | Planner | Runtime     | Time | DbHits | Rows | Memory (Bytes)
-| "PROFILE" | "READ_ONLY" | "CYPHER 4.1" | "COST"  | "PIPELINED" | 68   | 484361 | 7890 | 17062912
-|===
-
-[options="header", separator=¦]
-|===
-¦ Operator                               ¦ Estimated Rows ¦ Rows   ¦ DB Hits ¦ Time (ms) ¦ Memory (Bytes)
-¦ +ProduceResults@memorymanagement       ¦              2 ¦   7890 ¦       0 ¦    16.498 ¦
-¦ +Projection@memorymanagement           ¦            2 ¦   7890 ¦   46424 ¦    48.497 ¦
-¦ +Projection@memorymanagement           ¦              2 ¦   7890 ¦       0 ¦     5.987 ¦
-¦ +OrderedAggregation@memorymanagement   ¦           2 ¦   7890 ¦       0 ¦    26.009 ¦         376272
-¦ +Projection@memorymanagement           ¦           5 ¦  33440 ¦       0 ¦    54.526 ¦
-¦ +Sort@memorymanagement                 ¦              5 ¦  33440 ¦       0 ¦    96.382 ¦       16687456
-¦ +Projection@memorymanagement           ¦               5 ¦  33440 ¦  134704 ¦           ¦
-¦ +Filter@memorymanagement               ¦           5 ¦  33440 ¦  114163 ¦           ¦
-¦ +VarLengthExpand(All)@memorymanagement ¦         256 ¦ 115305 ¦  188936 ¦           ¦
-|===
-
-The output from Cypher Shell contains the total memory usage information in a separate summary table, rather than including it as part of the final operator.
-The summary table contains a column indicating the memory usage of 17062184 bytes or 17MB.
-
-[#measure-memory-usage-server]
-== Measuring memory usage on server
-
-We can also measure the memory usage on the Neo4j server using the following procedures:
-
-|===
-| Procedure | Description
-|`CALL dbms.listPools()` | describes thread pool memory usage
-|`CALL dbms.listTransactions()` | describes memory usage by running transactions
-|`CALL dbms.listQueries()` | describes memory usage by running queries
-|===
-
-For example, we can see the memory usage when our all pairs of people query is running, by executing the following query:
-
-[source,cypher]
-----
-CALL dbms.listQueries()
-YIELD queryId, username, database, query, allocatedBytes
-RETURN queryId, username, database, query, allocatedBytes;
-----
-
-[options="header"]
-|===
-| queryId |	username |	database |	query |	allocatedBytes
-| "query-32" |	"neo4j"	| "memorymanagement" |	" PROFILE MATCH (p1:Person), (p2:Person) WHERE p1 <> p2 MATCH path = (p1)-[*..5]-(p2) WITH p1, p2, path ORDER BY p1, p2, length(path) DESC WITH p1, p2, collect(path)[0] AS path RETURN p1.name, p2.name, [rel in relationships(path) \| type(rel)];" |	3234176
-| "query-34" |	"neo4j" |	"neo4j" |	"CALL dbms.listQueries() YIELD queryId, username, database, query, allocatedBytes RETURN queryId, username, database, query, allocatedBytes" |	64
-|===
-
-At the time that we ran this query, our all pairs of people query was only using 3,234,176 bytes of memory out of the 17,062,912 that we know it uses in total.
-
-
-[#restrict-memory-usage]
-== Restricting memory usage
-
-We can restrict the amount of heap memory available to transactions by specifying the link:/docs/operations-manual/4.1/performance/memory-configuration/#memory-configuration-limit-transaction-memory[following config settings^] in `$NEO4J_HOME/neo4j.conf`.
-
-[options="header"]
-|===
-| Setting | Description
-|`dbms.memory.transaction.global_max_size` | configures the global maximum memory usage for all of the transactions running on the server.
-|`dbms.memory.transaction.database_max_size` | limits the transaction memory usage per database
-|`dbms.memory.transaction.max_size` | limits the memory usage per transaction
-|===
-
-++++
-
    
-
-
    
-++++
-
-If we want to restrict the amount of memory used by an individual transaction to 10MB, we can set the following config:
-
-.neo4j.conf
-[source,properties]
-----
-dbms.memory.transaction.max_size=10m
-----
-
-Our query from the <> section uses more memory than this, so if we re-run that query, we'll see the following error message:
-
-[source,text]
-----
-The allocation of 64.3 KiB would use more than the limit 10.0 MiB. Currently using 9.9 MiB. dbms.memory.transaction.max_size threshold reached
-----
-
-[#resources]
-== Resources
-
-* link:/docs/operations-manual/4.1/performance/memory-configuration/[Documentation: Memory configuration^]
diff --git a/modules/ROOT/pages/modeling-designs.adoc b/modules/ROOT/pages/modeling-designs.adoc
deleted file mode 100644
index 60979b04..00000000
--- a/modules/ROOT/pages/modeling-designs.adoc
+++ /dev/null
@@ -1,212 +0,0 @@
-= Modeling Designs
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: modeling
-:tags: graph-modeling, data-model, schema, model-design, modeling-decisions
-:description: In this section, you will learn how to represent graph data using a variety of modeling decisions. How you construct your data model can impact your queries and performance.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-modeling/modeling-designs/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-Our goal is to show you how to evaluate your model and make appropriate changes, so you can define the best solution for your use case and maximize the performance of your queries.
-
-.Prerequisites
-[abstract]
-You should understand link:/developer/graph-database/[what a graph database is] and know the components of the link:/developer/graph-database#property-graph[property graph data model]
-[role=expertise {level}]
-{level}
-
-[#model-impact]
-== Why the Data Model Makes a Difference
-
-As with any database, the data model that you design is important in determining the logic your queries and the structure of data in storage.
-This practice extends to graph databases, with one exception.
-Neo4j is schema-free, which means that your data model can adapt and change easily with your business.
-
-Need to start collecting a new field and capture new analysis?
-Or need to change the way you interpret a customer or other entity and modify its definition?
-Or regulation requires systems to capture less information or restrict readability (change data format/types)?
-
-You may have worked for a company where each area or department defines a domain differently.
-Take, for instance, a generic customer domain.
-To different areas within the business, a customer can be defined as different types of individuals.
-These definitions may also change over time or the company may decide to unify the meaning of a customer across departments.
-
-If you have worked with other types of databases, you will already be familiar with the development and administrative work that any of these scenarios entail.
-However, Neo4j allows you to effortlessly adjust detailed and broad changes across pieces or the entirety of the graph.
-Whether it is small changes over time or a broad definition that includes a variety of needed information about your entities, the database is able to handle it.
-It is simply up to the developers and architects to determine the structure of the data model and how to define entities for queries.
-
-In the next few paragraphs, we will introduce a few different ways to look at different data sets and show how each impacts queries and performance for traversing graph data.
-
-[#property-vs-relationship]
-== Property vs Relationship
-
-One of the earliest decisions you may encounter is whether to model something as a property on a node or as a relationship to a separate node.
-Take, for example, the data below modeling a movie genre as a property on the `Movie` node.
-
-image::{img}modeling_genre_property.jpg[role="popup-link"]
-
-To write a query finding the genre(s) of a particular movie is very simple.
-It would find the `Movie` node it wants to know about, then return the values listed in the genre property.
-However, to find out which movies share genres, you would need a much more complex query to find each `Movie` node, loop through each of the genres in the property array, and compare with each value in the second movie's property array of genres.
-This would take a toll on performance (nested looping and comparison of node properties), and the query would be much more complicated, as well.
-
-The code block below is what the syntax would look like for each query.
-You can see the shift in logic and complexity of the loop in the second query.
-
-[source, cypher]
-----
-//find the genres for a particular movie
-MATCH (m:Movie {title:"The Matrix"})
-RETURN m.genre;
-
-//find which movies share genres
-MATCH (m1:Movie), (m2:Movie)
-WHERE any(x IN m1.genre WHERE x IN m2.genre)
-AND m1 <> m2
-RETURN m1, m2;
-----
-
-Now, instead, if we were to model our movies and genres as separate nodes and create a relationship between the two, we would come up with a model something like the image below.
-
-image::{img}modeling_genre_node.jpg[role="popup-link"]
-
-This creates a completely separate entity (node) for the genre, allowing you to connect all the movies with a shared genre to that `Genre` node.
-Let us see how this changes our queries.
-To find the genres of a particular movie, it first needs to find the `Movie` node it is looking for (in this case, 'The Matrix'), then find the node that is connected to that movie through the `IN_GENRE` relationship.
-
-The biggest difference is in the syntax for the second query to find which movies share genres.
-It is much simpler than our earlier version because it uses a natural, graph pattern (entity-relationship-entity) to find the information needed.
-First, Cypher finds a movie and the genre it is related to, then looks for a second movie that is in that same genre.
-
-[source, cypher]
-----
-//find the genres for a particular movie
-MATCH (m:Movie {title:"The Matrix"}),
-      (m)-[:IN_GENRE]->(g:Genre)
-RETURN g.name;
-
-//find which movies share genres
-MATCH (m1:Movie)-[:IN_GENRE]->(g:Genre),
-      (m2:Movie)-[:IN_GENRE]->(g)
-RETURN m1, m2, g
-----
-
-Neither version of the data model is worse or better, but the 'best' option highly depends on the types of queries you intend to run against your data.
-
-If you plan to do analysis on individual items and return only details about that entity (like genres on a particular movie), then the first data model would serve perfectly well for your needs.
-However, if you need to run analysis to find common ground between entities or look at a group of nodes, then the second data model would definitely improve performance of those types of queries.
-
-[#complex-models]
-== Complex Data Structures
-
-As many of us can probably agree, not all data models are simple and straightforward.
-Data is messy, and the model must attempt to better-organize it to help us see patterns and make decisions.
-
-One excellent example of a complex data structure that is difficult to model is Marvel comic data.
-In the Marvel universe, there are comics that have characters who make appearances or play lead roles.
-Comics can be organized into a series of particular storylines or narratives for a certain time, and major events can take place in a comic that define a character path or series.
-Creators (including writers, illustrators, etc) are the authors of comics, defining storyline, character adaptations, and events that happen.
-Multiple creators can also participate interchangably to create a comic or series.
-
-This dataset already seems complicated, with several entities and relationships at work.
-It adds a new layer of complexity when trying to model the hierarchies and intermediate entities that exist here.
-
-If you have some time, you can view the full video link to https://player.vimeo.com/video/79399404[Peter's presentation^] on Vimeo, but we want to highlight two key challenges that Peter discusses in the data set.
-
-First, he found that comic characters tend to be extremely dynamic.
-Many characters cannot be identified by name or costume or any particular property, as all of those change often.
-
-The other problem Peter identified is that of chronology.
-For those new to the comic universe, some might want to determine where to start or what comic(s) come next.
-However, comic issues are not always sequentially numbered, and there are even some storylines that appear across multiple series and back again.
-This makes it incredibly difficult to separate certain blocks of stories or events, along with renditions of characters.
-
-=== Example: Hyperedges or Intermediate Nodes
-
-One modeling technique that is useful in this model is the concept of a hyperedge.
-Hyperedges (or intermediate nodes) are often created to model relationships that exist between more than two entities.
-They are often created to represent the connection of multiple entities at a point in time.
-
-A common example of this is a university course.
-There may be multiple offerings of the same course with the same instructor in the same building, etc.
-Each section of the class (or offering) would then become an instance of the course.
-
-The way Peter at Marvel handled hyperedges in their data is by creating an `Appearance` node that represents the intersection of a `Person` and an `Alias` at a particular time.
-This `Appearance` can be related to multiple `Moment` nodes where the person and alias appear as a unit.
-This is represented in the models shown below (also in the https://player.vimeo.com/video/79399404[video^]).
-
-image:{img}modeling_marvel_hyperedge_appearance.jpg[role="popup-link"]
-
-In a relational store, attempting to categorize and relate all of these complicated aspects would be extremely difficult and further complicate analysis and review of the data as a whole.
-The graph model allowed them to model this heavily dynamic universe and track all of the changing connections throughout their data.
-For this use case, graph was the perfect fit.
-
-[#model-time-versions]
-== Time-bound Data and Versioning
-
-One way to model time-specific data and relationships is by including data in the relationship type.
-Because Neo4j is optimized specifically for traversing relationships between entities, you can often improve query performance by specifying a date as the relationship type and only traversing particular dated relationships.
-
-A common example is for modeling airline flights.
-An airline has a particular flight on a certain day to and from a specific location.
-We might start with a model like the first image below to show how flights travel from airport to airport.
-
-image::{img}modeling_airport_flights.jpg[role="popup-link"]
-
-We would soon realize that we need to model a `Flight` entity that exists between two destinations because multiple planes can travel between two destinations several times in one day.
-
-However, your queries probably will still show the model's weakness in filtering through all of the flights at a specific airport - especially for London and other major cities that have hundreds of flights connected to an `Airport` node over any span of time.
-Inspecting the several properties of each `Flight` node could be expensive on resources.
-
-If we were to create a node for a particular airport day and a relationship with a date in the type, then we could write queries to find flights from an airport on any specified date (or date range).
-This way, you wouldn't need to check each flight relationship to an airport.
-Instead, you would only look at the relationships for the dates you cared about.
-This model turns out like the one below.
-
-image::{img}modeling_airport_flight_dates.jpg[role="popup-link"]
-
-For the full walkthrough of the modeling process for airline flights, see https://maxdemarzi.com/2015/08/26/modeling-airline-flights-in-neo4j/[Max's blog post^].
-
-=== Versioning
-
-Similar to the model above where we create a dated relationship type, we can also use this to track versions of our data.
-Tracking changes in the data structure or showing a current and past value can be incredibly important for auditing purposes, trend analysis, etc.
-
-For instance, if you wanted to create a new effective-dated relationship between a person and their current address, but also retain past addresses, you could use the same principle of including a date in the relationship type.
-To find the current address of the person, the query would look for the most recently dated relationship.
-
-[#multiple-models]
-== Taking the Best of Both Worlds
-
-Sometimes, you might find that one model works really well for one scenario you need, but another model is better for something else.
-For instance, some models will perform better with write queries and other models handle read queries better.
-Both capabilities are important to your use case, so what do you do?
-
-In these cases, you can combine both models and use the benefits of each.
-Yes, you can use more than one data model in your graph!
-
-The tradeoff is that now you will need to maintain two models.
-Each time you create a new node or relationship or update pieces of the graph, you will need to make changes to accommodate both models.
-This can also impact query performance, as you might have double the syntax needed to update each model.
-
-While this is definitely a possible option, you should know the maintenance costs and evaluate whether those costs are overcome by the performance improvements you will see for each needed query.
-If so, being able to use more than one data model is a great solution!
-
-[#modeling-resources]
-== Resources
-* https://medium.com/neo4j/graph-data-modeling-all-about-relationships-5060e46820ce[Blog post: Modeling relationships^]
-* https://maxdemarzi.com/2015/08/26/modeling-airline-flights-in-neo4j/[Max's blog post: Modeling airline flights^]
-* https://maxdemarzi.com/2017/05/24/flight-search-with-neo4j/[Follow-up blog post: Flight search^]
-* https://medium.com/neo4j/graph-data-modeling-categorical-variables-dd8a2845d5e0[Blog post: Modeling data categories^]
-* https://maxdemarzi.com/2017/11/21/mutual-fund-benchmarks-with-neo4j/[Blog post: Modeling mutual funds^]
-* https://maxdemarzi.com/2018/07/11/building-a-dating-site-with-neo4j-part-one/[Blog post series: Building a Dating Site^]
-* https://maxdemarzi.com/2017/03/30/building-a-twitter-clone-with-neo4j-part-one/[Blog series: Building a Twitter Clone^]
-* https://community.neo4j.com/[Ask Questions on the Neo4j Community Site!^]
-* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^]
\ No newline at end of file
diff --git a/modules/ROOT/pages/modeling-tips.adoc b/modules/ROOT/pages/modeling-tips.adoc
deleted file mode 100644
index 561cec01..00000000
--- a/modules/ROOT/pages/modeling-tips.adoc
+++ /dev/null
@@ -1,95 +0,0 @@
-= Graph Modeling Tips
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: modeling
-:tags: graph-modeling, data-model, schema, model-tips, model-queries
-:description: In this guide, you will find some helpful information to designing a data model for your domain.  Optimizing the model will help developers to maximize performance of the system and queries.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-modeling/modeling-tips/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should understand xref:graph-database.adoc[what a graph database is] and know the components of the xref:guide-data-modeling.adoc[property graph data model].
-
-[role=expertise {level}]
-{level}
-
-[#modeling-tips]
-== Tips and Tricks of Modeling
-
-As you may have found in reading the modeling guides or in your own experience with graph data modeling, there is no right or wrong way to model your data.
-Some ways may be better-suited to your needs and more performant on the aspects you prioritize, but you have options.
-
-To find the best data model for your needs, it often helps to approach with a few techniques and make data model decisions from that analysis.
-We will talk about a few tips and tricks in the next paragraphs to help you decide upon your data model.
-
-[#modeling-queries]
-== Write Your Queries First
-
-Knowing the kinds of questions and queries you want to ask of your data is a great way to determining the structure of your data model.
-If you know your queries need to return results within a certain date range, then you probably should ensure that date is not a property on a node, but rather stored as a separate node or relationship.
-In contrast, for a university progam domain, finding similar class offerings to a current course might work well with a high-level category hierarchy that makes searching all classes within a subject topic more efficient.
-
-Even if you do not know the exact query syntax just yet, understanding the intention of the system or application you are building and then constructing the model around the business need will help you organize it in a more accurate way.
-
-[#prioritize-queries]
-== Prioritize Queries
-
-It is very difficult (if not impossible) to find the perfect model for every query or functionality.
-As we talked about in the xref:modeling-designs.adoc[modeling designs guide], there are tradeoffs with choosing one particular model over another (or using multiple).
-While you may improve certain things, there is no way to get a one-size-fits-all solution.
-
-Instead, you should determine which model _best_ suits your needs.
-You may not be able to max out performance on every individual query, but you may be able to get the most out of your system with certain resources, time, and code.
-
-To do this, you will need to decide which queries must absolutely have maximum performance and which capabilities are critical to provide value.
-This may be a tough decision, but no matter the technology you are working with, these decisions will exist in some facet or other.
-What makes Neo4j more valuable is that the model is flexible and able to change if your priorities adjust over time.
-
-[#test-model]
-== Test it Out
-
-You may come across scenarios that you did not realize in the design stages.
-One of the best ways to find these is to actually test the model out.
-
-Loading portions of your data and executing tests and queries on the system will determine if the results you receive fit your needs or your expected performance.
-Again, Neo4j is flexible so that you can adjust the model or optimize your queries to fine-tune the outputs.
-
-Having trouble deciding between one or more models?
-Try creating a proof-of-concept test for each model and both together and see how they operate.
-What is complicated or what is not worth the hassle?
-Is there one that actually performs better in real life or does a multiple-data-model approach truly give you the best results?
-Sometimes, the best way to find out is to test it out with live data.
-
-[#refactor-model]
-== Refactoring your Graph
-
-As mentioned in above and in other guides, changes are always possible with Neo4j.
-The data model is purposely flexible and easy to adjust for this very reason.
-Business needs and priorities tend to fluctuate.
-Users may also change their behaviors and cause shifts for the business.
-
-Cypher allows you to write queries to run mass updates across labels, add or remove properties, and insert additional nodes and relationships into the structure.
-There are also procedures to aid in batching queries and executing updates to cluster instances, as available.
-
-For more information in this topic, check out the https://neo4j-contrib.github.io/neo4j-apoc-procedures/[APOC standard library^]!
-
-[#model-concerns]
-== Other Concerns
-
-The size of your data set also can impact queries and performance.
-If you have a smaller data set, then you may not see much performance impact in more complex queries.
-It is only when the amount of your data grows that you may see increased impacts.
-This is where the data model and query optimizations become vital to maximizing the value from your system.
-
-[#modeling-resources]
-== Resources
-* https://community.neo4j.com/[Ask Questions on the Neo4j Community Site!^]
-* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^]
\ No newline at end of file
diff --git a/modules/ROOT/pages/mongodb.adoc b/modules/ROOT/pages/mongodb.adoc
deleted file mode 100644
index c4938145..00000000
--- a/modules/ROOT/pages/mongodb.adoc
+++ /dev/null
@@ -1,131 +0,0 @@
-= Neo4j and MongoDB
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:category: integrations
-:tags: integrations, mongodb, document, tools, libraries, deprecated
-:description: Taking advantage of the strengths of multiple database technologies is the concept of polyglot persistence.
-
-[NOTE]
-**This approach is deprecated** in favor of link:https://neo4j.com/labs/apoc/4.3/overview/apoc.mongodb/[APOC procedures for working with MongoDB].  This page is being maintained
-for reference, but is not current or supported.  Please consult the APOC documentation for the latest information.
-
-.Goals
-[abstract]
-{description}
-For example, a product catalog application might use a document database (such as MongoDB) to power browsing / searching for products along with a graph database (such as Neo4j) to provide real time personalized product recommendations.
-To enable polyglot persistence the application needs to store data in multiple databases, each with its own data model (graph vs. document).
-Being able to connect MongoDB to Neo4j and synchronize data automatically makes this process much simpler.
-
-.Prerequisites
-[abstract]
-You should have an understanding of MongoDB, Neo4j, be famililar with both the document data model and property graph data model, and have MongoDB and Neo4j installed.
-
-[role=expertise {level}]
-{level}
-
-[#neo4j-mongodb]
-== General Observations
-
-Often it is useful to synchronize meta data or a subset of data from MongoDB to Neo4j to take advantage of realationships in the data, something that Neo4j is optimized for and is more difficult to accomplish in MongoDB.
-The Neo4j Doc Manager project below facilitates this process.
-
-[#doc-manager]
-== Neo4j Doc Manager
-
-The developers at link:http://mongodb.com[MongoDB^] have provided the link:https://github.com/mongodb-labs/mongo-connector[mongo-connector^] project which provides a mechanism for listening for all update operations in MongoDB and facilitates mirroring those updates to another system.
-
-From the mongo-connector documentation:
-
-[quote]
-mongo-connector creates a pipeline from a MongoDB cluster to one or more target systems, such as Solr, Elasticsearch, or another MongoDB cluster.
-It synchronizes data in MongoDB to the target then tails the MongoDB oplog, keeping up with operations in MongoDB in real-time.
-From the Github repo for Mongo-connector - mongo-connector supports Python 3.4+ and MongoDB versions 3.4 and 3.6.
-
-
-
-To facilitate synchronizing data from MongoDB to a Neo4j instance, the community has implemented a link:https://github.com/neo4j-contrib/neo4j_doc_manager[Neo4j Doc Manager] for mongo-connector.
-It is intended for live one-way synchronization from MongoDB to Neo4j, where you have both databases running to take advantage of each databases' strengths in your application.
-
-[NOTE]
-This project is currently beta quality and not officially supported by Neo4j.
-
-=== Installing
-
-You must have Python installed to use Neo4j Doc Manager.
-
-It is recommended to install using link:http://pip.readthedocs.org/en/stable/[pip], the Python package manager.
-
-[source,shell]
-pip install neo4j-doc-manager --pre
-
-For alternative installation workflow, see the documentation link:neo4j-doc-manager/[here].
-
-=== Using Neo4j Doc Manager
-
-Ensure that a Neo4j instance is running.
-If authentication is enabled for Neo4j, set the +NEO4J_AUTH+ environment variable, containing username and password:
-
-[source,shell]
-export NEO4J_AUTH=user:password
-
-Ensure that MongoDB is running a replica set.
-To initiate a replica set, start MongoDB with this command:
-
-[source,shell]
-mongod --replSet myDevReplSet
-
-Then open mongo-shell and run:
-
-[source,shell]
-rs.initiate()
-
-Refer to the link:https://github.com/10gen-labs/mongo-connector/wiki/FAQ[Mongo Connector FAQ] for more information.
-
-Start the mongo-connector service with the following command:
-
-[source,shell]
-mongo-connector -m localhost:27017 -t http://localhost:7474/db/data -d neo4j_doc_manager
-
-* *-m* provides the MongoDB endpoint
-* *-t* specifies the Neo4j endpoint
-* *-d* specifies Neo4j Doc Manager as the doc manager
-
-To see all configuration options for Neo4j Doc Manager (including specifying which collections and fields to synchronize) see the documentation link:neo4j-doc-manager/[here].
-
-=== Data synchronization
-
-With the `neo4j_doc_manager` service running, any documents inserted into MongoDB will be converted to a property graph structure and immediately inserted into Neo4j as well. Document keys will be turned into nodes.
-Nested values on each key will become properties.
-
-image::{img}20160203162601/doc_to_graph1.png[Document to graph]
-
-To see this in action, let's consider the following document:
-
-[source,shell]
-{
-  "session": {
-    "title": "12 Years of Spring: An Open Source Journey",
-    "abstract": "Spring emerged as a core open source project in early 2003 and evolved to a broad portfolio of open source projects up until 2015."
-  },
-  "topics":  ["keynote", "spring"],
-  "room": "Auditorium",
-  "timeslot": "Wed 29th, 09:30-10:30",
-  "speaker": {
-    "name": "Juergen Hoeller",
-    "bio": "Juergen Hoeller is co-founder of the Spring Framework open source project.",
-    "twitter": "https://twitter.com/springjuergen",
-    "picture": "http://www.springio.net/wp-content/uploads/2014/11/juergen_hoeller-220x220.jpeg"
-  }
-}
-
-If we insert the document into MongoDB using the mongo-shell:
-
-[source,shell]
-db.talks.insert(  { "session": { "title": "12 Years of Spring: An Open Source Journey", "abstract": "Spring emerged as a core open source project in early 2003 and evolved to a broad portfolio of open source projects up until 2015." }, "topics":  ["keynote", "spring"], "room": "Auditorium", "timeslot": "Wed 29th, 09:30-10:30", "speaker": { "name": "Juergen Hoeller", "bio": "Juergen Hoeller is co-founder of the Spring Framework open source project.", "twitter": "https://twitter.com/springjuergen", "picture": "http://www.springio.net/wp-content/uploads/2014/11/juergen_hoeller-220x220.jpeg" } } );
-
-the document is converted to a property graph and inserted into Neo4j with this structure:
-
-image::{img}20160203162603/graph11.png[Property graph]
-
-To see more examples and the full documentation, please refer to the link:https://github.com/neo4j-contrib/neo4j_doc_manager[Neo4j Doc Manager project on GitHub^].
diff --git a/modules/ROOT/pages/movie-database.adoc b/modules/ROOT/pages/movie-database.adoc
deleted file mode 100644
index e5f3c2c1..00000000
--- a/modules/ROOT/pages/movie-database.adoc
+++ /dev/null
@@ -1,86 +0,0 @@
-= Dataset: Movie Database
-//:slug: movie-database
-:level: Intermediate
-:page-level: Intermediate
-:author: Michael Hunger
-:category: import-export
-:tags: graph-data, data-sets, import-data, graph-examples, app-development
-:description: There are lots of example datasets for Neo4j.  This guide outlines some of them.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be comfortable installing and importing data into Neo4j.
-
-[role=expertise {level}]
-{level}
-
-== The Dataset
-
-The movie database is a traditional dataset for graph databases, similiar to IMDB it contains movies and actors, directors, producers etc.
-It could be extended with Genres, Publishers, Ratings and more.
-
-[NOTE]
-You find a similar, but smaller dataset in the Neo4j-Browser of your Neo4j installation by entering `:play movie graph` in the commandline.
-
-This dataset was sourced from link:TheMovieDB.org.
-Thanks so much to them for allowing us to use it for educational purposes.
-It was originally used for the link:/developer/language-guides/spring-data-neo4j[Spring Data Neo4j] tutorial project Cineasts.
-
-The data model is straightforward:
-
-* (:Movie {title, released, ... })
-* (:Person {name, born, ...})
-* (:Person)-[:ACTED_IN|:DIRECTED|:PRODUCED]->(:Movie)
-
-image::{img}/data_modeling_movies_actors.png[]
-
-=== Download
-
-The dataset consists of 12862 movies, and 50179 people (44943 actors, 6037 directors).
-
-[NOTE]
-Make sure to download the correct version of the dataset for your Neo4j installation.
-
-* http://example-data.neo4j.org/files/cineasts_12k_movies_50k_actors_2.1.6.zip[Download for Neo4j 2.1 (12.3 MB)]
-* Download for Neo4j 2.2
-* Download for Neo4j 2.3
-
-Unzip the dataset after the download and move the `graph.db` folder to your `/path/to/neo4j/data` directory and override the `graph.db` folder that was previously there.
-
-Installation in Detail:
-
-* Stop your Neo4j server with `bin/neo4j stop` or your control app
-* Unzip the downloaded file
-* Override `graph.db` in `/path/to/neo/data`
-* Start the server again with `bin/neo4j start`
-* Open the Neo4j Web Interface on http://localhost:7474
-* Read about the Cypher Query Language
-* Follow the source links for some example Cypher queries
-
-You can also point the neo4j-shell to the extracted directory to run Cypher queries directly:
-[source, shell]
-----
-/path/to/neo/bin/neo4j-shell -path /path/to/graph.db
-----
-
-=== Example Queries
-
-include::example$movie-database-queries.txt[]
-
-=== Import Instructions
-
-http://themoviedb.org[TheMovieDB.org] offers a JSON API to access, movies, and their cast.
-
-We use that API with a ruby script to turn each movie-json object into a cypher statement.
-Make sure to get an https://www.themoviedb.org/faq/api[API-Key] upfront.
-
-[source,ruby]
-----
-include::example$import-movies.rb[]
-----
-
-It outputs the Cypher statements on stdout and can be piped to the neo4j-shell or a file, which then can be read by the neo4j-shell.
diff --git a/modules/ROOT/pages/multi-tenancy-worked-example.adoc b/modules/ROOT/pages/multi-tenancy-worked-example.adoc
deleted file mode 100644
index bdbece4c..00000000
--- a/modules/ROOT/pages/multi-tenancy-worked-example.adoc
+++ /dev/null
@@ -1,509 +0,0 @@
-= Multi Tenancy in Neo4j: A Worked Example
-:level: Beginner
-:page-level: Advanced
-:author: Mark Needham
-:neo4j-version: 4.0
-:category: cluster
-:tags: administration, multi-database, fabric, sharding, multi-tenancy, apoc
-:description: In this guide, we will learn how to do multi tenancy in Neo4j.
-:page-type: Tutorial
-:page-deprecated-title: the Neo4j Operations Manual
-:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/tutorial/tutorial-composite-database/
-
-// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) downloaded and installed.
-It helps to have read the section on xref:manage-multiple-databases.adoc[managing multiple databases].
-We'll also need to install https://neo4j.com/developer/neo4j-apoc/[APOC^], Neo4j's standard library.
-
-[role=expertise {level}]
-{level}
-
-[#multi-tenancy]
-In Neo4j (v4.0+), we can create and use more than one active database at the same time.
-This works in standalone and causal cluster scenarios and allows us to maintain multiple, separate graphs in one installation.
-
-In this guide we're going to learn how to use this feature with a multi tenant dataset.
-
-[#setup-db]
-== System setup
-
-If you haven't already, link:/download/[download^] Neo4j.
-
-We will need to have a database running and open link:/developer/neo4j-browser/[Neo4j Browser] to walk through this guide.
-If you are unsure how to create and start a database, step-by-step instructions for doing so in Neo4j Desktop are provided in link:/developer/neo4j-desktop[this guide].
-
-We're going to use the APOC Library in this guide, and in order to process local files, we'll need need to add the following entry to the `apoc.conf` file:
-
-.apoc.conf
-[source,properties]
-----
-apoc.import.file.enabled=true
-----
-
-We'll also be using https://neo4j.com/docs/operations-manual/current/fabric/introduction/[Neo4j Fabric^], which we can enable by adding the following properties in the `neo4j.conf` file:
-
-.neo4j.conf
-[source,properties]
-----
-fabric.database.name=fabric
-
-fabric.graph.0.name=mall1
-fabric.graph.0.uri=neo4j://localhost:7687
-fabric.graph.0.database=mall1
-
-fabric.graph.1.name=mall2
-fabric.graph.1.uri=neo4j://localhost:7687
-fabric.graph.1.database=mall2
-----
-
-[#carrefour-retail-dataset]
-== Carrefour Retail dataset
-
-We're going to use a dataset published by Carrefour, a French retailer, as part of their https://github.com/ging/carrefour_basket_data_challenge[Delighting Customers Challenge Basket Data^].
-Rik van Bruggen recently wrote a series of blog posts showing how to import and analyse this data in Neo4j 3.5.
-
-The data is available as https://data.neo4j.com/DelightingCustomersBD.json[a JSON file (1GB)^] curated by Rik.
-Once we've downloaded the file, we need to put it into the `import` folder of Neo4j.
-
-The dataset is accompanied by a data dictionary that describes each of the fields that we see in the file:
-
-.Data Dictionary
-[opts="header", cols="1,3"]
-|===
-| Field | Description
-| id |     Number id for that individual ticket.
-| mall |   Store where the ticket was printed. It has two values, 1 and 2.
-| data  |  Date and time the ticket was printed.
-| client | Some tickets will have a Customer ID. Many tickets will share a Customer ID.
-| items  | List of items contained in the printed ticket. The list contains a dictionary with a product description (desc), the amount charged (net_am), and the number of units bought (n_unit).
-|===
-
-From looking at these descriptions, `mall` seems like a good property to use to partition the data.
-We can store the tickets printed in mall 1 in one database, and the tickets printed in mall 2 in another database.
-
-
-[#exploring-data]
-== Exploring the data
-
-We can now process this file using the `apoc.load.json` procedure.
-
-[source,cypher]
-----
-CALL apoc.load.json("file:///DelightingCustomersBDclean.json")
-YIELD value
-RETURN value
-LIMIT 5;
-----
-
-.Results
-[opts="header"]
-|===
-| value
-| {date: "2016-01-14T20:07:00.000+0000", client: 77021708271, _id: 1001, items: [{n_unit: 1, net_am: 1.0, desc: "CARAMELOS S/AZUCAR"}], mall: 2}
-| {date: "2016-01-14T15:25:00.000+0000", client: 77021708271, _id: 1002, items: [{n_unit: 1, net_am: 3.0, desc: "TOSTA VARIADA"}, {n_unit: 1, net_am: 1.0, desc: "BAGUETTE TORTILLA"}], mall: 1}
-| {date: "2016-01-14T20:07:00.000+0000", client: 77021708271, _id: 1003, items: [{n_unit: 1, net_am: 2.83, desc: "QUESO TIERNO MEZCL"}, {n_unit: 1, net_am: 1.65, desc: "GUISANTES MUY FINO"}, {n_unit: 1, net_am: 1.77, desc: "BIFIDUS CON FRUTAS"}, {n_unit: 1, net_am: 1.16, desc: "MAIZ DULCE PACK3X140"}, {n_unit: 1, net_am: 2.5, desc: "SANUS FRESA L. CASEI"}, {n_unit: 1, net_am: 1.0, desc: "FANTA LIMÓN S/BURB"}, {n_unit: 1, net_am: 1.85, desc: "CEREALES ESTRELLITAS"}, {n_unit: 1, net_am: 2.15, desc: "SALVASLIP EVAX"}, {n_unit: 1, net_am: 1.09, desc: "YOGUR NATURAL DANO"}, {n_unit: 1, net_am: 1.15, desc: "ARROZ LARGO SOS 1 KI"}, {n_unit: 1, net_am: 1.5, desc: "YORK SANDWICH ELPO"}, {n_unit: 1, net_am: 0.75, desc: "BARQUILLO NATA COVY"}, {n_unit: 1, net_am: 1.39, desc: "TRINA LIMON 1,5LITRO"}, {n_unit: 1, net_am: 1.85, desc: "CHORIZO DULCE CARR"}, {n_unit: 1, net_am: 1.0, desc: "PIMIENTO PIQUILLO"}, {n_unit: 24, net_am: 6.0, desc: "CERVEZA HOLANDESA"}, {n_unit: 1, net_am: 1.34, desc: "PECHUGA PAVO LONCH"}, {n_unit: 6, net_am: 1.52, desc: "AGUA CARREFOUR 2 L"}, {n_unit: 1, net_am: 1.69, desc: "FIAMBRE JAMON PAVO"}, {n_unit: 1, net_am: 0.66, desc: "BIFIDUS COCO 0%"}, {n_unit: 1, net_am: 2.74, desc: "DUPLO COLGATE TRIP"}], mall: 2}
-| {date: "2016-01-14T16:25:00.000+0000", client: 77021708271, _id: 1004, items: [{n_unit: 1, net_am: 0.64, desc: "AGUA SOLAN CABRAS"}], mall: 2}
-| {date: "2016-01-14T14:25:00.000+0000", client: 77021708271, _id: 1005, items: [{n_unit: 1, net_am: 3.9, desc: "PAQUETE 500 HOJAS A4"}, {n_unit: 1, net_am: 4.99, desc: "LEGGING NINA 3/14"}, {n_unit: 1, net_am: 9.99, desc: "JERSEY UNISEX 3/14"}, {n_unit: 3, net_am: 4.6, desc: "HUESITOS LECHE 12"}, {n_unit: 1, net_am: 2.65, desc: "MINI FUET CAMPOFRIO"}, {n_unit: 2, net_am: 2.78, desc: "REGAÑA ACEITE OLIV"}, {n_unit: 1, net_am: 15.95, desc: "MEGA MAKI"}], mall: 1}
-|===
-
-We can see that some items are from mall 1 and some from mall 2.
-Let's have a look how many tickets each mall printed:
-
-[source,cypher]
-----
-CALL apoc.load.json("file:///DelightingCustomersBDclean.json")
-YIELD value
-RETURN value.mall, count(*)
-----
-
-.Results
-[opts="header"]
-|===
-| value.mall | count(*)
-| 2 | 293586
-| 1 | 292893
-|===
-
-It looks like we'll have two quite evenly populated databases.
-Let's get those databases created!
-
-[#creating-databases]
-== Creating databases
-
-We're going to store the data for each mall in a different Neo4j database.
-We'll create these databases using the https://neo4j.com/docs/cypher-manual/4.0/administration/databases/#administration-databases-create-database[`CREATE DATABASE`^] command, which we need to run against the `system` database.
-Let's first switch to that database:
-
-[source,cypher]
-----
-:use system;
-----
-
-Once we've done that we can run the following statements to create the databases:
-
-[source,cypher]
-----
-CREATE DATABASE mall1;
-CREATE DATABASE mall2;
-----
-
-We can check that those databases have been created by running the following command:
-
-[source,cypher]
-----
-SHOW DATABASES;
-----
-
-.SHOW DATABASES
-[opts="header"]
-|===
-| name     | address        | role         | requestedStatus | currentStatus | error | default
-| "neo4j"  | "0.0.0.0:7687" | "standalone" | "online"        | "online"      | ""    | TRUE
-| "system" | "0.0.0.0:7687" | "standalone" | "online"        | "online"      | ""    | FALSE
-| "fabric" | "0.0.0.0:7687" | "standalone" | "online"        | "online"      | ""    | FALSE
-| "mall1"  | "0.0.0.0:7687" | "standalone" | "online"        | "online"      | ""    | FALSE
-| "mall2"  | "0.0.0.0:7687" | "standalone" | "online"        | "online"      | ""    | FALSE
-|===
-
-[#graph-model]
-== Graph Model
-
-We're going to import the data into the following graph model:
-
-image::{img}4dot0_multi_tenancy_graph_model.png[role="popup-link"]
-
-We'll have nodes representing products, clients, tickets, and ticket items, and relationships that indicate which client purchased a ticket, the items that comprise a ticket, and the product that each item was for.
-
-[#importing-data]
-== Importing the data
-
-We'll use the same `apoc.load.json` procedure to import the data into each of our databases.
-We'll filter the JSON file by the `mall` property so that we load the data appropriately.
-
-Let's switch to the `mall1` database and setup parameters that we'll use in our import query:
-
-:mall: 1
-:url: "file:///DelightingCustomersBDclean.json"
-
-[source,cypher, subs="attributes"]
-----
-:use mall1;
-include::example$importTickets.cypher[tag=params]
-----
-
-And now we'll create that indexes that we're going to need:
-
-[source,cypher]
-----
-include::example$createConstraints.cypher[]
-----
-
-Now we're ready to process the JSON file.
-We'll wrap our call to `apoc.load.json` inside a call to `apoc.periodic.iterate` so that our import is batched, which will avoid out of memory exceptions:
-
-[source,cypher]
-----
-include::example$importTickets.cypher[tag=query]
-----
-
-:mall: 2
-
-And now let's do the same thing for mall 2.
-We'll switch to that database and setup our parameters:
-
-[source,cypher, subs="attributes"]
-----
-:use mall2;
-include::example$importTickets.cypher[tag=params]
-----
-
-And now we'll create that indexes that we're going to need:
-
-[source,cypher]
-----
-include::example$createConstraints.cypher[]
-----
-
-And now let's import the tickets sold in this store:
-
-[source,cypher]
-----
-include::example$importTickets.cypher[tag=query]
-----
-
-We can see a sample of the imported graph in the Neo4j Browser visualization below:
-
-image::{img}4dot0_multi_tenancy_browser_viz.png[role="popup-link"]
-
-[#querying-individual-databases]
-== Querying individual databases
-
-Now that we've imported the data, let's explore it by writing some queries.
-We'll show the results of running some queries on both databases and some just on one of them.
-
-****
-[NOTE]
-We can switch between the databases by running the command `:use mall1` or `:use mall2`.
-****
-
-The following query returns the biggest spending clients in the mall:
-
-[source,cypher]
-----
-MATCH (client:Client)-[:PURCHASED]->(ticket)-[:HAS_TICKETITEM]->(item:TicketItem)
-WHERE client.id <> "Unknown"
-WITH client, count(DISTINCT ticket) AS tickets,
-     apoc.math.round(sum(item.netAmount), 2) AS totalSpend
-RETURN client.id AS clientId, totalSpend, tickets,
-       apoc.math.round(totalSpend / tickets, 2) AS spendPerTicket
-ORDER BY totalSpend DESC
-LIMIT 10;
-----
-
-.Mall 1 biggest spenders
-[opts="header"]
-|===
-| clientId    | totalSpend | tickets | spendPerTicket
-| 77038482725 | 4125.71    | 95      | 43.43
-| 77091111583 | 3111.06    | 29      | 107.28
-| 77042913479 | 2726.84    | 51      | 53.47
-| 77035560017 | 2664.49    | 69      | 38.62
-| 77081132118 | 2644.51    | 33      | 80.14
-| 77060098914 | 2433.68    | 24      | 101.4
-| 77071854908 | 2379.27    | 27      | 88.12
-| 77079395996 | 2272.06    | 27      | 84.15
-| 77042176706 | 2183.27    | 65      | 33.59
-| 77059085165 | 2162.1     | 44      | 49.14
-
-|===
-
-
-.Mall 2 biggest spenders
-[opts="header"]
-|===
-| clientId    | totalSpend | tickets | spendPerTicket
-| 77038482725 | 3314.19    | 87      | 38.09
-| 77074230047 | 3281.82    | 47      | 69.83
-| 77035560017 | 3120.69    | 67      | 46.58
-| 77042573786 | 2696.9     | 31      | 87.0
-| 77017054434 | 2572.42    | 45      | 57.16
-| 77091111583 | 2494.08    | 27      | 92.37
-| 77037711999 | 2369.63    | 36      | 65.82
-| 77053280208 | 2359.28    | 58      | 40.68
-| 77045642784 | 2337.03    | 44      | 53.11
-| 77049483454 | 2259.89    | 56      | 40.36
-|===
-
-Interestingly, the biggest spending client in both malls is the same person.
-
-The `spendPerTicket` also looks interesting.
-The following query shows the products bought by the client that has the highest `spendPerTicket` in mall 2 :
-
-[source,cypher]
-----
-MATCH path = (:Client {id: 77078849426})-[:PURCHASED]->()-->(:TicketItem)-->(:Product)
-RETURN path
-----
-
-image::{img}4dot0_multi_tenancy_big_spender.png[role="popup-link",title="A big spender in Mall 2"]
-
-On the left hand side of the diagram we can see highlighted nodes that have skewed the average spend!
-My Spanish isn't great, but I can see an iPad Air among the items purchased.
-_ASP ROB LG HOMB S5_ is a https://www.carrefour.es/robot-aspirador-lg-lg-hombot-turbo-vr8602rr-smart-inverter-0-6-l-60-db-rojo/8806087897081[robot vacuum cleaner^] and I think _POR APP MB MJVE2YA_ is an Apple Macbook.
-The other items look more like the type of products you'd buy in a normal grocery shop.
-
-Let's go back to mall 1 and see which products are purchased most often.
-We can write the following query to compute this:
-
-[source,cypher]
-----
-MATCH (item:TicketItem)-->(product:Product)
-WITH product, apoc.math.round(sum(item.netAmount), 2) AS totalSpend,
-     count(*) AS purchases
-RETURN product.description, totalSpend, purchases,
-       apoc.math.round(totalSpend / purchases, 2) AS costPerItem
-ORDER BY totalSpend DESC
-LIMIT 10;
-----
-
-.Mall 1 popular products
-[opts="header"]
-|===
-| product.description    | totalSpend | purchases | costPerItem
-| "ROSCON NATA MEDIANO"  | 4596.64    | 450       | 10.21
-| "ACEITE OLIVA HOJIB"   | 3597.33    | 269       | 13.37
-| "CERVEZA MAHOU CLAS"   | 3328.62    | 363       | 9.17
-| "CERVEZA MAHOU 5 *"    | 2870.3     | 383       | 7.49
-| "ROSCON MEDIANO SIN R" | 2632.75    | 363       | 7.25
-| "ATÚN CLARO ACEITE"    | 2404.49    | 502       | 4.79
-| "ROSCON DE REYES"      | 2363.89    | 158       | 14.96
-| "ACEITE OLIVA CARBO"   | 2268.72    | 180       | 12.6
-| "CAMISA CRO M/LARGA"   | 2208.31    | 293       | 7.54
-| "PAÑAL DODOT"          | 2186.34    | 75        | 29.15
-|===
-
-All the top items are priced at less than 30 euros each.
-We can see a couple of beers (cerveza) in positions 3 and 4, and then Roscon seems to be a range of https://www.carrefour.es/supermercado/roscon-de-reyes-con-nata-1-3-kg-carrefour/R-577901507[tasty looking pastries^].
-
-[#querying-across-databases]
-== Querying across databases
-
-We can also write queries across databases using https://neo4j.com/docs/operations-manual/current/fabric/configuration/[Neo4j Fabric^].
-This functionality is useful for executing aggregate queries across multiple databases.
-
-Let's first change to the _fabric_ database by running the following command:
-
-[source,cypher]
-----
-:use fabric
-----
-
-Now we're going write a query that returns the total spend on products in each of our databases.
-
-We'll prefix our queries with the https://neo4j.com/docs/cypher-manual/4.0/clauses/use/[`USE`^] clause, which tells the Fabric engine which database to execute the query against.
-The syntax of this clause is as follows:
-
-`USE .`
-
-Or in our case `USE fabric.mall1` and `USE fabric.mall2`.
-
-The following query finds the total spend and number of purchases in each mall, combines the results using the https://neo4j.com/docs/cypher-manual/4.0/clauses/union/#union-combine-queries-retain-duplicates[`UNION ALL`^] clause, and then orders the results by total spend:
-
-[source,cypher]
-----
-USE fabric.mall1
-MATCH (item:TicketItem)-->(product:Product)
-RETURN "Mall 1" as mall,
-       apoc.math.round(sum(item.netAmount), 2) AS totalSpend,
-       count(*) AS purchases
-
-UNION ALL
-
-USE fabric.mall2
-MATCH (item:TicketItem)-->(product:Product)
-RETURN "Mall 2" as mall,
-       apoc.math.round(sum(item.netAmount), 2) AS totalSpend,
-       count(*) AS purchases
-
-ORDER BY totalSpend DESC;
-----
-
-.Total spending in malls
-[opts="header"]
-|===
-| mall     | totalSpend | purchases
-| "Mall 1" | 953536.51  | 219561
-| "Mall 2" | 943463.47  | 219966
-|===
-
-The total spend and number of purchases are almost identical between the malls!
-We can also write a version of this query that reduces duplication by using the built-in https://neo4j.com/docs/operations-manual/current/fabric/queries/#fabric-built-in-functions[`graphIds`^] function and the https://neo4j.com/docs/cypher-manual/4.0/clauses/call-subquery/[`CALL{}`^] clause, as shown below:
-
-[source,cypher]
-----
-WITH ["Mall 1", "Mall 2"] AS malls
-UNWIND fabric.graphIds() AS graphId
-CALL {
-  USE fabric.graph(graphId)
-  MATCH (item:TicketItem)-->(product:Product)
-  RETURN apoc.math.round(sum(item.netAmount), 2) AS totalSpend,
-         count(*) AS purchases
-}
-RETURN malls[graphId] AS mall, totalSpend, purchases
-ORDER BY totalSpend DESC;
-----
-
-We can also aggregate the results returned by the sub queries executed on each of the mall databases.
-The following query finds the highest selling products in each mall, and then shows the top 10 products across both malls:
-
-[source,cypher]
-----
-UNWIND fabric.graphIds() AS graphId
-CALL {
-  USE fabric.graph(graphId)
-  MATCH (item:TicketItem)-->(product:Product)
-  WITH product, apoc.math.round(sum(item.netAmount), 2) AS totalSpend,
-       count(*) AS purchases
-  RETURN product, totalSpend, purchases
-  ORDER BY totalSpend DESC
-}
-RETURN product.description, totalSpend, purchases
-ORDER BY totalSpend DESC
-LIMIT 10
-----
-
-.Top selling products across malls
-[opts="header"]
-|===
-| product.description    | totalSpend | purchases
-| "ROSCON NATA MEDIANO"  | 4596.64    | 450
-| "ROSCON NATA MEDIANO"  | 4462.64    | 451
-| "ACEITE OLIVA HOJIB"   | 3597.33    | 269
-| "LED SAM UE48J6200"    | 3546.08    | 6
-| "CERVEZA MAHOU CLAS"   | 3406.55    | 392
-| "CERVEZA MAHOU CLAS"   | 3328.62    | 363
-| "PAÑAL DODOT"          | 3078.55    | 99
-| "CERVEZA MAHOU 5 *"    | 2870.3     | 383
-| "ROSCON MEDIANO SIN R" | 2632.75    | 363
-| "ACEITE OLIVA HOJIB"   | 2478.99    | 222
-|===
-
-The top selling product in both malls is _ROSCON NATA MEDIANO_, but it'd be good if the sales for each product was aggregated so that we have only one row per product.
-We can do this using the https://neo4j.com/docs/cypher-manual/4.0/functions/aggregating/#functions-sum[`SUM`^] aggregation function:
-
-[source,cypher]
-----
-UNWIND fabric.graphIds() AS graphId
-CALL {
-  USE fabric.graph(graphId)
-  MATCH (item:TicketItem)-->(product:Product)
-  WITH product, apoc.math.round(sum(item.netAmount), 2) AS totalSpend,
-       count(*) AS purchases
-  RETURN product, totalSpend, purchases
-  ORDER BY totalSpend DESC
-}
-RETURN product.description, sum(totalSpend) AS totalSpend, sum(purchases) AS purchases
-ORDER BY totalSpend DESC
-LIMIT 10
-----
-
-****
-[NOTE]
-The implicit group by key for these aggregation functions is `product.description` and *not* `product`.
-The product node is completely different in each of the databases, so if we grouped by that we'd still end up with individual per product sold in each mall.
-****
-
-The table below shows the results of running the aggregation query:
-
-.Top aggregated selling products across malls
-[opts="header"]
-|===
-| product.description    | totalSpend         | purchases
-| "ROSCON NATA MEDIANO"  | 9059.28            | 901
-| "CERVEZA MAHOU CLAS"   | 6735.17            | 755
-| "ACEITE OLIVA HOJIB"   | 6076.32            | 491
-| "LED SAM UE48J6200"    | 5343.08            | 9
-| "PAÑAL DODOT"          | 5264.89            | 174
-| "CERVEZA MAHOU 5 *"    | 5236.620000000001  | 740
-| "ROSCON MEDIANO SIN R" | 4902.26            | 689
-| "ROSCON DE REYES"      | 4783.57            | 318
-| "ATÚN CLARO ACEITE"    | 4758.15            | 974
-| "CAMISA CRO M/LARGA"   | 4335.5599999999995 | 571
-
-|===
-
-
-[#resources]
-== Resources
-
-* link:/developer/manage-multiple-databases/[Managing Multiple Databases in Neo4j^]
-* link:/docs/operations-manual/current/manage-databases/[Documentation: Multi-database^]
-* link:https://adamcowley.co.uk/neo4j/multi-tenancy-neo4j-4.0/[Multi-Tenancy in Neo4j 4.0^]
-* link:https://adamcowley.co.uk/neo4j/sharding-neo4j-4.0/[When and how to implement Sharding in Neo4j 4.0^]
diff --git a/modules/ROOT/pages/neo4j-apoc.adoc b/modules/ROOT/pages/neo4j-apoc.adoc
deleted file mode 100644
index b373dcfb..00000000
--- a/modules/ROOT/pages/neo4j-apoc.adoc
+++ /dev/null
@@ -1,179 +0,0 @@
-= Neo4j APOC Library
-:level: Beginner
-:page-level: Beginner
-:experimental:
-:neo4j-version: 3.4.0
-:author: Jennifer Reif
-:category: labs
-:tags: graph-platform, apoc, utilities, libraries, procedures, functions
-:description: Through this guide, you will learn about one of Neo4j's extension libraries - APOC.
-:page-pagination:
-:page-newsletter: true
-:page-deprecated-title: the APOC user guide
-:page-deprecated-redirect: https://neo4j.com/docs/apoc/current/
-
-// This page has been deprecated in favour of the APOC user guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[TIP]
-====
-icon:flask[size=2x]
-The APOC library is one of our Neo4j Labs projects.
-To learn more, visit the https://neo4j.com/labs/apoc/[APOC^] Labs page.
-====
-
-.Goals
-[abstract]
-{description}
-You will understand why the library was created and how to use it to extend the functionality of the database and Cypher for use in applications, querying, loading/exporting data, and nearly any other functionality you could imagine.
-
-.Prerequisites
-[abstract]
-You should be familiar with the Neo4j graph database and what Cypher is.
-This tool is used mostly through the Neo4j Desktop application and through the Neo4j Browser, so you should also know how to use the basic functionality of link:/developer/neo4j-desktop/[Neo4j Desktop] and link:/developer/neo4j-browser/[Neo4j Browser].
-
-[role=expertise {level}]
-{level}
-
-[#about-apoc]
-APOC stands for **A**wesome **P**rocedures **o**n **C**ypher.
-Before APOC's release, developers needed to write their own procedures and functions for common functionality that Cypher or the Neo4j database had not yet implemented for support.
-Each developer might write his own version of these functions, causing a lot of duplication.
-
-So, one of our Neo4j developers created the APOC library as a standard utility library for common procedures and functions.
-This allowed developers across platforms and industries to use a standard library for common procedures and only write their own functionality for business logic and use-case-specific needs.
-
-The APOC library is believed to be the largest and most-widely used extension library for Neo4j.
-It includes over 450 standard procedures, providing functionality for utilities, conversions, graph updates, and more.
-They are well-supported and are very easy to run as separate functions or to include in Cypher queries.
-Before you begin to write adhoc functions for your application, be sure check APOC first to see if it exists there!
-
-[#installing-apoc]
-== Installing the APOC Library
-
-If you're using link:{aura_signup}[AuraDB] congratulations, you're already done with this section.  
-https://neo4j.com/docs/aura/current/getting-started/apoc/[A subset of APOC core functionality^] is already built in.  The rest of this section is for users who run Neo4j in a self-managed mode.
-
-If you haven't already, http://neo4j.com/download/[download] Neo4j Desktop and use the provided instructions (shown when downloading) to get a project and database ready to run.
-Once you have a project and database to work within, you can install APOC for your database using the "Plugins" panel on the right.
-
-[#apoc-intro]
-== Intro to Using the APOC Library
-
-Now that we have the library installed, there are a variety of ways we can interact with the various procedures and functions in the library.
-You can use them for parts of Cypher queries, or you can call them as standalone procedures from applications, scripts, command line, etc.
-We will cover more information about this in the sections below.
-
-++++
-
    
-
-
    
-++++
-
-[NOTE]
-====
-If you watched the video above, don't feel intimidated by the Cypher in some of the examples.
-Those topics are be covered in the guide and training classes for the https://neo4j.com/developer/cypher/[Cypher Query Language].
-====
-
-[#apoc-tips]
-== APOC Helpful Tips
-
-With so much to offer in the APOC library, it is easy to feel overwhelmed and unsure how to approach it.
-There are a variety of docs and blog posts that show how to use some of the procedures, which are linked at the bottom of this guide.
-To get started, though, let us take a look at some helpful ways to begin working with APOC.
-
-=== Command Syntax
-
-As mentioned above, there are two ways to use an APOC procedure - within a Cypher statement or as a regular function call.
-With either method, the same syntax is used, as noted below.
-
-[source,cypher]
-----
-CALL ..(,,...);
-----
-
-The `CALL` command is used to initiate the procedure call, followed by the name of the package to denote which package is being called (in this case, the APOC library).
-Like many programming language packages, we note subpackages to narrow the scope of what is being called.
-A real-life example of this syntax for calling an APOC procedure is explained below.
-
-[source,cypher]
-----
-CALL apoc.help('keyword')
-----
-
-=== CALL apoc.help('keyword')
-
-This is one of the best ways to start looking for procedures in APOC that will fit your need.
-Using the syntax from above, we are able to list the possible APOC procedures and basically search the library.
-In place of `keyword` in the parentheses, however, we can search for a type or category.
-
-For instance, take a look at the below example and screenshot.
-If we needed to find all of the procedures that could modify, convert, or deal with text in some way, we could run that APOC command and return the results from the screenshot.
-
-[source,cypher]
-----
-CALL apoc.help('text')
-----
-
-.Results for APOC procedures dealing with text
-image:{img}apoc_help_text.png[role="popup-link"]
-
-This command also gives a description and sample implementation with parameters and their data types.
-
-[TIP]
-====
-For a comprehensive list of all the procedures, you can execute `CALL apoc.help('apoc')`.
-====
-
-
-[#apoc-examples]
-== Common Examples and Usage
-
-There are endless opportunities for using provided procedures in APOC.
-While it is not possible to cover them fully here, we would like to highlight a few of the most-used ones and give you a feel for using them in real-life scenarios.
-
-* *apoc.date.format(dateForConversion, [timeUnit], [format]) —* convert an epoch time-value to a desired format.
-Can be useful for outputting to reports, showing on a web screen, including it in a URL as a parameter, and hundreds of other uses.
-There are also other procedures for various date/time conversions from and to other data types, as well as expiration dates and retrievals for current date.
-
-[TIP]
-====
-Neo4j 3.4 released some date/time functionality as part of the core product.
-If you are using 3.4, you may not need APOC for common date/time work.
-For any users on previous versions, APOC is the best way to incorporate this functionality.
-More information about using the date/time functionality can be found in the resources at the bottom of this guide.
-====
-
-* *apoc.load.json(url) -* load data from a URL or a JSON-formatted file and use Cypher statements to create or update data in Neo4j database.
-Excellent for calling an API and dumping retrieved data into Neo4j.
-Other similar procedures exist for `apoc.load.jdbc` for a direct JDBC to a database, `apoc.load.xml` for xml data, and `apoc.load.csv` for CSV flat files.
-No matter what your data import needs might be, APOC is likely to have something to address them.
-
-[TIP]
-====
-For more information on using APOC for data import, check out our https://neo4j.com/developer/data-import/[Data Import section].
-====
-
-* *apoc.periodic.iterate(query1, query2, {param1: value1}) —* used as a sort of batch loader.
-Can pull a list of results in the first query, then execute another query on each of those query1 results to update each one or retrieve other data for it.
-Can set parameters for batch size, variables, retry number, etc.
-There are a number of variations on this procedure for running background processes, managing threads, and committing/submitting/canceling processes.
-
-[#apoc-help]
-== Getting Help and Asking Questions about APOC
-
-If you have consulted the documentation, blogs, and other resources and still cannot solve how use APOC in a particular query or function, you can reach out to a variety of Neo4j and community experts.
-The different options and descriptions of each type are listed below.
-
-* https://community.neo4j.com[Neo4j Online Community^] - post questions and answer other users' posts in a message-based forum.
-* https://github.com/neo4j-contrib/neo4j-apoc-procedures[APOC GitHub project^] - post GitHub issues for items that are not working as documented or pull requests for additional features or updates.
-Issues will be prioritized and included in future releases of the APOC library.
-
-[#apoc-resources]
-== Resources
-* https://neo4j.com/labs/apoc/4.3/[APOC Documentation^]
-* https://medium.com/neo4j/explore-new-worlds-adding-plugins-to-neo4j-26e6a8e5d37e[Installing Plugins: Blog post^]
-* https://www.youtube.com/watch?v=V1DTBjetIfk&list=PL9Hl4pk2FsvXEww23lDX_owoKoqqBQpdq[YouTube: APOC Video Series^]
-* https://www.adamcowley.co.uk/neo4j/temporal-native-dates/[Date/Time Functionality: Adam's blog post^]
-* https://neo4j.com/docs/developer-manual/3.4/cypher/syntax/temporal/[Date/Time Data Types: Neo4j documentation^]
-* https://neo4j.com/docs/developer-manual/3.4/cypher/functions/temporal/[Date/Time Functions: Neo4j documentation^]
diff --git a/modules/ROOT/pages/neo4j-bloom.adoc b/modules/ROOT/pages/neo4j-bloom.adoc
deleted file mode 100644
index 301c9ae1..00000000
--- a/modules/ROOT/pages/neo4j-bloom.adoc
+++ /dev/null
@@ -1,326 +0,0 @@
-= Neo4j Bloom User Interface Guide
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: bloom
-:tags: graph-platform, server, desktop, sandbox, perspective, graph, visualization, data
-:description: This article shows how to use Neo4j Bloom for exploring graph data in Neo4j.
-:page-pagination:
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/graph-visualization/graph-visualization/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-It helps to have read the section on xref:graph-database.adoc[graph databases] and link:/developer/neo4j-desktop/[Neo4j Desktop].
-
-[role=expertise {level}]
-{level}
-
-[#what-is-bloom]
-Neo4j Bloom is a data exploration tool that link:/developer/graph-visualization/[visualizes data in the graph] and allows users to navigate and query the data without any query language or programming.
-
-Users can write patterns similar to natural language questions to retrieve data and traverse layers of the graph.
-Bloom also allows appropriate users to edit, update, or correct the graph when missing information or bad data is found.
-
-++++
-
    
-
-
    
-++++
-
-[#using-bloom]
-== Ways to use Neo4j Bloom
-
-There are a couple of different ways to access and use Neo4j Bloom.
-These are outlined below.
-
-* Neo4j Bloom local with users accessing Bloom via Neo4j Desktop (free for local database instances)
-* Neo4j Bloom server with users accessing Bloom via a web browser
-* Neo4j Bloom through the https://sandbox.neo4j.com/?usecase=bloom&ref=developer-bloom[sandbox^]
-* Neo4j Bloom through Neo4j Database as a Service, link:/aura/[AuraDB^]
-* Included in link:/startup-program/[Neo4j Startup Program^]
-
-For this guide, we will focus on using Neo4j Bloom locally via Neo4j Desktop.
-Neo4j Bloom 1.3 onwards is available for free on Neo4j Desktop. No activiation key or download action is required.
-
-[#install-neo4j-bloom]
-== Install Neo4j Bloom
-
-If you do not yet have Neo4j Desktop, you can install it from our link:/download-center/#desktop[download page^]. Neo4j Bloom is part of the installation. You will need to have Neo4j Desktop version 1.2.7 or above.
-After installing, you will need to create a database instance.
-Detailed instructions for this are on the link:/developer/neo4j-desktop/[Neo4j Desktop guide].
-
-As noted above, we will be working with a local version of Bloom from Neo4j Desktop.
-The Neo4j Bloom application should show under the `Graph Apps` tab in Neo4j Desktop.
-More information about Bloom in Desktop is in the link:/docs/bloom-user-guide/current/bloom-installation/#_neo4j_desktop_hosted_bloom_client[documentation^].
-
-[#data-set]
-== Our example data set
-
-In this guide, we will walk through an example of exploring a retail data set to help us see what Bloom can provide and the functionality it supports.
-We will use the Northwind data, which is a fictional retail system of orders, products, and customers.
-
-With Bloom, we will see how to navigate and explore the data to gather business value and insights.
-
-To load this data, we can start our instance in Neo4j Desktop and open Neo4j Browser by going to `localhost:7474` in a web browser.
-Then, in the Cypher command bar, type `:play northwind-graph` and run it for the Browser guide to appear in a pane below the command line.
-Click on and run the Cypher queries in the slides to load all of the Northwind data into the graph.
-When finished, we can run `CALL db.schema.visualization()` in the command line, and the data model should match the one shown below.
-
-image::{img}northwind_data_model.jpg[role="popup-link"]
-
-[#bloom-start]
-== Starting Bloom
-
-First, we need to open the application from Neo4j Desktop.
-Navigate to the Neo4j Desktop project where the database you wish to explore is located. Once the local database instance is started, you can launch Neo4j Bloom by clicking on the blue down arrow on the database instance, as illustrated in the image below.
-
-image::{img}bloom_launch_app.jpg[role="popup-link", width=50%]
-
-[#bloom-perspective]
-=== Choosing a Perspective
-
-When Bloom loads, it will prompt us to choose a perspective.
-A perspective is a business representation of the data in the graph.
-Bloom can generate a default perspective based on the graph data model, or users can create different perspectives to suit their business functions or particular data needs.
-
-The same graph can have multiple perspectives, depending on the different business views and functions.
-For instance, sales may view the data differently than marketing, and accounting will have more access to sensitive data than other functions of the business.
-All of these different functions can categorize and show/hide pieces of the graph to fit the needs of those using the data.
-More information on perspectives and customizing is in the link:/docs/bloom-user-guide/current/bloom-perspectives/[documentation^].
-
-In this guide, we will choose to let Bloom generate a perspective based on the data model in our graph.
-It creates categories based on labels and brings over the relationships and properties found from the data.
-
-We want to click on the button that says `Generate perspective based on my data` if an existing, appropriate perspective does not already exist.
-
-image::{img}bloom_perspective_gallery_1.jpg[role="popup-link"]
-
-You will be given the option to either work from a blank perspective, or to generate one based on existing data. The latter option will autogenerate labels and relationships based on the data in the database.
-
-image::{img}bloom_perspective_gallery_2.jpg[role="popup-link"]
-
-The newly-generated perspective will then show up in the list of potential perspectives to choose from. Click `Use Perspective` to apply the newly-created perspective.
-
-image::{img}bloom_perspective_gallery_3.jpg[role="popup-link"]
-
-The main Bloom window now displays, with the menu tabs along the left and the main navigation and exploration pane (called the scene) covering the rest of the window. The perspective categories are listed in the upper right of the scene.
-
-image::{img}bloom_main_screen_1.jpg[role="popup-link"]
-
-[#bloom-exploration]
-== Exploring data with Bloom
-
-Now we are ready to start exploring our data!
-Bloom uses query syntax that feels similar to natural language in order to express search patterns for parts of the graph users are interested in exploring.
-
-[#bloom-search]
-=== Using the search bar
-
-First, let us start with a small example.
-We want to see the products in our retail system for the Northwind company to sell.
-To do this, we can click on the search bar at the top of the scene and type in `Product`.
-
-image::{img}bloom_product_search_1.jpg[role="popup-link"]
-
-Notice that the search has prompted Bloom to offer suggestions based on what we type and includes the same category colors used in the legend on the right of the scene.
-We can use the recommended suggestion for `Product` by ensuring it is highlighted (the top suggestion is automatically highlighted or we can use the mouse or arrow keys to highlight the option wanted), then clicking on it or pressing the `Enter` key.
-This will bring back all the products and show them on our screen.
-
-image::{img}bloom_products_scene_1.jpg[role="popup-link"]
-
-To display all of the products in the scene, Bloom has zoomed out quite a bit where we cannot see the details of each node.
-We can zoom in closer to a section of the results by using the trackpad on our laptop or mouse or by using the `+` and `-` buttons in the lower right of the scene.
-Once we zoom in a bit, we can see the property Bloom has assigned as the identifier for the node (in this case, the productID number).
-We can double-click on a node to see the rest of the properties we have for it.
-
-image::{img}bloom_pick_product_1.jpg[role="popup-link"]
-
-[#alter-perspective]
-=== Altering the perspective
-
-Now, the `productID` doesn't really have as much business meaning as probably the product name, so let's tell Bloom to show the name instead.
-To do this, we open the perspective drawer in the left menu (top icon).
-
-image::{img}bloom_perspective_drawer_1.jpg[role="popup-link"]
-
-This shows us the perspective we have defined with our categories listed.
-Since we want to change the property shown on the `Product` nodes, we can choose that category from the list.
-Another panel pops out to the right, and we can see all of the properties and whether to show or hide (exclude) them from the perspective.
-
-For our example, we will choose to show the `productName` property, so check the box next to that property under the `Caption` column and uncheck the `Caption` checkbox for the `productID` property.
-If we leave both properties checked as captions, then both properties will be shown on the nodes.
-
-image::{img}bloom_alter_perspective_1.jpg[role="popup-link"]
-
-Finally, we can verify that everything worked by closing the perspective panel and looking at our scene again.
-The `Product` nodes should have adjusted to show the product name, rather than the id.
-
-image::{img}bloom_show_productName_1.jpg[role="popup-link"]
-
-[#expand-nodes]
-== Expanding nodes
-
-Now that we tried a small search and altered the perspective, let us see how Bloom allows us to expand results without writing a different search.
-Using our previous search for the `Product` nodes, we had one node selected to view its properties.
-
-
-[TIP]
-====
-*If you are starting in the middle of this guide...*
-You can open Bloom and type `Product` into the search bar and press the `Enter` key.
-Zooming in a bit on the results, you can click on a node that interests you.
-For this example, we have picked the product for `Ipoh Coffee`.
-====
-
-
-If we have more than one node selected, then we can click in a blank part of the scene to unselect everything, then click on the node we are interested in viewing.
-
-Now, let us clear out all the other nodes that we are not interested in exploring, so we can focus on this node.
-To do that, we can right-mouse click (keyboard:`ctrl/cmd key + click`) on the selected node, which opens an option menu.
-
-image::{img}bloom_node_options_1.jpg[role="popup-link"]
-
-We choose the `Dismiss other nodes` option from the list, and the other nodes (except our `Ipoh Coffee`) node should disappear from our scene.
-
-image::{img}bloom_dismiss_others_1.jpg[role="popup-link"]
-
-Next, we can expand this node to see all of the relationships it has with other nodes.
-We right-click (keyboard:`ctrl/cmd key + click`) on the node to bring up the option menu again, then choose `Expand`. You can either expand by a specific relationship type, or you can expand all of the relationships.
-You also see the number of nodes it will add to the scene in that option, so we know how much data we are adding and how many connections the node has. Select `All`.
-
-image::{img}bloom_expand_node_1.jpg[role="popup-link"]
-
-We should now see a small graph around the product node of all of the relationships and nodes it is connected to.
-All of the nodes match the colors from the legend, so we have some red nodes that are orders (28 of those), an orange node that is a supplier (1 of those), and a category node that is a product category (1 of those).
-
-image::{img}bloom_product_expanded_1.jpg[role="popup-link"]
-
-The scene also shows ids for each one of these nodes, so we can refer to our earlier step for <> to view other properties for the categories.
-
-
-[TIP]
-====
-*Taking it further...*
-you can continue to explore this section of the graph by expanding the `Category` node to see what other products are in that product category or by expanding a particular order to see which customer made the order and what (if any) other products were ordered with our `Ipoh Coffee` product.
-====
-
-[#clearing-scene]
-== Clearing data in Neo4j Bloom
-
-As we explore data with Neo4j Bloom, it continues to add more data to the scene and not remove it (unless we dismiss nodes using each node's option menu).
-However, there is a way to clear the whole scene and start with a blank slate so we can run different queries or explore other parts of the graph without previous results cluttering the view.
-
-To do this, we can right-mouse click (keyboard:`ctrl/cmd key + click`) in a blank spot of the scene to bring up the scene menu and choose the `Clear Scene` option from the list.
-
-image::{img}bloom_clear_scene_1.jpg[role="popup-link"]
-
-This will bring us back to an empty scene.
-
-[#bloom-patterns]
-== Searching patterns
-
-Similar to how we <> earlier in this guide, we can also search for more complex patterns.
-
-We can take it one step further by looking for suppliers who supply more than one product.
-To search for this in Bloom, we need to find products that share a supplier, so we can write a palindrome search (search is the same if re-ordered end to beginning).
-We can type in the terms below and press the `Enter` key to find results.
-
-.Search
-`Product Supplier Product`
-
-.Results
-image::{img}bloom_palindrome_search_1.jpg[role="popup-link"]
-
-Notice that we didn't need to specify relationships in our search.
-Bloom makes suggestions based on what it finds in the graph, so we don't have to type it out.
-When picking from the search suggestions, we can choose which relationships are traversed.
-
-We can zoom in a bit and pick out some interesting clusters.
-For instance, the supplier `Specialty Biscuits` supplies four different products, all with a teatime theme (longbreads, chocolate biscuits, scones, and marmalade).
-
-image::{img}bloom_supplier_products_1.jpg[role="popup-link"]
-
-Feel free to explore others, as well, such as the suppliers `Pavlova` and `New Orleans Cajun Delights`.
-
-[#bloom-order-search]
-=== Finding customers who ordered two specific products
-Let us see if we can find one more complex pattern for finding customers who have ordered two different products.
-We can choose anything, but let's start with the products `Ipoh Coffee` and `Teatime Chocolate Biscuits`.
-
-For this type of query, we can use Bloom's capability of searching indexed properties to shorten our syntax.
-Instead of searching for a product node with a particular property and value, we can type in the indexed property value, and Bloom will find the related node category.
-Just typing `Ipoh Coffee` can also do a full-text search
-
-This is the search we can use to answer our question.
-
-.Search
-`Teatime Chocolate Biscuits Order Customer Order Ipoh Coffee`
-
-.Results
-image::{img}bloom_customer_order_2products_1.jpg[role="popup-link"]
-
-All of the customer nodes (in blue) are those who have ordered both the coffee and chocolate biscuits products.
-We can dig into the different customers to find out more information about who is purchasing them.
-For instance, only one customer is in North America (Tortuga Restaurante in Mexico) - all others are South America or Europe.
-
-[#bloom-shortest-path]
-=== Finding the shortest path between two products
-
-Let us continue searching for more information on our two products - `Ipoh Coffee` and `Teatime Chocolate Biscuits` - by finding the shortest path in the graph from one to the other.
-The shortest path will search the graph to find the fewest number of hops from the start node to the destination node.
-To do this, we need to search and retrieve the two nodes in our scene.
-
-image::{img}bloom_coffee_node_1.jpg[role="popup-link"]
-
-image::{img}bloom_chocbiscuits_node_1.jpg[role="popup-link"]
-
-[TIP]
-====
-*Walkthrough Steps...*
-First, clear the scene.
-Then, search for one product and press `Enter` to add it to the scene.
-Next, search for the second product and press `Enter` to add it to the scene.
-====
-
-Once the coffee and chocolate biscuit products are in our scene, we can select both products (click on product1 + `ctrl/cmd` key + click on product2).
-This should highlight both products by placing a white border around each one of them.
-
-Now, we need to find the shortest path between them.
-We can right-mouse click (keyboard:`ctrl/cmd key + click`) on either of the selected nodes and choose the `Path` option that expands into a submenu.
-
-image::{img}bloom_shortest_path_option_1.jpg[role="popup-link"]
-
-Click on `Shortest path` to choose and run it.
-This gives us the results for the fewest number of hops between the `Teatime Chocolate Biscuits` product and the `Ipoh Coffee` product.
-
-image::{img}bloom_2products_shortest_path_1.jpg[role="popup-link"]
-
-In this case, it looks like there is an `Order` node that includes both products.
-
-[#bloom-wrapup]
-== Wrapping up
-
-In this guide, we walked through navigating and exploring graph data with Neo4j Bloom.
-We saw how to set up Neo4j Bloom for our environment and create (as well as alter) perspectives for viewing the data.
-To explore data and analyze results, we used the search bar to find nodes with a certain label, patterns for product suppliers, and specific patterns for particular products.
-Finally, we learned how to use the option menus and selection within the scene to dismiss miscellaneous nodes, clear the scene, and find shortest paths between nodes.
-
-There are many more things to learn and explore with this tool, so check out the resources listed below to continue the journey.
-
-[#resources]
-== Resources
-
-* Documentation: Learn more about link:/docs/bloom-user-guide/current/[Neo4j Bloom^]
-* Product page: link:/bloom/[About Bloom^]
-* Video Series: See how to use capabilities in Bloom in the https://youtu.be/GV3WCEsHRYI[YouTube series^]
-* YouTube video: https://www.youtube.com/watch?v=tX2OiKcIOok&t=12s[Bloom Tips and Tricks^]
-* Community Site: Ask questions and assist others in the https://community.neo4j.com/c/neo4j-graph-platform/neo4j-bloom[Neo4j Bloom topic^]
-* Guide: link:/developer/graph-visualization/[Visualization tools with Neo4j^]
diff --git a/modules/ROOT/pages/neo4j-browser.adoc b/modules/ROOT/pages/neo4j-browser.adoc
deleted file mode 100644
index a5d34a0e..00000000
--- a/modules/ROOT/pages/neo4j-browser.adoc
+++ /dev/null
@@ -1,255 +0,0 @@
-= Neo4j Browser User Interface Guide
-:level: Beginner
-:page-level: Beginner
-:experimental:
-:neo4j-version: 4.4.0
-:author: Jennifer Reif
-:category: browser
-:tags: graph-platform, web, desktop, guides, styling, queries, configuration, visualization
-:description: This article demonstrates how to use the Neo4j Browser for querying, visualization, and data interaction.
-:page-pagination:
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/#neo4j-tools
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Please launch an link:{aura_signup}[AuraDB Instance] or have link:/download[Neo4j^] downloaded and installed.
-It also helps if you have read the section on xref:graph-database.adoc[graph databases].
-
-[role=expertise {level}]
-{level}
-
-Neo4j Browser is an interactive cypher command shell that allows you to interact with your graph and visualize the information in it.  
-Neo4j Browser is bundled with Neo4j and is available in all editions and versions of Neo4j.
-
-image::{img}neo4j-browser-oneshot.png[]
-
-++++
-
    
-
-
    
-++++
-
-[#launch-neo4j-browser]
-== Launch Neo4j Browser
-
-If you using link:https://neo4j.com/cloud/aura/?ref=developer-guides[AuraDB], go to the Aura Console, and find Neo4j Browser in the "Open" button.
-
-image::{img}open-auradb-browser.png[]
-
-If you are using Neo4j Desktop, you can use the graph apps tab on the left hand side (which looks like four squares) to find Neo4j Browser, which will
-connect to any running database that you have.
-
-image::{img}open-desktop-browser.png[]
-
-If you are running Neo4j in another environment, Neo4j Browser is available via HTTP at `http://localhost:7474` and HTTPS at `https://localhost:7473`,
-substituting the right address for your environment.
-
-[#using-neo4j-browser]
-== Using Built-In Guides
-
-Neo4j Browser contains built-in guides which introduce different concepts.  Now that we can access the graph, we can use these guides to start working with data using link:/developer/cypher[Cypher^].
-
-To use one of these guides, just type the command (such as `:play intro`) into the command line on the right of Neo4j Browser and press kbd:[Enter] or click the play button (image:{img}cypher_run_button.jpg[width=25]).
-
-[%autowidth.spread]
-|===
-|*Title* |*Description* |*Command*
-|Intro |A guided tour of Neo4j Browser |`:play intro`
-|Concepts |Graph database basics |`:play concepts`
-|Cypher |Neo4j's graph query language introduction |`:play cypher`
-|The Movie Graph |A mini graph model of connections between actors and movies |`:play movie graph`
-|The Northwind Database |A classic use case of RDBMS to graph with import instructions and queries |`:play northwind graph`
-|Custom Guides |Use _:play _ to play a custom guide (link:/developer/guide-create-neo4j-browser-guide/[custom guide documentation^])|`:play
-
-https://guides.neo4j.com/restaurant_recommendation`
-|===
-
-The full list of available browser guides link:/developer/browser-guide-list/[can be found here].
-You can also link:/developer/guide-create-neo4j-browser-guide/[create your own custom browser guides] to share learning with colleagues, students, and others in the community.
-
-If you'd like to pin guides to the top so they do not get pushed down as you run additional queries, just use the pushpin button (image:{img}pin_button.png[width=25]) in Neo4j Browser.
-
-[#browser-metadata]
-== Graph Metadata
-
-In the left menu, the top icon is the database section (icon:database[]), where you can find the *currently used* node labels, relationship types, and property keys.
-Clicking on any one of those options runs a quick query to show you a sample of the graph with those elements.
-
-[#browser-styling]
-== Change Graph Appearance
-
-Any query you run in Neo4j Browser will populate your results below the command line.
-You can switch between visual graph, table format, and ascii-table results with the icons on the left side of the result frame.
-You can also adjust by moving the view or dragging nodes to re-arrange them.  To see more of the graph, click any empty spot and drag it.
-
-Nodes are assigned captions by the browser, which selects a property to use.  
-These properties appear below the visualization when a node is selected.
-Larger property sets may be collapsed into a subset, but there is an option to expand them.
-
-image::{img}style_person_node.png[]
-
-For example, if you click on one of the `Movie` nodes in the MovieGraph (`:play movie graph`), then you can see its properties below the graph visual.
-The same applies for `Actor` nodes or the `ACTED_IN` relationships.
-If you click on any label or relationship *above* the graph visualization, you can then choose its styling in the area below the graph.
-Colors, sizes, and captions are selectable from there.
-
-To see this for yourself, you can click on the `Person` label above the graph and change the color, size, and captions of all nodes labeled with `Person`.
-The first image below shows changes to nodes labeled `Person`. The second image shows changes to relationships labeled `ACTED_IN`.
-
-image::{img}style_actedin_relationship.png[]
-
-[#browser-commands]
-== Commands and Keyboard Shortcuts
-
-|===
-|Shortcut |Purpose
-
-m|:help
-|Help System
-
-m|:help commands
-|Useful Commands
-
-m|:clear
-|Clear Frames
-
-m|:style [reset]
-|Styling Popup & Reset
-
-m|:help keys
-|Keyboard Help
-
-|kbd:[Ctrl+Enter] or kbd:[Cmd+Enter]
-|Execute Statement
-
-|kbd:[Ctrl+Up] or kbd:[Cmd+Up]
-|Previous Statement
-
-|kbd:[Shift+Enter]
-|Enter Multiline Mode
-
-|kbd:[/]
-|Move Focus to Editor
-
-|kbd:[ESC]
-|Toggle Editor to Full Screen
-|===
-
-[#browser-tips]
-== Query and Command-Line Tips
-
-.Query Tips
-You can remove all accumulated output frames with `:clear`. The 'X' button at the top right of each pane removes that frame and aborts a (long-)running statement.
-The maximum number of frames that are kept is configurable in the Browser Settings from the left-side menu.
-
-If you want to review a past query, you can find the result pane and click the query above the graph visualisation to pull it back into the editor.
-The keyboard shortcuts listed above will help you work efficiently within the editor area.
-
-You can also write and edit multi-line queries by switching to multi-line editing mode with kbd:[Shift+Enter], then kbd:[Enter] will create newlines.
-You then need to run kbd:[Ctrl+Enter] or kbd:[Cmd+Enter] to run multi-line queries.
-
-.Command Tips
-kbd:[Ctrl+Up] and kbd:[Ctrl+Down] (Mac users, use the kbd:[Cmd] key) allows you to navigate command history, and you can access all command history with `:history`.
-The command history is persisted across Browser restarts.
-
-.Output, Export, & Visualization Tips
-You can switch between `Graph`, `Table`, `Text`, and `Code` views to see the results in various formats by clicking the icons on the left of each pane.
-
-[TIP]
-====
-Don't worry if you don't see any output.
-You might just be in Graph mode, but had your query return tabular/scalar data.
-To see the results, just switch the mode to the Table view.
-====
-
-Query time is reported in the `Table` or `Code` views (*don't rely on that exact timing though*), and it includes the latency and deserialization costs, not just the actual query execution time.
-
-You can also export the results of queries as a CSV or JSON, and for the graph view, you may also export a PNG or SVG image, as shown below.
-
-image::{img}export-graph-browser.png[]
-
-If you enter fullscreen mode of a graph visualization, you can zoom in and out.
-After a node is clicked, it gets a halo, where you can expand and remove nodes from the visualization.
-You can also turn previously dragged nodes loose again.
-
-[#browser-favorites]
-== Setting Favorites
-
-If you currently have an empty frame, you can display some nodes and relationships by using the Favorites (icon:star-o[]) in the left-side menu.
-Neo4j stores a few default favorites to get you started.
-Just click on the *Basic Queries*, then choose **Get Some Data** and run the query.
-This executes the statement `MATCH (n) RETURN n limit 100`, which fetches some nodes.
-
-You can save your own queries as favorites by "starring" them.
-Just populate the Browser command line with the query you want to favorite, then click the Favorites (icon:star-o[]) icon to the right of the command line.
-This will add the query to your Favorites list in the left-side menu.
-To run one of your Favorites, click on the left-side menu Favorites, choose the query, and run it.
-
-To provide a title or helpful info, you can use a comment `// comment` above your query to provide a title.
-The Favorites menu uses your comment as the query name.
-
-Creating folders can help organize your favorites, and you can rearrange them by dragging or delete them if they are no longer useful.
-
-[#browser-styling-adv]
-== Advanced Styling
-
-For more advanced styling, you can bring up the style-viewer with `:style` and copy/paste the graph-style-sheet (GRASS) that is returned.
-You can edit this stylesheet offline, save the file as a .grass file, and drag it back onto the drag-area of the viewer.
-
-[TIP]
-You can reset to the default styles with `:style reset`.
-
-Within the GRASS file, you can change colors, fonts, sizes, outlines, and titles per node label and relationship type.
-It is also possible to combine multiple properties into a caption with `caption: '\{name\}, born in \{born\}';`
-
-[#browser-config]
-== Configuration
-
-The defaults for all the settings can adjusted at any time by going to the configuration option on the left-side menu.
-Some possible config changes and views are listed below.
-
-* You can retrieve the current configuration with `:config`.
-* Individual settings are configured with the following defaults:
-** `:config maxNeighbours:100` - maxiumum number of neighbours for a node
-** `:config maxRows:100` - maximum number of rows for the tabular result
-
-You can also see current stats on your database, such as store sizes, ID allocation, page cache, and transaction info.
-To do this, just type the command `:sysinfo` on the command line.
-
-image::{img}sysinfo_stats.png[]
-
-[#browser-rest]
-== Executing REST requests
-
-You can also execute REST requests with Neo4j Browser. The command syntax is `:COMMAND /a/path {"some":"data"}`.
-The available commands are `:GET`, `:POST`, `:PUT` and `:DELETE`.
-
-A simple query like `:GET /db/data/` inspects the available endpoints of the database, with the returned results formatted in JSON.
-Then, you can retrieve all labels in the database with `:GET /db/data/labels`.
-
-To execute a Cypher statement, you post to the transaction Cypher endpoint like this:
-[source,javascript]
-----
-:POST /db/data/transaction/commit {"statements":[
-     {"statement":"MATCH (m:Movie)  WHERE m.title={title} RETURN m.title, m.released, labels(m)",
-      "parameters":{"title":"Cloud Atlas"}}]}
-----
-
-There are endless possibilities to send and retrieve data using REST.
-In a later guide, you can create an application to interact with Neo4j and use REST endpoints for interaction between you and the database.
-See the link:/developer/language-guides/[Language Guides] section for more information.
-
-[#browser-resources]
-== Further Information
-
-* https://neo4j.com/docs/browser-manual/current/[Neo4j Browser Manual^]
-* https://neo4j-browser.canny.io/feature-requests[Send Neo4j Browser feature requests^]
-* https://neo4j-browser.canny.io/changelog[Neo4j Browser change-log^]
diff --git a/modules/ROOT/pages/neo4j-cloud-aws-ec2-ami.adoc b/modules/ROOT/pages/neo4j-cloud-aws-ec2-ami.adoc
deleted file mode 100644
index f240f699..00000000
--- a/modules/ROOT/pages/neo4j-cloud-aws-ec2-ami.adoc
+++ /dev/null
@@ -1,187 +0,0 @@
-= Deploy to Amazon EC2: Host a Single Instance of Neo4j on AWS 
-:level: Intermediate
-:page-level: Intermediate
-:author: David Allen
-:category: cloud
-:tags: cloud, aws, setup, virtual-machine, deployment
-:description: This guide explains how to deploy a single instance of Neo4j on Amazon's EC2 platform.
-:label: DEPRECATED
-
-{label}
-
-[WARNING]
-This page is no longer supported. To learn more about deploying a single instance of Neo4j on Amazon EC2 platform, visit link:https://aws.amazon.com/marketplace/seller-profile?id=23ec694a-d2af-4641-b4d3-b7201ab2f5f9[the Neo4j page on AWS].
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should know how to run and operate Neo4j locally.
-Knowledge of link:/developer/language-guides/[remote drivers] to access Neo4j from your application helps you connect to your cloud-hosted database.
-
-[#neo4j-aws]
-Before continuing with the guide, make sure you have http://docs.aws.amazon.com/cli/latest/userguide/installing.html[installed the AWS Command Line Interface^].
-If you are not comfortable using the command line, you can https://aws.amazon.com/marketplace/pp/B071P26C9D[install Neo4j using the AWS web console^] instead.
-
-Let's get started!
-
-[#ec2-key]
-== Create EC2 key pair
-
-First, we need to create an http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html[EC2 key pair^] that we will use to connect to our instance via SSH.
-
-[source,shell]
-----
-export KEY_NAME="Neo4j-AWSMarketplace-Key"
-aws ec2 create-key-pair \
-  --key-name $KEY_NAME \
-  --query 'KeyMaterial' \
-  --output text > $KEY_NAME.pem
-----
-
-If you have an existing key that you would like to use instead, you can set `KEY_NAME` to the name of that key pair and ignore the 2nd line in the above command that creates one.
-
-[#security-group]
-== Create security group
-
-Now, we will create a http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-network-security.html[security group^], which will allow us to control access to our instance.
-Run the following command to create a new security group:
-
-[source,shell]
-----
-export GROUP="neo4j-sg"
-aws ec2 create-security-group \
-  --group-name $GROUP \
-  --description "Neo4j security group"
-----
-
-Next, let us open Neo4j's HTTP, HTTPS, and Bolt ports so we can access the server from our application.
-We will also open the SSH port so we can remotely access the instance.
-All of these steps are completed in the below command.
-
-[source,shell]
-----
-for port in 22 7474 7473 7687; do
-  aws ec2 authorize-security-group-ingress --group-name $GROUP --protocol tcp --port $port --cidr 0.0.0.0/0
-done
-----
-
-If you have an existing group that you would like to use instead, set `GROUP` to the name of that group and ignore the rest of the commands.
-
-[#ami-launch]
-== Locate the AMI ID you want to launch
-
-To start the instance, we'll need the ID of an Amazon Machine Image (AMI).
-
-Neo4j publishes many different versions, and the AMI ID depends on which region you want to launch in.
-AMI IDs are specific to regions, so you need to locate the right ID for your version and region needs.
-For that reason, in the instructions below we will provide just a placeholder AMI of `ami-XYZ`.
-
-To get the right AMI ID, you can follow these instructions:
-
-* Open the AWS EC2 console, and select `Images` > `AMIs` on the left-hand nav bar.
-* In the filter, select `Public images` and search for either "neo4j-enterprise" or "neo4j-community" depending on which version you would like to use.
-* You will know you are using the right one when you see the "Owner" field showing this number: `385155106615`
-* Locate the version you want, and use the AMI ID in the script below.
-* As an example: Neo4j Enterprise 3.5.1 is AMI ID `ami-009272c7ac939919d` in `region us-west-2`.
-As new versions are published, they will be mentioned in the https://community.neo4j.com/c/neo4j-graph-platform/cloud[Cloud topic^] on the Neo4j community site.
-
-If you would like to use the CLI to do this same step, the following command locates all image
-IDs and names for the images Neo4j publishes in `us-east-1` region.
-By changing the region, you can find AMI IDs near you.
-
-[source,shell]
-----
-aws ec2 describe-images \
-   --region us-east-1 \
-   --owner 385155106615 \
-   --query "Images[*].{ImageId:ImageId,Name:Name}"
-----
-
-[#instance-start]
-== Start up the instance
-
-We are now ready to start up a Neo4j instance.
-
-Make sure to substitute `ami-XYZ` with your chosen AMI ID from the directions above!
-
-[source,shell]
-----
-aws ec2 run-instances \
-  --image-id ami-XYZ \
-  --count 1 \
-  --instance-type m3.medium \
-  --key-name $KEY_NAME \
-  --security-groups $GROUP \
-  --query "Instances[*].InstanceId" \
-  --region us-east-1
-----
-
-We do not yet have a public DNS name but it should be available after a few seconds.
-
-We can run the following command to find out what it is:
-
-[source,shell]
-----
-aws ec2 describe-instances \
-  --instance-ids [InstanceId] \
-  --query "Reservations[*].Instances[*].PublicDnsName" \
-  --region us-east-1
-----
-
-Please note that your security group settings may affect whether or not a public DNS name is
-associated with your instance.
-If this command does not work for you, check to make sure your security group is properly configured and that it is not associated with a VPC.
-
-Now, let's navigate to `https://[PublicDnsName]:7473` and login with the user name `neo4j` and password `neo4j`.
-
-[NOTE]
---
-Some methods of launching Neo4j on AWS (such as via the AWS marketplace) automatically set the password of
-an instance to the instance ID you launched.
-Other methods (such as when launching enteprise AMIs directly) leave the password as the default `neo4j`.
-In either case, we recommend changing the password immediately after you launch.
---
-
-[#ssh-instance]
-== How do I SSH into the instance?
-
-You can run the following command to SSH into the instance:
-
-[source,shell]
-----
-ssh -i $KEY_NAME.pem ubuntu@[PublicDnsName]
-----
-
-You might get an error about the permissions on the pem file, so don't forget to make sure it isn't accessible by any other users.
-Permissions for the pem can be locked down similar to those shown below (owner = read+write, group = none, users = none).
-
-[source,shell]
-----
-chmod 600 $KEY_NAME.pem
-----
-
-[#vm-workings]
-== How the AWS Virtual Machine Works
-
-Please consult {opsmanual}/cloud-deployments/cloudVms/[Neo4j Cloud VMs^] for details on internals of virtual machines, including configure Neo4j inside of the VM and access various files.
-
-[#terminate-instance]
-== Terminating the instance
-
-Once we have finished using the instance, we can run the following command to terminate it:
-
-[source,shell]
-----
-aws ec2 terminate-instances \
-  --instance-ids [InstanceId] \
-  --region us-east-1
-----
-
-[#aws-questions]
-== Questions?
-
-You can ask questions and connect with other people launching Neo4j in the cloud through the
-https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^].
diff --git a/modules/ROOT/pages/neo4j-cloud-azure-cluster.adoc b/modules/ROOT/pages/neo4j-cloud-azure-cluster.adoc
deleted file mode 100644
index 99f7421f..00000000
--- a/modules/ROOT/pages/neo4j-cloud-azure-cluster.adoc
+++ /dev/null
@@ -1,131 +0,0 @@
-= Deploy a Neo4j Causal Cluster on Microsoft Azure
-:level: Intermediate
-:page-level: Intermediate
-:author: David Allen
-:category: cloud
-:tags: cloud, azure, cluster, setup, virtual-machine, deployment
-:description: This guide explains how to deploy a Neo4j Enterprise Causal Cluster via Microsoft Azure.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-. You have a Neo4j Enterprise license or a link:/lp/enterprise-cloud/?utm_content=azure-marketplace[trial license for Azure^].
-. You should have familiarity with {opsmanual}/clustering/causal-clustering/introduction/[causal cluster architecture^].
-. Knowledge of link:/developer/language-guides/[remote drivers] to access Neo4j from your application helps you connect to your cloud-hosted database.
-
-Let's get started!
-
-[#neo4j-azure]
-== Select Neo4j from the Azure Marketplace
-
-Navigate to the https://azuremarketplace.microsoft.com/en-us/marketplace/[Azure Marketplace^] and
-search for Neo4j, selecting the Neo4j Enterprise Causal Cluster.
-https://azuremarketplace.microsoft.com/en-us/marketplace/apps/neo4j.neo4j-enterprise-causal-cluster?tab=Overview[This link^] will also take you directly to the right marketplace entry.
-
-image::{img}marketplace-entry.png[width=700,float=center,role="popup-link"]
-
-Click "Get it Now".
-A dialog will pop up providing basic details of the solution.
-Click "Continue".
-
-[#config-cluster]
-== Configuring Your Cluster
-
-The next screen provides a description of the solution and some screenshots.
-At the very bottom, the "Resource Manager" deployment approach will be pre-selected.
-Click "Create".
-
-The first step is to configure some basics, shown below.
-
-image::{img}basics.png[float=center,role="popup-link"]
-
-The admin account name will be used for SSH access to the machines in your cluster.
-We recommend creating a new resource group to hold the artifacts of your deployment.
-Once complete, click "OK".
-
-In the next section, you need to choose a password for the Neo4j user.
-You also select the number of core and read replica nodes in your cluster, along with the VM size, disk size/type, and IP allocation method.
-
-The cluster size (core and read replica nodes, along with VM size) is the largest cost driver of the cluster.
-You will be billed for Azure resource time only.
-As this is a "bring your own license" offering, you will not be billed hourly for the Neo4j software.
-
-image::{img}neo4j-settings.png[float=center,role="popup-link"]
-
-Make sure to choose a meaningful name for the Neo4j Cluster Name.
-Most resources in the deployment will be named following this pattern.
-Click "OK" to continue.
-
-On the summary page, you will see a recap of all of your choices.
-At this stage, Azure runs a validation process.
-The most common reason that validation might fail is if you choose VMs that are too large and exceed your Azure quota.
-These errors can be resolved by choosing smaller VMs or by increasing your VM quota.
-
-image::{img}summary.png[float=center,role="popup-link"]
-
-On the final screen, you will be prompted to accept terms and conditions.
-At the bottom of the page, click "Create".
-
-Your cluster deployment has now begun and may take about 10 minutes to complete.
-You can monitor your deployment by checking the resource group you created and the "Deployments" menu underneath that.
-
-image::{img}deploy-in-progress.png[float=center,role="popup-link"]
-
-[#use-browser]
-== Start using Neo4j Browser
-
-After the deployment succeeds, we are now ready to start using Neo4j!
-
-Locate the IP address of any of the core VMs created as part of the deployment.
-Use your browser to access that IP or hostname as follows:
-
-[source,shell]
-----
-https://[MY-AZURE-IP]:7473/
-----
-
-You may log in with user `neo4j` and the Neo4j password you chose in the configuration step.
-You may see an SSL warning screen because the out-of-the-box deployment uses an unsigned SSL certificate.
-
-To verify that the cluster has formed correctly, run the cypher statement `CALL dbms.cluster.overview()`.
-
-[#ssh-instance]
-== How do I SSH into the instance?
-
-You may SSH into any of the machines using the admin credentials chosen in the first step of the deployment, along with the IP or hostname chosen as part of the deployment process.
-
-[#vm-workings]
-== How the Virtual Machines Work
-
-Please consult {opsmanual}/cloud-deployments/cloudVms/[Neo4j Cloud VMs^] for details on internals of virtual machines, including configure Neo4j inside of the VM and access various files.
-
-[#default-config]
-== Your Cluster Default Configuration
-
-The following notes are provided on your default cluster configuration.
-
-* Ports 7687 (bolt) and 7473 (HTTPS access) are the only ports exposed to the entire internet.
-Consider narrowing access to these ports to only your needed networks.
-External unencrypted HTTP access is disabled by default.
-* Ports 5000, 6000, and 7000 are enabled only for internal network access (`10.0.0.8`), as they are needed for internal cluster communication.
-
-[#next-steps]
-== What's Next
-
-* Visit the link:/docs/operations-manual/current/[Neo4j Operations Manual^] for information on how
-configure all aspects of your cluster
-* Add users and change passwords as necessary
-* Consider creating DNS entries to permit addressing your cluster with client applications under a single host name.
-
-[#terminate-cluster]
-== Terminating your Cluster
-
-Should you need to, you can tear down the infrastructure created by deleting the entire resource group you created as part of the deployment.
-
-[#azure-resources]
-== Questions?
-
-You can ask questions and connect with other people launching Neo4j in the cloud through the https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^].
diff --git a/modules/ROOT/pages/neo4j-cloud-azure-image.adoc b/modules/ROOT/pages/neo4j-cloud-azure-image.adoc
deleted file mode 100644
index ff3be100..00000000
--- a/modules/ROOT/pages/neo4j-cloud-azure-image.adoc
+++ /dev/null
@@ -1,128 +0,0 @@
-= Deploy to Microsoft Azure: Host a Single Instance of Neo4j on Azure
-:level: Intermediate
-:page-level: Intermediate
-:author: David Allen
-:category: cloud
-:tags: cloud, azure, instance, setup, virtual-machine, deployment
-:description: This guide explains how to deploy a single instance of Neo4j on Microsoft Azure.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should know how to run and operate Neo4j locally.
-Knowledge of link:/developer/language-guides/[remote drivers] to access Neo4j from your application helps you connect to your cloud-hosted database.
-
-[#azure-image]
-Before continuing with the guide, make sure you have installed the https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest[Azure command line interface^].
-
-[NOTE]
---
-For deploying a Neo4j cluster in Azure, see link:/developer/neo4j-cloud-azure-cluster/[this guide^].
---
-
-Let's get started!
-
-[#marketplace-azure]
-== Neo4j in the Azure Marketplace
-
-In the https://azuremarketplace.microsoft.com/en-us/marketplace/[Azure Marketplace^], Neo4j has multiple offerings.
-For single-node instances, the correct one is called https://azuremarketplace.microsoft.com/en-us/marketplace/apps/neo4j.neo4j-enterprise?tab=Overview[Neo4j Enterprise VM^].
-
-Navigate to the Marketplace entry in the link above, and click "Get it Now".
-The marketplace will prompt you to click "Continue" after providing some basic version and publisher information.
-Click "Continue".
-
-image::{img}azure-launch-page.png[]
-
-On the next screen, Azure will inform you that this offering is a "Bring Your Own License" offering.
-If you are an existing Neo4j customer, you already have a license.
-If you need an evaluation license, you can link:/lp/enterprise-cloud/?utm_content=azure-marketplace[obtain one here^].
-Click the "Create" button.
-
-[#config-instance]
-== Configuring Your Instance
-
-On the next page, fill out all of the details for the VM you want to create.
-The most important settings to consider are `Size` (which controls the available CPU and memory) and `Disks` (where you can configure high speed SSDs and larger disk capacity sizes).
-
-In general, we recommend deploying an instance into a new Azure resource group.
-The wizard will allow you to choose a new name, and the resource group will be created as part of the deploy process.
-
-You will be required to configure administrator access, which you can do either with a password
-or an SSH public key.
-
-image::{img}configure-vm-details.png[]
-
-When your settings are just as you want them, click the "Review + Create" tab and then the "Create" button at the bottom of the screen.
-
-image::{img}review-and-create.png[]
-
-After you click "Create", Azure will create a deployment for your new instance.
-You can watch the progress of the deployment as it goes.
-All necessary firewall rules will be created for you, so unless you have custom requirements, you will not need to change anything.
-
-After your deployment succeeds, it may take a minute or two for the Neo4j service to come up.
-
-[#access-instance]
-== Access Your New Instance
-
-Navigate to the resource group that you used and locate the VM you created.
-It will be named after the name you chose in the initial configuration step.
-Locate the VM's IP address and then use your browser to connect to Neo4j via:
-
-[source,shell]
-----
-https://[MY_AZURE_IP]:7473/
-----
-
-The default username/password for your instance is `neo4j/neo4j`, and Neo4j will force you to change it on your first login.
-Make sure to change this password right after deployment.
-
-Because you do not have a hostname configured or a valid SSL certificate by default, your browser will warn you that the certificate is not trusted.
-This is expected, and you can configure a certificate later, if you choose.
-
-Bolt clients may connect to this same IP address on port 7687.
-
-[#ssh-instance]
-== How do I SSH into the instance?
-
-You can use any SSH client to connect to the public IP of your instance with the administrative user credentials (password or SSH key) configured during the launch.
-This user will have sudo access on the machine.
-
-Inside the VM, you can check the status of the Neo4j service like this:
-
-[source,shell]
-----
-$ sudo systemctl status neo4j
-● neo4j.service - Neo4j Graph Database
-   Loaded: loaded (/etc/systemd/system/neo4j.service; enabled; vendor preset: enabled)
-   Active: active (running) since Wed 2018-03-14 11:19:56 UTC; 15min ago
- Main PID: 1290 (pre-neo4j.sh)
-    Tasks: 46
-   Memory: 325.7M
-      CPU: 20.690s
-   CGroup: /system.slice/neo4j.service
-           ├─1290 /bin/bash /etc/neo4j/pre-neo4j.sh
-           └─1430 /usr/bin/java -cp /var/lib/neo4j/plugins:/etc/neo4j:/usr/share/neo4j/lib/*:/var/lib/neo4j/plugins/* -server -XX:+UseG1GC
-----
-
-[#vm-workings]
-== How your new virtual machine works
-
-Please consult {opsmanual}/cloud-deployments/cloudVms/[Neo4j Cloud VMs^] for details on internals of virtual machines, including configure Neo4j inside of the VM and access various files.
-
-[#delete-instance]
-== Deleting the instance
-
-When you are finished with the instance, and if you chose to deploy into a new resource group, delete the entire resource group.
-This will remove everything for you.
-If you deployed into an existing resource group, you will need to delete the resources that are part of the
-deployment individually.
-
-[#azure-resources]
-== Questions?
-
-You can ask questions and connect with other people launching Neo4j in the cloud through the https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^].
\ No newline at end of file
diff --git a/modules/ROOT/pages/neo4j-cloud-google-image.adoc b/modules/ROOT/pages/neo4j-cloud-google-image.adoc
deleted file mode 100644
index 51ba8a93..00000000
--- a/modules/ROOT/pages/neo4j-cloud-google-image.adoc
+++ /dev/null
@@ -1,131 +0,0 @@
-= Deploy to Google Cloud Platform: Host a Single Instance of Neo4j on GCP
-:level: Intermediate
-:page-level: Intermediate
-:author: David Allen
-:category: cloud
-:tags: cloud, google, instance, setup, virtual-machine, deployment
-:description: This guide explains how to deploy a single instance of Neo4j on Google Cloud Platform.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should know how to run and operate Neo4j locally.
-Knowledge of link:/developer/language-guides/[remote drivers] to access Neo4j from your application helps you connect to your cloud-hosted database.
-
-[#neo4j-gcp]
-Before continuing with the guide, make sure you have installed the Google Cloud SDK and authenticated the gcloud command line interface.
-More information on these steps is provided in the link:{opsmanual}/cloud-deployments/neo4j-gcp/single-instance-vm/#_prerequisites_2[documentation^].
-
-[NOTE]
---
-For deploying a Neo4j cluster in GCP, see link:/developer/neo4j-google-cloud-launcher/[this guide^].
---
-
-Let's get started!
-
-[#firewall-rules]
-== Create Firewall Rules
-
-By default, Google will not let traffic in and out of instances unless we specify to allow it.
-So, we will need to define a firewall rule to allow traffic on specific ports, though we don't have to open port 22 for SSH, as that is already done.
-
-Command syntax for the firewall rule is shown and explained in the link:{opsmanual}/cloud-deployments/neo4j-gcp/single-instance-vm/#_create_a_firewall_rule_to_access_your_instance[Neo4j documentation^].
-
-In Google's Cloud Console, here's what your rule should look like:
-
-image::{img}firewall-rule.png[role="popup-link"]
-
-[#compute-instance]
-== Create a Google Compute Instance from the Neo4j Public Image
-
-Launching the instance is a single command:
-
-[source,shell]
-----
-gcloud config set project my-project-id
-gcloud compute instances create my-neo4j-instance \
-    --image neo4j-enterprise-1-3-4-9-apoc \
-    --tags neo4j \
-    --image-project launcher-public
-----
-
-The first line sets your project configuration to ensure you know where you are launching your instance.
-The second line launches an image found in a public project that we have provided.
-The image name `neo4j-enterprise-1-3-4-9-apoc` corresponds to an Ubuntu-based image that contains Neo4j 1:3.3.9 with the APOC plugin.
-
-If you get an "image not found" error, this may mean you are attempting to launch a version that Neo4j has not published.
-
-Note that the `gcloud` tool comes with many command line options that are not covered here.
-Consult the https://cloud.google.com/sdk/gcloud/reference/compute/instances/create[GCloud documentation^] for a large number of options dealing with machine type, memory, available storage, and so on.
-
-The `--tags` argument is important because it allows us to configure the correct network
-permissions in the next step.
-By default, Google will block all external access to the network services unless you open them.
-
-You know that the launch succeeded when you see output like this:
-
-[source,shell]
-----
-Created [https://www.googleapis.com/compute/v1/projects/testbed-187316/zones/us-east1-b/instances/my-neo4j-instance].
-NAME               ZONE        MACHINE_TYPE   PREEMPTIBLE  INTERNAL_IP  EXTERNAL_IP     STATUS
-my-neo4j-instance  us-east1-b  n1-standard-1               10.142.0.3   35.231.125.253  RUNNING
-----
-
-[#access-instance]
-== Access Your New Instance
-
-Navigate to `https://[External_IP]:7473` and login with the user name `neo4j` and password `neo4j`.
-You will be prompted to change the password immediately.
-
-Because you do not have a hostname configured or a valid SSL certificate by default, your browser will warn you that the certificate is not trusted.
-This is expected, and you can configure a certificate later, if you choose.
-
-[#ssh-instance]
-== How do I SSH into the instance?
-
-You can run the following command to SSH into the instance:
-
-[source,shell]
-----
-gcloud compute ssh my-neo4j-instance
-----
-
-Inside the VM, you can check the status of the neo4j service like this:
-
-[source,shell]
-----
-$ sudo systemctl status neo4j
-● neo4j.service - Neo4j Graph Database
-   Loaded: loaded (/etc/systemd/system/neo4j.service; enabled; vendor preset: enabled)
-   Active: active (running) since Wed 2018-03-14 11:19:56 UTC; 15min ago
- Main PID: 1290 (pre-neo4j.sh)
-    Tasks: 46
-   Memory: 325.7M
-      CPU: 20.690s
-   CGroup: /system.slice/neo4j.service
-           ├─1290 /bin/bash /etc/neo4j/pre-neo4j.sh
-           └─1430 /usr/bin/java -cp /var/lib/neo4j/plugins:/etc/neo4j:/usr/share/neo4j/lib/*:/var/lib/neo4j/plugins/* -server -XX:+UseG1GC
-----
-
-[#vm-workings]
-== How your new virtual machine works
-
-Please consult {opsmanual}/cloud-deployments/cloudVms/[Neo4j Cloud VMs^] for details on internals of virtual machines, including configure Neo4j inside of the VM and access various files.
-
-[#delete-instance]
-== Deleting the instance
-
-Once we have finished using the instance, we can run the following command to delete it:
-
-[source,shell]
-----
-gcloud compute instances delete my-neo4j-instance
-----
-
-[#gcp-resources]
-== Questions?
-
-You can ask questions and connect with other people launching Neo4j in the cloud through the https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^].
diff --git a/modules/ROOT/pages/neo4j-cloud-hosting-providers.adoc b/modules/ROOT/pages/neo4j-cloud-hosting-providers.adoc
deleted file mode 100644
index 00f22467..00000000
--- a/modules/ROOT/pages/neo4j-cloud-hosting-providers.adoc
+++ /dev/null
@@ -1,63 +0,0 @@
-= Other Neo4j Cloud Hosting Providers
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:category: cloud
-:tags: cloud, hosting, deployment, partners
-:description: This guide introduces Neo4j's cloud-hosting partners.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should know how to run and operate Neo4j locally.
-Knowledge of link:/developer/language-guides[remote drivers] to access Neo4j from your application helps you connect to your cloud-hosted database.
-
-[#neo4j-providers]
-There are a number of other options that provide hosting of Neo4j instances in the cloud, oustide of those covered in earlier guides in this section.
-This page will point to any companies and resources for additional alternatives.
-
-[NOTE]
-If you provision databases via their offering, you enter a contract with those partners, not with Neo4j.
-
-////
-==== GraphGrid
-
-image::{img}20160914051238/graphgrid.jpg[width=300,float=right]
-
-* *GraphGrid Data Platform* is the complete Neo4j data platform, architected for the enterprise to unleash the full power of Neo4j into your modern graph data architecture.
-* *High Performance Neo4j Clusters* The full power of Neo4j at your fingertips with thousands of graph compute cores for big graph data processing and graph data warehouse management.
-* *On Graph Analytics in Real Time* Don't drown in your disconnected data lake. Embrace the connectedness of your data and discover meaningful insights today.
-* *Setup your Neo4j Cluster in Minutes* Enterprise-grade graph data management platform enabling batch, interactive and real-time analytics and data processing applications simultaneously across your connected graph data.
-
-==== GrapheneDB
-
-image::{img}graphenedb.png[width=400,float=right]
-
-http://www.graphenedb.com/[GrapheneDB] offers Neo4j instances from free plans suitable for evaluation and testing to production-ready plans for demanding graph applications, with following features:
-
-* Pay-as-you-go pricing model
-* On-demand instant provisioning in several regions on AWS, Azure
-* Online, automated and manual backups
-* 24x7 monitoring and support
-* Support for community plugins and custom extensions
-* DB management tools: access server logs, update configuration, cloning, upgrading or downgrading a server
-* Advanced deployment options with Neo4j Enterprise Edition
-
-GrapheneDB is also available as a link:https://elements.heroku.com/addons/graphenedb[Heroku Add-On] with free and tiered plans.
-////
-[#graphstory-host]
-== GraphStory
-
-image::{img}graphstory.png[width=400,float=right]
-
-link:http://www.graphstory.com[Graph Story] provides end-to-end Graph Database hosting for Community and Enterprise Neo4j, including:
-
-* On-demand instances with monthly pricing
-* Available on AWS, Azure, Google Compute Engine, Digital Ocean and Heroku
-* World-class support via chat & email
-* Data management tools for Import & Export, Backup & Restore, and Clear & Refresh
-* Scheduled backups & log access
-* Add-ons for New Relic and Logentries
diff --git a/modules/ROOT/pages/neo4j-cloud-vms.adoc b/modules/ROOT/pages/neo4j-cloud-vms.adoc
deleted file mode 100644
index 356204a8..00000000
--- a/modules/ROOT/pages/neo4j-cloud-vms.adoc
+++ /dev/null
@@ -1,61 +0,0 @@
-= Neo4j Cloud Virtual Machines
-:level: Intermediate
-:page-level: Intermediate
-:author: David Allen
-:category: cloud
-:tags: cloud, virtual-machine, configuration, deployment
-:description: This guide describes how Neo4j deployed in cloud virtual machines operate, and how they differ from other installation platforms for Neo4j.
-
-.Goals
-[abstract]
-{description}
-
-[#cloud-vms]
-== Basics and File Locations
-
-Neo4j cloud VMs are based off of the Ubuntu distribution of Linux.
-When Neo4j is installed on the VM, the method used to do this matches the {opsmanual}/installation/linux/debian/[Debian install instructions^] provided in the Neo4j operations manual.
-
-Because cloud images are based on the standard Neo4j Debian package, file locations match the file locations described in the {opsmanual}/configuration/file-locations/[Neo4j Operations Manual^], where `neo4j-home` is set to `/var/lib/neo4j`.
-
-The remainder of this page deals only with topics that are different from a standard Linux install.
-If you have any other question not covered by this page, please consult the Operations Manual for Linux installs.
-
-[#vm-config]
-== VM configuration
-
-An important thing to note about cloud version of neo4j is that you should not modify the `/etc/neo4j/neo4j.conf` file directly, but rather modify `/etc/neo4j/neo4j.template`.
-The system service that restarts neo4j calls a shell script called `pre-neo4j.sh`.
-
-In cloud environments, much of the external configuration environment may change.
-A machine may have a different IP address or different set of tags when it restarts.
-Because of this dynamic nature, the `pre-neo4j.sh` script dynamically overwrites the normal `neo4j.conf` file each time the system service starts.
-As a result, you should configure the template used to do those substitutions and not the configuration file itself directly, as it will be automatically overwritten.
-
-[#config-tags]
-== Configuration via Tags
-
-On cloud platforms, you may set general `neo4j.conf` configuration parameters as tags on the VM, which will be picked up and substituted into the configuration file.
-In this way, for example, you might set a tag on a VM of `dbms_backup_enabled` with the value `false`, in order to disable the backup port.
-Note that naming conventions for tags follow the same conventions as docker containers, and dots in a configuration parameter's name should be replaced by underscore characters.
-
-When changing VM tags, the configuration is not immediately applied to the Neo4j system service running inside of the VM.
-To affect these changes, please {opsmanual}/installation/linux/systemd/[restart the system service].
-
-[#neo4j-interact]
-== Interacting with the Neo4j Service
-
-You can get system status for neo4j within the VM by executing the following:
-
-[source,shell]
-----
-systemctl status neo4j
-----
-
-For more information or help, feel free to reach out to us on the the https://community.neo4j.com/[Neo4j Community Forum^]!
-
-[#cloudvm-resources]
-== Questions?
-
-You can ask questions and connect with other people launching Neo4j in the cloud at the
-https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site^].
\ No newline at end of file
diff --git a/modules/ROOT/pages/neo4j-database.adoc b/modules/ROOT/pages/neo4j-database.adoc
deleted file mode 100644
index 0f3edcc9..00000000
--- a/modules/ROOT/pages/neo4j-database.adoc
+++ /dev/null
@@ -1,158 +0,0 @@
-= Neo4j Graph Database
-:author: Jennifer Reif
-:category: documentation
-:tags: graph-platform, graph, database, features, versions
-:page-pagination:
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/#graph-database
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#database]
-Neo4j's primary product and focus is our graph database that stores data in the form of nodes and relationships.
-It handles both transactional and/or analytics workloads and is optimized for traversing paths through the data using the relationships in the graph to find connections between entities.
-
-[#neo4j-features]
-== New 4.x features
-
-With each update of Neo4j, the database improves performance and features (plus bug fixes, too!).
-It can be difficult to locate which features and improvements belong to which releases, as well as the best resources for learning and using them.
-This guide will provide some insight into the highlights and where to go for more information.
-
-=== Multi-database
-
-This capability allows you to operate and manage multiple databases within a single installation of the Neo4j DBMS.
-The data can be segmented by use case, sensitivity, or business applications into different databases.
-
-link:/developer/manage-multiple-databases/[Developer Guide: Multiple Databases] +
-link:{cyphermanual}/administration/databases/[Docs: Cypher commands to manage databases^] +
-link:{opsmanual}/manage-databases/introduction/[Docs: Multi-database concepts^]
-
-=== Neo4j Fabric
-
-Neo4j Fabric provides the ability to shard graph data by allowing users to break a larger graph down into individual, smaller graphs and store them in separate databases.
-These shards can be accessed individually or aggregated to see all of the data, when required.
-
-link:/developer/neo4j-fabric-sharding/[Developer Guide: Neo4j Fabric] +
-https://markhneedham.com/blog/2020/02/03/neo4j-cross-database-querying-fabric/[Blog post: Querying across Databases with Neo4j Fabric] +
-link:{opsmanual}/fabric/introduction/[Docs: Neo4j Fabric^]
-
-=== Cypher
-
-Cypher is a graph query language that allows you to read and write data to the graph.
-It is a very visual, yet powerful, way to interact with nodes and relationships.
-Consistent improvements for built-in functions, syntax, and performance are announced nearly every release.
-
-
-link:/developer/cypher/subqueries/[Developer Guide: Cypher Subqueries]
-
-Some of the significant improvements include:
-
-link:/developer/cypher/subqueries/[Developer Guide: Cypher Subqueries] +
-link:/developer/cypher/filtering-show/[Developer Guide: Filtering SHOW commands] +
-link:/developer/cypher/replanning/[Developer Guide: Cypher Runtime and Replanning]
-
-
-=== Data access controls
-
-Securing data is important and complex, especially when dealing with sensitive data and regulations surrounding it.
-There are many ways to of securing the data - access, user roles, protected environments, system architecture, and more.
-Neo4j provides these capabilities and regularly improves or adds to them.
-
-https://youtu.be/jQIqXVVYPE0[Video: Schema-based security^] +
-https://medium.com/neo4j/role-based-access-control-in-neo4j-4-1-3e65d5b3f45[Blog post: Role-based access control in Neo4j^] +
-link:{opsmanual}/authentication-authorization/[Docs: Neo4j authentication/authorization^] +
-link:{cyphermanual}/administration/security/[Docs: Cypher commands for managing user access^]
-
-=== Reactive drivers
-
-Neo4j reactive drivers incorporate the principles of the reactive manifesto in passing data between the database and clients using the drivers.
-Developers can take advantage of the reactive approach to process queries and return results.
-This means that communication between the driver and the database can be managed and adjusted dynamically according to data needs of the client.
-
-https://medium.com/neo4j/spring-data-neo4j-rx-released-into-the-wild-f1473951f91d[Blog post: Spring Data Neo4j Reactive^] +
-https://medium.com/neo4j/having-another-go-e50823b6fc79[Blog post: Go Driver 1.8^] +
-link:/docs/java-manual/current/session-api/reactive//[Docs: Reactive Sessions^]
-
-[#neo4j-versions]
-== Neo4j Versions
-
-Though there are many versions of Neo4j still in use and available for download, we will focus on the few latest releases here and leave you with some resources for accessing information about each.
-
-=== Support plans
-
-There are a few terms Neo4j uses to categorize support plans and timelines for each release version.
-We can cover a brief overview here.
-
-* Short-term support (STS) - the minimum support length of 18 months after release date.
-Every version of Neo4j comes with this minimum support time.
-* Long-term support (LTS) - the support length of 36 months after release date.
-Each last minor release of a major version comes with this minimum support time (example, 3.5).
-* Extended-term support (ETS) - optional support agreement for customers willing to purchase.
-Provides 36 months of support from release date, rather than the standard 18 months.
-
-The table below shows the latest releases and planned support timelines and extensions for each Neo4j version.
-
-[%autowidth.spread]
-|===
-|*Version* |*Support category* |*Support Start* |*Support End* |*Extended support end*
-|version 4.1 |Short-term support |June 2020 |December 2021 |June 2023
-|version 4.0 |Short-term support |January 2020 |June 2021 |December 2022
-|version 3.5 |Long-term support |November 2018 |December 2021 |N/A
-|===
-
-=== Neo4j 4.1 (June 2020)
-
-link:/blog/introducing-neo4j-4-1/[Blog post: 4.1 Announcement^] +
-link:/docs/operations-manual/current/[Neo4j 4.1 Docs^] +
-link:/release-notes/neo4j-4-1-0/[Release Notes^] +
-https://github.com/neo4j/neo4j/wiki/Neo4j-4.1-changelog[4.1 Changelog^] +
-https://www.youtube.com/playlist?list=PL9Hl4pk2FsvV__9u8fho4oVpP1DvJAZhm[YouTube playlist: 4.1 Features presented by the engineers who built them^] +
-https://youtu.be/of53lUFs5hc[Video: 4.1 Whirlwind Tour with Michael Hunger^]
-
-++++
-
    
-
-
    
-++++
-
-++++
-
    
- 
     
-
    
-++++
-
-=== Neo4j 4.0 (January 2020)
-
-*Official documentation -* +
-link:/docs/operations-manual/4.0/[Docs: Neo4j 4.0 Operations^] +
-link:/docs/cypher-manual/4.0/[Docs: 4.0 Cypher^] +
-link:/docs/migration-guide/4.0/[Migration guide^] +
-
-*Feature highlights -* +
-link:/release-notes/neo4j-4-0-0/[Initial Release Notes^] +
-https://dzone.com/articles/see-whats-new-in-neo4j-40[DZone article: What's new in 4.0^] +
-https://medium.com/neo4j/neo4j-treasure-map-where-to-find-all-of-the-4-0-resources-you-need-948cf71004f9[Blog post: 4.0 Resource Treasure Map^] +
-
-*Technical deep-dives -* +
-Developer guides: link:/developer/manage-multiple-databases/[multiple databases], link:/developer/neo4j-fabric-sharding/[Neo4j sharding], link:/developer/multi-tenancy-worked-example/[multi-tenancy example], link:/developer/subqueries/[Cypher subqueries] +
-https://youtu.be/GcaJ-aVLzr4[Video: Migrating from Neo4j 3.5 to 4.0^] +
-https://medium.com/neo4j/new-neo4j-4-0-features-copy-a-database-and-more-c51d1744a7e3[Blog post: Copy a database^] +
-NODES online developer conference sessions: https://youtu.be/jQIqXVVYPE0[4.0 security^], https://youtu.be/mrs4TQwNo6g[Spring Data Neo4j Reactive^], https://youtu.be/AfhJcyys108?t=1504[Behind-the-Tech in 4.0^] +
-https://medium.com/neo4j/introducing-neo4j-drivers-for-the-next-gen-database-96981f65e8b8[Drivers for Neo4j 4.0^] +
-https://markhneedham.com/blog/2020/01/28/neo4j-database-dump-docker-container/[Blog post: Database dump in a Docker container^] +
-https://adamcowley.co.uk/neo4j/multi-tenancy-neo4j-4.0/[Blog post: Connecting an application with multi-tenancy and multi-db^] +
-https://adamcowley.co.uk/neo4j/sharding-neo4j-4.0/[Blog post: How and when to implement sharding^] +
-https://blog.bruggen.com/2020/02/securing-sample-fraud-graph-with-neo4j.html[Blog post: Securing a fraud graph^]
-
-=== Neo4j 3.5 (November 2018)
-
-link:/docs/operations-manual/3.5/[Neo4j 3.5 Docs^] +
-link:/release-notes/neo4j-3-5-0/[Initial Release Notes^]
-
-[#db-other-resources]
-== Other Resources
-
-link:/release-notes/[All Neo4j Release Notes^]
-
diff --git a/modules/ROOT/pages/neo4j-desktop.adoc b/modules/ROOT/pages/neo4j-desktop.adoc
deleted file mode 100644
index 746be2c8..00000000
--- a/modules/ROOT/pages/neo4j-desktop.adoc
+++ /dev/null
@@ -1,248 +0,0 @@
-= Neo4j Desktop User Interface Guide
-:level: Beginner
-:page-level: Beginner
-:experimental:
-:neo4j-version: 3.3.4
-:author: Jennifer Reif
-:category: desktop
-:tags: graph-platform, desktop, activation, graph, manage-graph, database
-:description: This article demonstrates how to use the Neo4j Desktop GUI for managing instances of Neo4j locally for development.
-:page-pagination:
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/#neo4j-tools
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-It helps if you have read the section on xref:graph-database.adoc[graph databases] or tried Neo4j in our https://neo4j.com/sandbox/?ref=developer-neo4j-desktop[Sandbox^].
-
-[role=expertise {level}]
-{level}
-
-NOTE: There is now an official https://neo4j.com/docs/desktop-manual/current/[Neo4j Desktop Manual^] and also the option to https://neo4j-desktop.canny.io/feature-requests[send feature requests^] and https://neo4j-desktop.canny.io/changelog[read the change-log^].
-
-[#install-neo4j-desktop]
-link:/download/?ref=developer-neo4j-desktop[If you haven't already, download Neo4j Desktop Now^].
-
-Instructions for installing Neo4j Desktop will be shown after you click the download button. These instructions are customized for your OS.
-
-NOTE: The activation key will be displayed at the top of the instruction page. Copy it as you need it when you first open Neo4j Desktop.
-
-Using the provided instructions, follow the steps to install and start the Neo4j Desktop application.
-
-[#what-is-neo4j-desktop]
-== What is Neo4j Desktop
-
-Neo4j Desktop is a Developer IDE or Management Environment for Neo4j instances similar to Enterprise Manager, but better.
-You can manage as many projects and database servers locally as you like and also connect to remote Neo4j servers.
-Neo4j Desktop comes with a free Developer License of Neo4j Enterprise Edition. The Java Runtime is also bundled.
-
-Each database server managed through desktop can be configured, upgraded and maintained through the UI, no command line needed.
-You can install popular Neo4j extensions like link:/labs/apoc[APOC^] or the link:/developer/graph-data-science[Graph Data Science^] library with a single click.
-
-Similar to IDE plugins, link:/developer/graph-apps[Graph Apps^] are helper web applications that make interacting with Neo4j easier.
-They cover visualization and querying with Neo4j Browser and Bloom, import tools for relational databases, and monitoring tools to query log analyzers.
-
-Both Neo4j Desktop as well as Graph Apps offer automatic updates.
-
-Here is a video that shows you how to get started with Neo4j Desktop:
-
-video::uR9-NLxLzg4[youtube]
-
-[#desktop-activate]
-== Opening Neo4j Desktop for the First Time
-
-If you are opening Neo4j Desktop for the first time, it should ask you to register the software with an activation key.
-This activation key is generated when you first download Neo4j Desktop and will be displayed on the download confirmation page.
-Keep it somewhere safe.
-
-image::{img}desktop_registration_may2020.jpg[float="right"]
-
-Copy this activation key into the Software registration form and click Activate to continue.
-
-As a new user to Neo4j Desktop, you will be greeted with a project containing the sample database containing https://github.com/neo4j-graph-examples/movies[Movies and Actors^].
-
-image::{img}generic-first_open_desktop.png[role="popup-link"]
-
-This database is already started for you. If for some reason the database is not started, you can start it by hovering to the right of the database name and clicking the Start button.
-The sample database is created using a randomly generated password, which is stored within Neo4j Desktop and used by Neo4j Browser to authenticate against the database.
-
-This database has a Browser Guide that introduces you to the database. The database must be started before you open the Browser Guide.
-
-Clicking the Open button for the Sample database will open up the Neo4j Browser with a Browser Guide that will guide you through your first queries on this Neo4j database.
-
-image::{img}start-movie-guide.png[role="popup-link"]
-
-Here is how the Browser Guide appears when you first open it:
-
-image::{img}movie-guide-started.png[role="popup-link"]
-
-Within the Guide, you can navigate through the pages to learn more about accessing this sample database.
-
-[#desktop-navigate]
-== Navigation
-
-The left sidebar of Neo4j Desktop shows high-level icons for projects, graph applications, settings, activation keys, and Neo4j info.
-At the top, the folder with a bookmark (image:neo4j-desktop-project-icon.png[width=30]) shows the list of projects you have created.
-
-=== Settings
-
-Application settings can be modified or viewed by clicking the gear towards the bottom of the left-side menu in Neo4j Desktop (image:neo4j-desktop-settings-icon.png[width=30]).
-You can modify any of these settings at any time.
-
-The first block is for privacy settings where you can adjust what data is sent back to Neo4j for assistance and future improvements.
-The next two sections are where you can configure a proxy or run Neo4j Desktop in offline mode.
-
-
-[TIP]
-====
-We do not recommend running Neo4j Desktop in offline mode unless required by your company.
-Offline mode prevents software updates from occurring as well as downloading required resources.
-====
-
-=== Other sidebar sections
-
-The `DBMS` section shows which versions of the database are used in your projects (if any) and lists the currently set up projects and databases.
-
-Another section, `Graph Apps` enables you to add and integrate graph apps with Neo4j.
-
-In addition, you can access the javascript console of an application for debugging and error reporting through the developer tools.
-
-The `Notification Center` section enables you to update software.
-
-The `Software Keys` section (image:{img}activation_keys_icon.jpg[width=20]) shows the licenses that are attached to this installation in Neo4j Desktop, which includes activation of Desktop itself along with any graph app licenses.
-
-The `About` section (image:{img}neo4j_icon.jpg[width=20]) is information about Neo4j itself and includes links to the main company website, as well as release and license information.
-
-[#desktop-create-project]
-=== Creating a Project
-
-include::./_includes/desktop-create-project.adoc[]
-
-To remove the project, hover over the `Project` row in the projects menu and click the trash bin icon that appears on the right side (image:neo4j-desktop-remove-icon.png[width=30]).
-
-[#desktop-create-DBMS]
-== Creating a DBMS
-
-A DBMS is a Neo4j Server instance that contains the `system` and `neo4j` databases, but can contain more databases (multi-db feature of Neo4j 4.x Enterprise Edition)
-
-include::./_includes/desktop-create-and-start-DBMS.adoc[]
-
-You have now created y our own DBMS!
-
-[#desktop-manage-dbms]
-== Managing the DBMS
-
-After you have created a DBMS you may want to:
-
-* Add a plugin (library) that will be used for the instance.
-* Add a file for importing data into a database of the DBMS.
-* Open a terminal window for the DBMS.
-* Modify runtime settings for the DBMS.
-* View log files for the DBMS.
-* Reset the password for the DBMS.
-
-[#desktop-add-plugin]
-=== Adding a plugin
-
-A plugin is additional functionality that can be enabled for a DBMS. Currently with the latest release of Neo4j Desktop that uses a 4.2.x DBMS,
-the following plugins come with Neo4j Desktop that you can install:
-
-* Awesome Procedures for Cypher (APOC)
-* Graph Data Science Library
-* GraphQL
-* Neo4j Streams
-
-If you click the DBMS, a pane should open on the right with the Details, Plugins, and Upgrade tabs. If you do not see this pane on the right, make sure your Neo4j Dekstop window is wider to accomodate this pane.
-
-image::{img}generic-view_plugins.png[role="popup-link"]
-
-You can install or uninstall a plugin from this tab. The version of the plugin must be compatible with the version of the DBMS. Neo4j Desktop will ensure compatibility.
-After you install or uninstall a plugin, you must restart the DBMS.
-
-Here we see that we can now access APOC procedures for this DBMS:
-
-image::{img}generic-APOC_installed.png[role="popup-link",width=400]
-
-[#desktop-add-files-for-import]
-=== Adding files to import
-
-The default local folder where Cypher expects CSV files when you are importing data is the import folder for the DBMS.
-Open a finder window on your system by hovering over the three dots to the right started DBMS and select *Open folder*, and then *Import*:
-
-image::{img}generic-open_import_folder.png[role="popup-link"]
-
-You can copy or move CSV files into this *import* folder. With CSV files here, you can use `LOAD CSV` to import data into your database.
-
-[#desktop-open-terminal]
-=== Opening a terminal
-
-You can open a terminal window that will be automatically ready for using your Neo4j DBMS. In a terminal window you can use the command-line tools that are available in your installation such as:
-
-* cypher-shell
-* neo4j-admin
-
-To open a terminal window you select the dropdown menu to the right of the Open button and select `Terminal`:
-
-image::{img}generic-open_terminal.png[role="popup-link",width=400]
-
-In the terminal window you can navigate to the `bin` directory to execute the `cypher-shell` or `neo4j-admin` tools.
-
-[#desktop-DBMS-settings]
-=== Modifying settings for the DBMS
-
-You can view or modify settings for the DBMS by selecting the dropdown menu to the right of the Open button and select `Settings`.
-The current settings for the DBMS are displayed. You can modify them. In most cases the DBMS will need to be restarted.
-
-image::{img}generic-edit_DBMS_settings.png[role="popup-link"]
-
-
-[#desktop-view-logs]
-==== Viewing logs
-
-There are different types of log files associates with a DBMS.
-The main log file can be viewed by selecting the dropdown menu to the right of the Open button and select `Logs`.
-Here is the *neo4j.log* file:
-
-image::{img}generic-view_logs.png[role="popup-link"]
-
-There are other log files that can be used by the Neo4j DBMS. These files are all found in the *logs* directory.
-
-[#desktop-reset-password]
-=== Resetting the DBMS password
-
-You may have forgotten or want to change the password for the DBMS.
-You can reset it by clicking the DBMS, where a pane should open on the right with the Details, Plugins, and Upgrade tabs.
-If you do not see this pane on the right, make sure your Neo4j Desktop window is wider to accommodate this pane.
-
-If you scroll to the bottom of the Details pane, you can reset the password for the DBMS:
-
-image::{img}generic-reset_password.png[role="popup-link",width=400]
-
-[#desktop-feedback]
-== Feedback & Questions
-
-If you have feedback or questions on how to use Neo4j Desktop, feel free to reach out to us.
-You can submit messages to us through the https://community.neo4j.com/c/neo4j-graph-platform/desktop/75[Neo4j Desktop category on the Neo4j Community website^].
-If you have spot any bugs, please create an issue in the https://github.com/neo-technology/neo4j-desktop[Neo4j Desktop Github Repository^]
-
-[#desktop-next-steps]
-== Next Steps
-
-Now that we covered the basics of Neo4j Desktop, you can start working with data using our query language, link:/developer/cypher/[Cypher].
-You can also get a feel for interacting with Neo4j through xref:neo4j-browser.adoc[Neo4j Browser].
-The link:/sandbox/?ref=developer-desktop[Neo4j Sandbox^] walks you through demos of popular use cases in Neo4j and helps you get more familiar with the interfaces and Cypher.
-If you're ready to dive in, feel free to check out how to link:/developer/guide-importing-data-and-etl/[import your data] to Neo4j.
-Our link:/developer/language-guides/[Language Guides] section shows you how to create an application in your preferred programming language to interact with data in Neo4j.
-
-[#desktop-resources]
-== Resources
-* https://github.com/neo4j-apps/neo4j-desktop/wiki/FAQ[FAQ^]
-* http://gist.neo4j.org/[GraphGists: Neo4j Use Case Examples^]
-* https://www.youtube.com/neo4j[Neo4j YouTube Channel^]
diff --git a/modules/ROOT/pages/neo4j-doc-manager.adoc b/modules/ROOT/pages/neo4j-doc-manager.adoc
deleted file mode 100644
index 059aac9d..00000000
--- a/modules/ROOT/pages/neo4j-doc-manager.adoc
+++ /dev/null
@@ -1,13 +0,0 @@
-= Neo4j Doc Manager
-:doctype: book
-:level: Intermediate
-:page-level: Intermediate
-:asciidoctor-source: https://raw.githubusercontent.com/neo4j-contrib/neo4j_doc_manager/master/docs/
-:author: Neo4j
-:category: integrations
-:tags: integrations, mongodb, document, tools, libraries
-
-
-The Neo4j Doc Manger project facilitates real time data synchronization from MongoDB to Neo4j.
-
-include::{asciidoctor-source}/neo4j_doc_manager_doc.adoc[]
diff --git a/modules/ROOT/pages/neo4j-etl.adoc b/modules/ROOT/pages/neo4j-etl.adoc
deleted file mode 100644
index 5832caa7..00000000
--- a/modules/ROOT/pages/neo4j-etl.adoc
+++ /dev/null
@@ -1,271 +0,0 @@
-= How-To: Neo4j ETL Tool
-:level: Beginner
-:page-level: Beginner
-:experimental:
-:neo4j-version: 3.4.6
-:author: Jennifer Reif
-:category: import-export
-:tags: graph-platform, etl, tools, integrations, relational, graph, load, import, connect, data
-:description: Through this guide, you will learn how to take your data from a relational database or table format and import to Neo4j.
-:page-pagination:
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-import/relational-to-graph-import/#relational-etl-tool
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[TIP]
-====
-icon:flask[size=2x]
-The ETL Tool is one of our Neo4j Labs projects.
-To learn more, visit the link:/labs/etl-tool/[Neo4j ETL Tool^] Labs page.
-====
-
-.Goals
-[abstract]
-{description}
-You will understand how the data model is transformed behind-the-scenes and how to connect different types of data formats for loading.
-
-.Prerequisites
-[abstract]
-You should be familiar with Neo4j's link:/developer/graph-database#property-graph[property graph data model] and relational databases.
-It is also helpful to have read the link:/developer/graph-db-vs-rdbms/[Concepts: Relational to Neo4j guide].
-We will focus on how to use this tool through the Neo4j Desktop application, so you should also know how to use the basic functionality of link:/developer/neo4j-desktop/[Neo4j Desktop].
-
-[role=expertise {level}]
-{level}
-
-[#install-neo4j-etl]
-If you haven't already, link:/download/[download^] Neo4j and use the provided instructions (shown when downloading) to get a project and database ready to run.
-
-[TIP]
-====
-This import tool uses a command flag that requires Neo4j 3.4.0 and higher versions.
-If you are running an older version of Neo4j, you can upgrade the database or use the ETL https://github.com/neo4j-contrib/neo4j-etl[command line tool^].
-====
-
-Once you have a project and a database instance created, you will need to go to the `Graph Applications` tab in Neo4j Desktop, copy and paste the `https://r.neo4j.com/neo4j-etl-app` url for the Neo4j ETL tool in the install box, and click the `Install` button.
-
-.Step 1
-image:{img}etl1a_install_graph_app.jpg[role="popup-link"]
-
-.Step 2
-image:{img}etl1b_agree_graph_app_install.jpg[role="popup-link"]
-
-.Step 3: verify ETL tool is listed
-image:{img}etl1c_verify_graph_app.jpg[role="popup-link"]
-
-Now you should have access, and you can load the application 2 different ways.
-
-.Option a
-In the `Graph applications` pane on the left, you can click the icon to the right of the ETL Tool to open the application.
-This will not be specific to any project or database, and a database does not have to be running to load the graph app this way.
-
-.Option b
-If you go back to your `Projects` icon along the left, you can choose the project you want to work within and start the database you want to use.
-The grayed out `Open` button with its dropdown on the left side of the database instance activates once the instance starts.
-Clicking the dropdown next to open shows a list of graph apps you can use.
-Click on the `Neo4j ETL Tool` option to load the app.
-
-.Step 4: open ETL tool
-image:{img}etl2_load_app.jpg[role="popup-link"]
-
-[#etl-steps]
-== ETL Tool Steps and Process
-
-Now that the application is all set up, there are only a few steps to import data.
-
-1. Set up a database connection for a relational database.
-2. Choose the relational database (from the step above) to import from and the Neo4j database to import to.
-3. Verify schema mapping and make adjustments to graph data model.
-4. Import the data with a chosen import method.
-
-[#etl-load-project]
-== Pre-Steps: Loading ETL Tool and Choosing a Project
-
-When you first load the ETL Tool, you might be asked to allow some background processes to run.
-We will choose `Allow` here, which continues loading the application and brings up the main screen.
-
-image::{img}etl3_allow_background_procs.jpg[role="popup-link"]
-
-On this screen, a default project should show and the database instances in that project.
-If you do not have any graph database instances created, you will see an image like the one below.
-You will need to xref:neo4j-desktop.adoc#desktop-create-DBMS[create a graph^] before the next steps with the ETL tool.
-The ETL Tool supports local and remote instances (including link:/aura/[Neo4j AuraDB^]).
-
-image::{img}etl_no_db_instances.jpg[role="popup-link"]
-
-Once you have a database created, you will need to select a project to work within.
-This determines which Neo4j databases will populate as options for you to import the data in a later step.
-The screenshot below shows the steps, with the red highlighting and numbering to show where to click and in what order.
-
-image::{img}etl4_choose_project.jpg[role="popup-link"]
-
-[TIP]
---
-You can choose which Neo4j database you want to use here, but it is not required.
-This can also be chosen in a later step.
---
-
-[#etl-connection-setup]
-== Set Up Connection to Relational Database
-
-After choosing a project, you will need to set up a connection to a relational database.
-You can do this by clicking the `Add Connection` button in the lower left of the pane.
-
-[TIP]
-====
-If you previously set up connections with the ETL tool, these will be remembered and listed.
-You can always create new connections and edit or remove existing connections.
-====
-
-When you click to add a new connection, you will see a form to enter the relational database connection details.
-We recommend first choosing the type of database from the dropdown box in the middle of the page, as that will automatically fill in some of the other fields for you.
-
-image::{img}etl5_connection_setup.jpg[role="popup-link"]
-
-The image below shows the list of relational databases included in the tool.
-If you are using a database that is not specified in the dropdown list, you can still connect by choosing the `JDBC Driver` option and filling in the details.
-
-Both the MySQL and PostgreSQL connections pre-populate many of the configurations for you.
-For any of the other database options, you will also specify the driver file for that database.
-
-image::{img}etl6_connection_options.jpg[role="popup-link"]
-
-Once you have filled in the form, click the `Test and Save Connection` button at the bottom.
-The results for whether it was successful or not will show in a blue (success) or red (error) message bar at the top of your window.
-
-[#etl-db-import]
-== Choose the 'From' and 'To' Databases for Import
-
-To begin the ETL process, we need to tell the application which relational database we want as the source and which Neo4j database we want as the target.
-In the screenshot below, you can see the list of relational databases on the left side of the pane, and the Neo4j database options on the right side of the pane.
-
-image::{img}etl7_db_mapping.jpg[role="popup-link"]
-
-Choose your relational database, then the Neo4j database and click the `Start Mapping` button in the lower righthand side.
-Just as before, a blue message bar will show at the top of your screen if the step was successful or a red one will show if step failed.
-In this case, it was successful, so our `Start Mapping` button inactivates, and the `Next` button activates to proceed to the next step!
-
-image::{img}etl8_mapping_success.jpg[role="popup-link"]
-
-[#etl-mapping-rules]
-== Review data model transformation and make adjustments
-
-This step is where the actual translation of the relational data into graph data happens.
-There are three rules the tool uses to convert from relational to graph.
-
-* A *table with a foreign key* is treated as a *join* and imported as a *node with a relationship*
-image:{img}etl9_mapping_rule1.jpg[role="popup-link"]
-
-* A *table with 2 foreign keys* is treated as a *join table* and imported as a *relationship*
-image:{img}etl9_mapping_rule2.jpg[role="popup-link"]
-
-* A *table with >2 foreign keys* is treated as n *intermediate node* and imported as a *node with multiple relationships*
-image:{img}etl9_mapping_rule3.jpg[role="popup-link"]
-
-Those rules create a graph data model like the one below.
-
-[TIP]
-====
-This example is using the popular Northwind data set.
-You can download and test this data set with the links further on link:/developer/neo4j-etl/#neo4j-etl-options[this page].
-====
-
-image::{img}etl10a_mapping_sample.jpg[role="popup-link"]
-
-This is where you can edit the mapping to change any of the translations, such as property names, data types, and relationships.
-
-You can use the mouse to zoom in on the model or drag the image to focus on certain areas of the graph.
-If you do not see a component you are looking for, you can start typing the entity name in the search bar on the left side of the pane.
-Any matching results will show immediately.
-
-Nodes and relationships are listed in respective tabs on the left side.
-To update, simply click the entity in the list.
-The tool also allows you to skip any nodes or relationships that you do not want to import to the graph.
-In the screenshot below, we have chosen to skip the `UsState` nodes because there are no relationships, so the data is less valuable to us in graph format.
-However, we could still choose to import those nodes and later refactor the graph to create relationships from those nodes to others in the graph.
-
-image::{img}etl10b_skip_node.jpg[role="popup-link"]
-
-To edit the details of mappings, click on the pencil icon next to entity in the list or double click on the entity in the visualization on the right.
-A popup box will list the fields and offer options for any changes.
-You can click `Save` to apply your changes to the graph.
-
-image::{img}etl10c_edit_node.jpg[role="popup-link"]
-
-Oftentimes, the relationship types will be defaulted to meaningless names for a graph, so we can update them with more meaningful names for improved data context.
-The image below shows an example of some changes to relationship types.
-
-.Updated Graph Data Model
-image:{img}etl10d_update_relationships.jpg[role="popup-link"]
-
-Once you have made any changes here, you can click `Save Mapping`.
-The status of the change will show in a blue (success) or red (error) message bar at the top of your window.
-If successful, you can click `Next` to go to the import step.
-
-image::{img}etl10e_save_mapping.jpg[role="popup-link"]
-
-[#etl-import-method]
-== Choose an Import Method and Import the Data
-
-There are a couple of different ways that the ETL Tool can import data to Neo4j, depending on the status of your graph database.
-Each method has certain requirements and advantages, which are listed below.
-
-* a. If database is running (works for both local and remote instances, including Neo4j AuraDB) -
-** 1. Online Direct: runs via BOLT connection for import, turning SQL results into Cypher parameters.
-** 2. Online Batch: uses CSV files from mapping stage to import over BOLT connection in batches.
-* b. If database is shut down -
-** 1. Bulk Import: fast loader for initial load with offline import (running neo4j-admin import tool).
-
-image::{img}etl11_import_modes.jpg[role="popup-link"]
-
-We will show the online vs. offline import methods to see the different options that come with each.
-
-.For online import...
-After you choose an import method from the dropdown box, you can choose if you want to import to a specific database (Neo4j v4.0+ only).
-
-image::{img}etl12a_online_import.jpg[role="popup-link"]
-
-.For offline import...
-Though there is only one import type when the database is shut down, there are a couple more things you can specify.
-You can tell the import to go to a specific database (Neo4j v4.0+ only), whether it exists or not.
-If that database name does not exist, you can choose to manually create the database after the import (with Cypher command `CREATE DATABASE ` on system database) or you can click the checkbox at the bottom to have the ETL Tool create the database for you with the import.
-
-image::{img}etl12b_offline_import.jpg[role="popup-link"]
-
-With either method, once you have filled out the form as much as you need or want, click the `Import Data` button in the lower right corner to start the load.
-If the import is successful, you will see a screen similar to the one below (this example used the Online Direct import method).
-If it fails, you will see a red error message at the top of the screen, and you can troubleshoot the issue with the logs by clicking the `See Logs` button at the bottom.
-
-image::{img}etl13_successful_import.jpg[role="popup-link"]
-
-[TIP]
-====
-This import tool uses a command flag that requires Neo4j 3.4.0 and higher versions.
-If you are running an older version of Neo4j, this step will fail.
-To continue, you can upgrade the database or use the https://github.com/neo4j-contrib/neo4j-etl[command line tool^].
-====
-
-Now, you can query the Neo4j database or use Neo4j Browser to verify the data loaded to properly.
-Your relational data has now been transformed to a graph!
-
-[#neo4j-etl-options]
-== How To Do This Example Yourself and Other ETL Options
-
-If you want to test the ETL Tool, and you do not already have a data set, you can use the Northwind example, as we did here.
-We have included links to download both PostgreSQL and MySQL, if you don't already have a relational database in mind.
-
-* Download db of choice - https://postgresapp.com/[Postgresql^], https://dev.mysql.com/downloads/workbench/[MySQL^], or other option
-* Download JDBC driver (only if _not_ using MySQL or PostgreSQL)
-* Insert data to relational db - https://github.com/pthom/northwind_psql[PostgreSQL Northwind^]
-* Install ETL tool on Neo4j Desktop (or download GitHub https://github.com/neo4j-contrib/neo4j-etl[command line tool^]), then follow import steps from this page.
-
-There are also other options for ETL.
-Feel free to check out some link:/developer/integration/[partner integrations^], the {cyphermanual}/clauses/load-csv/[LOAD CSV^] functionality, and the link:/labs/apoc/[APOC developer library^].
-
-[#etl-resources]
-== Resources
-* https://medium.com/@jennifer.reif/tap-into-hidden-connections-translating-your-relational-data-to-graph-d3a2591d4026[Walkthrough: Blog post^]
-* https://medium.com/neo4j/neo4j-etl-tool-1-3-1-release-white-winter-2fc3c794d6a5[Latest release notes^]
-* link:/developer/guide-importing-data-and-etl/[Tutorial: Importing Relational Data to Neo4j^]
diff --git a/modules/ROOT/pages/neo4j-fabric-sharding.adoc b/modules/ROOT/pages/neo4j-fabric-sharding.adoc
deleted file mode 100644
index 33572f37..00000000
--- a/modules/ROOT/pages/neo4j-fabric-sharding.adoc
+++ /dev/null
@@ -1,256 +0,0 @@
-= Sharding Graph Data with Neo4j Fabric
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:neo4j-version: 4.0
-:category: cluster
-:tags: administration, fabric, sharding, graph-sharding, architecture, examples
-:description: This guide will discuss the concept of sharding graph data using Neo4j Fabric.
-:page-deprecated-title: the Neo4j Operations Manual
-:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/composite-databases/
-
-// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-If you are not familiar with graph database concepts, please read the section on xref:graph-database.adoc[graph databases].
-It also helps to be familiar with the architecture of link:/docs/operations-manual/current/clustering/introduction/[database clustering in Neo4j^].
-
-[role=expertise {level}]
-{level}
-
-[#data-sharding]
-Data sharding is a topic that has arisen from the vast quantities of data stored in databases of all types.
-It may be often-associated with document, key-value, or columnar NoSQL database types, but it has broadened its scope to include relational and graph databases, as well.
-We will discuss what data sharding is and why it is used, as well as what it looks like and how it operates for Neo4j.
-
-[#what-is-sharding]
-== What is data sharding?
-
-https://en.wikipedia.org/wiki/Shard_(database_architecture)[Data sharding^] is dividing data into horizontal partitions that are organized into various instances or servers, most often to spread load across multiple access points.
-These shards can be accessed individually or aggregated to see all of the data, when required.
-
-[#why-sharding]
-== Why use sharding?
-
-A few different use cases make sense to shard into separate databases or instances.
-Many of these have to do with business environments and data size.
-We can look at few listed here.
-
-* For legal or privacy reasons, you need to store some data in isolated databases to protect or limit access (i.e. GDPR laws).
-* Data is already siloed into separate instances for everyday business, but you might need to weave them together into a unified graph (i.e. knowledge graph).
-* To minimize latency of queries in various regions, relevant segments of the graph can be stored in cloud regions close to query request sources (i.e. application hosted in Europe that queries for data stored in a Europe cloud region).
-* Archived data might be separated by date, such as by year, but adhoc reporting or other needs might require queries across these graphs.
-* The graph size is becoming large enough (tens of billions of nodes) that it makes sense to divide the data into smaller graphs to run on smaller-sized hardware and be accessed by necessary parties.
-
-There are other possible scenarios where sharding the data may be the solution of choice, but at the core, sharding provides a way to distribute data across instances or geographic regions for easier or more secure access.
-
-[#sharding-example]
-== Sharding Example
-
-For instance, let us say we have people data stored in our system.
-This could be for customers, social network profiles, or any use case requiring storage of person data.
-We might have millions or billions of people in our system, and we want to divide this up to make querying easier for users.
-
-Here is our data as rows and columns (typical relational database structure).
-However, we could format this data as individual documents, entities, or any other database structure.
-
-image::{img}concept_fabric_people_data.jpg[role="popup-link",width=450]
-
-This is a very small sample of the data in our system, but we can choose to shard it in a few different ways.
-
-1. Separate by continent - this would divide the records into 6 different segments (Africa, Asia, Australia, Europe, North America, South America).
-2. Separate by age bracket - this would divide the records into 5 segments (0-15 years old, 15-30 years old, 30-50 years old, 50-70 years old, 70+ years old).
-3. Other - based on any other data we might collect, such as job role, year of system entry, residency status, relationships to other nodes, etc.
-
-The first two options are shown below.
-
-.By continent
-image:{img}concept_fabric_shard_continent.jpg[role="popup-link"]
-
-.By age bracket
-image:{img}concept_fabric_shard_age.jpg[role="popup-link"]
-
-The concept of sharding in the property graph model takes on a slightly different approach than with other database models, which we will discuss next.
-
-[#graph-sharding]
-== Graph data sharding and how it's different
-
-Sharding implementation is very much related to the type of database – graph, relational, key/value, document, and more.
-Another factor involved is data connectedness - the more data is connected, the more complicated it is to shard.
-Some sort of data redundancy is needed to bridge connections across shards and point to the connected entities.
-As you might imagine, this is a tricky thing to do for a graph database, which places high value on relationships in the data.
-
-[#neo4j-fabric]
-== What is Neo4j Fabric?
-
-Neo4j Fabric is Neo4j's solution to graph sharding by allowing users to break a larger graph down into individual, smaller graphs and store them in separate databases.
-For graphs that are highly-connected, this means some level of data redundancy to maintain the relationships between entities.
-
-As an example, `NodeA` and `NodeB` connected by `RELATIONSHIP_1` could be sharded this way.
-
-.Single graph
-image:{img}concept_fabric_single_graph.jpg[role="popup-link",width=550]
-
-.Sharded graph
-image:{img}concept_fabric_sharded_graph.jpg[role="popup-link",width=550]
-
-In the second diagram, we have decided that we will keep all of the `NodeA` nodes in one shard and then place the relationships from `NodeA` nodes to `NodeB` nodes in another shard.
-The `NodeA` node shown in Shard1 and Shard2 are the exact same entity but duplicated into the second shard to store the relationship to it.
-
-We can take our graph from the first diagram and move the data to the appropriate shard.
-Then, when we run queries, Fabric will find the `NodeA` nodes we are interested in on one shard and pull the relationships from the other shard and bring that data together in results to send back.
-
-[#fabric-structure]
-== Fabric structure
-
-Neo4j sharding contains all of the fabric graphs (instances or databases) that are managed by a coordinating fabric database.
-The fabric database is actually a virtual database that cannot store data, but acts as the entrypoint into the rest of the graphs.
-We can think of this like a proxy server that handles requests and connection information.
-It helps distribute load and sends requests to the appropriate endpoint.
-
-If we take our example from link:/developer/neo4j-fabric-sharding/#what-is-sharding[above with people data], we can visualize this structure.
-We will have 1 fabric graph that will manage all the connections and requests to the rest of the graphs.
-Then, depending on how we shard the data, we can have as many fabric graphs as we choose.
-For this example, we will shard the data by continent and create a separate database for each continent.
-Let us see what our system would look like with this model.
-
-image::{img}concept_fabric_example_system.jpg[role="popup-link",width=550]
-
-Queries coming from users or applications will hit the fabric database first, then get routed to the instance or instances required to answer the query.
-The answers from each involved graph are sent back to the fabric database, where they are aggregated or filtered into a unified result that is sent back to the requesting party.
-An example of this process is shown below.
-
-image::{img}concept_fabric_query_example.jpg[role="popup-link",width=550]
-
-[#fabric-architectures]
-== Architecting a system with Fabric
-
-There are a variety of ways to architect the people data sharded graph system, especially with capabilities for multi-database and clustering.
-Any or all of the graphs could be in the same DBMS on a physical server in a regional location, or graphs could be distributed across different DBMSs in physical and cloud servers around the world.
-
-This can feel rather mind-bending, especially if you are new to the concept of sharding, so we will cover only 3 architectures (out of numerous options) that can be the choices for many scenarios.
-
-=== Example 1: A single DBMS for everything
-
-In this example, we will place all of our data into a single Neo4j DBMS.
-This DBMS could be hosted locally or remotely and on in-house or cloud servers.
-No matter the location of the DBMS, we have sharded our data into 1 instance for the fabric database and 6 separate instances for each of continents containing their people data.
-
-image::{img}concept_fabric_example_1dbms.jpg[role="popup-link",width=550]
-
-Reasons for architecting the system this way is that there is a manageable amount of traffic for a single DBMS to handle, that latency has little to no effect on requests (coming from a narrow region set or not critical), or that there are no regulatory or data privacy issues with storing the domain together.
-
-=== Example 2: Fabric database in separate DBMS
-
-We can take our previous example up a level by placing the fabric database in a separate DBMS.
-Now, either our proxy (fabric db) or the data instances (people data) can be local or remote, in-house or cloud.
-Those choices depend on the requirements and preferences of necessary parties.
-We still have our shards categorized into 1 for the fabric database and 6 separate instances for the people data by continent.
-
-image::{img}concept_fabric_example_2dbms.jpg[role="popup-link",width=550]
-
-The reasons we might choose this architecture is that we need to load-balance the requests.
-To do that, we need to replicate the data across regions.
-Fabric databases must be standalone, single instances, though.
-By putting that instance into its own DBMS, we can then place all of the data instances into another DBMS that can be added to a cluster for replication.
-We could also replicate fabric's single instance to manage more load to the clusters.
-
-=== Example 3: Multiple DBMSs
-
-One step further puts us separating the data stores into shards, as well, and placing certain ones into their own DBMSs.
-Any combination of local or remote, in-house or cloud can be used for each DBMS to fit business needs and requirements.
-The shards are still organized into 1 for the coordinator (fabric db) and 6 instances for the people data based on continent, but the change is that all of these instances are now bundled or separated into different systems.
-
-image::{img}concept_fabric_example_3dbms.jpg[role="popup-link",width=550]
-
-This architecture might be chosen because certain data might be required to be hosted privately or separately from other data or because a bulk of requests are for a particular dataset.
-It could also be for reduced latency in requests to and from certain regions.
-Other requirements could also be solved with this particular setup.
-
-In our particular example, we have done a combination of all of these.
-We have separated the fabric database to solely handle all of the load and processing of requests.
-Next, the Europe graph has been placed in its own DBMS for data privacy reasons to meet GDPR compliance for European user data.
-To the right of that, our North America and South America graphs have been placed in another DBMS to be hosted regionally, and our last DBMS for Africa, Australia, and Asia has been combined for region and load.
-Just as in our Example 2, we could replicate any or all of these DBMSs - fabric database with a second DBMS copy and any of the data DBMS into clusters for replication and load.
-
-=== Other possibilities
-
-Above, we only covered 3 example architectures and their common use cases.
-When we start discussing clustering, especially for certain DBMSs and not for others, it can start to feel complex and confusing.
-The same complexity exists around physically hosting in-house or remotely hosting in the cloud, as certain DBMSs might require one or the other.
-These two designs can also be combined where certain ones are hosted in-house, others in the cloud and some are clustered and replicated while others are not.
-As mentioned above, the fabric database can also be replicated (as a single instance) to provide more proxies for handling request load.
-
-[#fabric-considerations]
-== Implementation considerations
-
-As mentioned earlier, the property graph data model required a specific approach to sharding compared to other data structures.
-The property graph model relies on nodes and the physical connections between them in order to create a graph.
-To implement sharding meant handling and managing that connected unit as many graph pieces and still retaining the value of the relationships across instances and clusters, along with maintaining data consistency and integrity.
-
-There are a couple of specific things to consider for those looking to implement Neo4j Fabric.
-Other considerations are listed in the link:/docs/operations-manual/4.4/fabric/considerations/[documentation^].
-
-=== Where to divide the data
-
-The business or individual will need to make the decisions on data separation and manually refactor the data into shards for Fabric to manage.
-Determining the best places to divide the graph data into separate graphs for Fabric can be trickier than you might imagine, especially if the data is tightly connected.
-The best approach is to look for clean breaks in the data where there are few or no relationships crossing graphs.
-We can think of these as natural subgraphs or disconnected structures in our data where there is the cleanest division.
-
-In our people data example, there are natural, clean divisions in the data by continent.
-We can split our data into people who all share the same continent, which means that there should be few to no people who exist in other continents.
-
-The only exception to this is if our continent is based on citizenship/residence, as it's possible (though unlikely) that many people will have citizenship in multiple continents.
-If so, we may need to have minimal duplication for this where the person's data exists in each of the continents they are citizens.
-If our continent data is based on birth, however, it eliminates this, as a person cannot be born in more than one continent.
-
-Understanding the context and data definitions, then, could also be important to making the best decision on where to divide the data.
-Another perspective to this is how the data is modeled, as this can impact whether clear subgraphs in the data naturally occur based on the model.
-We will look at this consideration next.
-
-=== Data model/schema
-
-The data model plays an important role in how Fabric is implemented and architected and how the data is divided.
-In a Fabric system, the data model can have even more impact on queries and performance, since we could be dealing with a variety of factors for latency, distance, breadth of query (how many shards does it touch), volume of requests to the Fabric database, and more.
-
-Dividing the data as cleanly as possible is key to determine the number of databases involved and how much data will reside on each.
-Once that step is complete, we can begin organizing the instances into a combined or separate DBMS and determine replication needs.
-
-Our people data example has a data model that fits our needs for dividing and querying the data.
-Whether we need to divide the data by continent or age bracket, we can do so and not end up with too many subgraphs (5 for age brackets and 6 for continents), and separating instances for privacy or replication also works well.
-As an alternative, separating by first letter of name could mean we end up with 26 (or more in non-English alphabets) potential databases or separating by country could mean around 200 potential databases (imagine if we needed to replicate these!).
-However, if working with billions or more entities, then 26 or 200 databases might be more valuable and efficient for many queries that only require a subset of the data.
-
-****
-[NOTE]
-While 200 databases is probably a little much, you could bundle the lower-population or smaller countries into groups and make that number more manageable.
-****
-
-Knowing ahead of time the types of queries that will be necessary of the systems or the kinds of requests that are most common will also help plan a data model that best suits the performance requirements of those queries and optimize for them.
-Our next paragraphs will take a closer look at this.
-
-=== Query structure and optimization
-
-Planning ahead by understanding the query requirements will help in constructing a data model and architecture that best optimizes for those expected requests.
-We cannot always plan every request that may be asked of the system, but drafting up those example queries we know can make a big difference in building a system that is designed to handle what is needed.
-
-For our people data example, we have architected a system that can fit all of our query and performance needs.
-Using Example 3 with multiple DBMSs, perhaps we know that users will request information for both North America and South America in queries, so it makes sense to place both of those instances close together or in the same DBMS.
-Likewise, if queries for Australia will be very few, we can place that instance together with other instances to spread the load more evenly across DBMSs.
-Our Europe graph may get the bulk of the requests, so placing it in its own DBMS could help balance the load and avoid irrelevant traffic to that DBMS.
-Requirements may be completely different in your system architecture and may need a different structure.
-
-The combination of all of these factors discussed will help improve stability and resiliency in handling business and data needs.
-
-[#fabric-resources]
-== Resources
-
-* Documentation: link:/docs/operations-manual/4.4/fabric/[Neo4j Fabric^]
-* Developer guide: link:/developer/multi-tenancy-worked-example[Multi-tenancy Example with Multi-database and Fabric^]
-* Blog post: https://adamcowley.co.uk/neo4j/sharding-neo4j-4.0/[Adam Cowley on Neo4j Fabric^]
diff --git a/modules/ROOT/pages/neo4j-google-cloud-launcher.adoc b/modules/ROOT/pages/neo4j-google-cloud-launcher.adoc
deleted file mode 100644
index 0522113e..00000000
--- a/modules/ROOT/pages/neo4j-google-cloud-launcher.adoc
+++ /dev/null
@@ -1,122 +0,0 @@
-= Deploy a Neo4j Causal Cluster on GCP Marketplace
-:level: Intermediate
-:page-level: Intermediate
-:author: David Allen
-:category: cloud
-:tags: cloud, gcp, setup, gcp-cluster, virtual-machine, deployment
-:description: This guide explains how to deploy a Neo4j Enterprise Causal Cluster via GCP Marketplace.
-:page-type: Tutorial
-:page-deprecated-title: the Neo4j Operations Manual
-:page-deprecated-redirect: https://neo4j.com/docs/operations-manual/current/cloud-deployments/neo4j-gcp/cluster-vm/
-
-// This page has been deprecated in favour of the Neo4j Operations Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-* You have a Neo4j Enterprise license
-* You should have familiarity with link:/docs/operations-manual/current/clustering/causal-clustering/introduction/[causal cluster architecture^].
-* Knowledge of link:/developer/language-guides[remote drivers] to access Neo4j from your application helps you connect to your cloud-hosted database.
-
-[#gcloud-launch]
-Let's get started!
-
-[#marketplace-gcp]
-== Select Neo4j in GCP Marketplace
-
-Select Neo4j from the Google Cloud Launcher console and click `Launch` on Compute Engine.
-
-image::{img}launch-screen.png[width=700,float=center]
-
-[#enterprise-deploy]
-== Deploy Neo4j Enterprise
-
-* Choose a name for your Neo4j instance
-* Choose a machine type
-* Select a number of core nodes in the cluster
-* Click `Deploy`
-
-image::{img}deploy-screen.png[width=700,float=center]
-
-* Once the deploy has finished, save the URL, username, and password for the next steps.
-
-image::{img}deployed-screen.png[width=700,float=center]
-
-[#start-neo4j]
-== Start using Neo4j Browser
-
-We are now ready to start using Neo4j!
-
-Use your browser to access the URL provided in the previous step and log in with the initial
-username and password provided.
-You may see an SSL warning screen because the deployment uses an unsigned SSL certificate out of the box.
-
-The initial password is set to a strong, random password and is saved as a metadata entry on the VMs themselves, so you cannot lose it.
-
-To verify that the cluster has formed correctly, run the cypher statement `CALL dbms.cluster.overview()`.
-
-image::{img}working-cluster.png[width=700,float=center]
-
-You will know that everything is working properly when you see one `LEADER` node with the remainder of
-your nodes as `FOLLOWER`.
-The IP addresses and endpoints will match what Compute Engine shows you for your running instances.
-
-image::{img}vm-instances.png[width=700,float=center]
-
-[#ssh-instance]
-== How do I SSH into the instance?
-
-On the deployment manager screen above, there is a button provided to SSH directly into the first node of the cluster.
-Cluster members are regular Google Compute Engine VMs.
-As a result, you can always access any of them via SSH.
-Check your Compute Engine VMs.
-They should be named `cluster-name-vm-1`, `cluster-name-vm-2`, and so on.
-
-Using the Google Cloud CLI, you can access them via the following command:
-
-[source,shell]
-----
-gcloud compute ssh my-cluster-deploy-vm-1
-----
-
-[#vm-workings]
-== How the Virtual Machines Work
-
-Please consult {opsmanual}/cloud-deployments/cloudVms/[Neo4j Cloud VMs^] for details on internals of virtual machines, including configure Neo4j inside of the VM and access various files.
-
-[#default-config]
-== Your Cluster Default Configuration
-
-The following notes are provided on your default cluster configuration.
-
-* Ports 7687 (bolt) and 7473 (HTTPS access) are the only ports exposed to the entire internet.
-Consider narrowing access on these ports to only your needed networks.
-External unencrypted HTTP access is disabled by default.
-* Ports 5000, 6000, and 7000 are enabled only for internal network access (`10.0.0.8`), as they are needed for internal cluster communication.
-* Because cloud VMs can start and stop with different IP addresses, the configuration of these VMs is driven by a file in `/etc/neo4j/neo4j.template`.
-Configuration changes should be made to the template, **not** to the `/etc/neo4j/neo4j.conf` file, which is overwritten with template substitutions at every startup.
-The template allows you configure aspects of the cluster with VM metadata (see the "Custom Metadata" on any of your launched VMs for examples).
-The template's behavior and layout matches the usual `neo4j.conf` file.
-
-[#next-steps]
-== What's Next
-
-* Visit the {opsmanual}/clustering/[Neo4j Operations Manual^] for information on how to configure all aspects of your cluster
-* Add users and change passwords as necessary
-* Consider creating DNS entries with Google to permit addressing your cluster with client applications under a single host name
-
-[#terminate-deploy]
-== Terminating the Deployment
-
-Should you need to, you can tear down this infrastructure by using the deployment manager to delete
-the deployment.
-To ensure data safety, the disks that back the VMs will not be autodeleted if the cluster deployment is deleted.
-These disks must be deleted separately, and manually, if desired.
-
-[#gcp-resources]
-== Questions?
-
-You can ask questions and connect with other people launching Neo4j in the cloud through the https://community.neo4j.com/c/neo4j-graph-platform/cloud[cloud topic on the Community Site].
\ No newline at end of file
diff --git a/modules/ROOT/pages/neo4j-ogm.adoc b/modules/ROOT/pages/neo4j-ogm.adoc
deleted file mode 100644
index d195520d..00000000
--- a/modules/ROOT/pages/neo4j-ogm.adoc
+++ /dev/null
@@ -1,107 +0,0 @@
-= Neo4j - OGM Object Graph Mapper
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: java
-:category: drivers
-:tags: ogm, object-graph-map, app-development, applications
-:description: For Java developers that require a mechanism to manage their domain objects with Neo4j, this guide introduces the Neo4j Object Graph Mapping (OGM) library.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/java/neo4j-ogm/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-include::./_includes/common-language-prereq.adoc[]
-
-[role=expertise {level}]
-{level}
-
-[#neo4j-ogm]
-== Neo4j-OGM
-
-include::https://raw.githubusercontent.com/neo4j/neo4j-ogm/master/neo4j-ogm-docs/src/main/asciidoc/introduction/index.adoc[lines=25..32,leveloffset=+1]
-
-[#ogm-features]
-== Features
-
-The Neo4j-OGM supports the features you would expect:
-
-* Object graph mapping of annotated node- and relationship-entities
-* Fast class metadata scanning
-* Optimized management of data loading and change tracking for minimal data transfers
-* Persistence lifecycle events
-* Query result projection to DTOs
-
-[#ogm-code-example]
-== Minimal Code Snippet
-
-This code example is taken from the Example Project (see below).
-
-[source,java]
-----
-@NodeEntity
-public class Movie {
-
-   @Id @GeneratedValue
-   Long id;
-
-   @Property(name="title")
-   private String name;
-}
-
-@NodeEntity
-public class Actor {
-
-   @Id @GeneratedValue
-   private Long id;
-
-   @Property(name="name")
-   private String fullName;
-
-   @Relationship(type="ACTED_IN", direction=Relationship.OUTGOING)
-   private List filmography;
-
-}
-
-@RelationshipEntity(type="ACTED_IN")
-public class Role {
-    @Id @GeneratedValue
-    private Long relationshipId;
-
-	@Property
-    private String title;
-
-	@StartNode
-    private Actor actor;
-
-	@EndNode
-    private Movie movie;
-}
-----
-
-include::./_includes/sample_project.adoc[]
-
-[#ogm-faq]
-== FAQ
-
-include::https://raw.githubusercontent.com/neo4j/neo4j-ogm/master/neo4j-ogm-docs/src/main/asciidoc/appendix/faq.adoc[lines=3..-1,leveloffset=+1]
-
-[#ogm-resources]
-== Resources
-
-:maven-ogm: http://search.maven.org/#search|ga|1|a%3A%22neo4j-ogm%22
-
-[cols="1,4"]
-|===
-| icon:user[] Authors | The Neo4j and http://graphaware.com/neo4j-experts/[GraphAware Teams]
-| icon:gift[] Package | link:{maven-ogm}[http://maven.org]
-| icon:github[] Source | https://github.com/neo4j/neo4j-ogm
-| icon:book[] Docs | https://neo4j.com/docs/ogm-manual/current/
-|===
-
-:ogm-version: {neo4j-ogm-version}
-include::https://raw.githubusercontent.com/neo4j/neo4j-ogm/master/neo4j-ogm-docs/src/main/asciidoc/reference/getting-started.adoc[lines=3..-1,leveloffset=+1]
diff --git a/modules/ROOT/pages/online-meetup.adoc b/modules/ROOT/pages/online-meetup.adoc
deleted file mode 100644
index 1168235f..00000000
--- a/modules/ROOT/pages/online-meetup.adoc
+++ /dev/null
@@ -1,96 +0,0 @@
-= Twitch, YouTube and Meetup Live Streams
-:slug: online-meetup
-:section: Documentation and Resources
-:category: documentation
-:tags: resources, online-meetup, community, developer, events, training, twitch
-:page-player: https://player.twitch.tv/?channel=neo4j&muted=false&autoplay=true&parent=twitter.com&parent=cards-dev.twitter.com&parent=cards-frame.twitter.com
-
-// image::https://blog.twitch.tv/assets/uploads/03-glitch.jpg[width="300px",float="right"]
-++++
-
-
    
-
-++++
-
-We run a variety of live streams and online meetups so that people around the world can get fresh Neo4j content. Our regular streamers cover a variety of topics at different times during the week. We also have online meetups, often with special guests!
-
-You can keep an eye on what's happening by:
-
-* https://twitch.tv/neo4j[Follow us on Twitch^] or https://www.youtube.com/neo4j?sub_confirmation=1[subscribe to our YouTube channel^] and you get notified when we go live
-* Keep an eye out for event announcements on our https://www.meetup.com/Neo4j-Online-Meetup/[Online Meetup^] group
-* Join our https://www.discord.gg/neo4j/[Neo4j Community Discord Server^] where we talk about the latest events 
-
-[#calendar]
-== Neo4j Live Stream Calendar 
-
-See what is planed for the next days:
-++++
-
-++++
-
-[#regular-streamers]
-== Our regular streamers 
-
-=== Monday -  Discover Neo4j AuraDB Free with Michael & Alexander
-
-image::https://raw.githubusercontent.com/neo4j-documentation/developer-guides/publish/modules/ROOT/images/discoveraura.png[Discover AuraDB Free,width="300px",float="right"]
-
-Alternating weekly between +
-*8am UTC (12am PST, 3am EST, 9am BST, 10am CEST, 1:30pm IST, 4pm SGT)* & +
-*2pm UTC (6am PST, 9am EST, 3pm BST, 4pm CEST, 7:30pm IST, 10pm SGT)*
-
-https://twitter.com/mesirii[Michael Hunger^] and https://twitter.com/alexandererdl[Alexander Erdl^] take requests of online data sets, figure out some questions, define a data model, load the data and query, all against https://dev.neo4j.com/discover-aura[Neo4j AuraDB Free tier]. Got a data set you want them to explore? Let them know! 
-
-Missed a stream? Check out the playlist:
-
-* https://www.youtube.com/playlist?list=PL9Hl4pk2FsvVZaoIpfsfpdzEXxyUJlAYw[Discover Neo4j AuraDB Free^]
-* https://github.com/neo4j-examples/discoveraurafree[Read up the code examples on the repo^]
-
-=== Tuesday - Going Meta 
-
-image::https://github.com/neo4j-documentation/developer-guides/raw/publish/modules/ROOT/images/meta.png[Going Meta,width="300px",float="right"]
-
-Every first Tuesday of the month +
-*4pm UTC (8am PST, 11am EST, 3pm BST, 4pm CEST, 9:30pm IST, 12am SGT)*
-
-https://twitter.com/BarrasaDV[Jesús Barrasa^] and https://twitter.com/alexandererdl[Alexander Erdl^] run a series on graphs, semantics and knowledge graphs. 
-
-Missed an episode? Check out the playlist:
-
-* https://www.youtube.com/watch?v=NQqWBnyQlS4&list=PL9Hl4pk2FsvX-5QPvwChB-ni_mFF97rCE[Going Meta^]
-* https://github.com/jbarrasa/goingmeta[Read up the code examples on the repo^]
-
-
-=== Bi-Monthly - Neo4j Live 
-
-image::https://raw.githubusercontent.com/neo4j-documentation/developer-guides/publish/modules/ROOT/images/live.png[Neo4j Live,width="300px",float="right"]
-
-Aiming to take place at least twice a month and is showcasing exciting graph projects from the Neo4j Community! 
-
-https://twitter.com/alexandererdl[Alexander Erdl^] is happy to host you, if you have an interesting project around Neo4j and Graphs to show. Ping him via Twitter or during the show if you are interested!  
-
-Missed a stream? Check out the playlist:
-
-* https://www.youtube.com/watch?v=386eMuIktek&list=PL9Hl4pk2FsvW1NtrhILyptfFnLMjg5Vmc[Neo4j Live^]
-
-
-[#stream-catchup]
-== Stream catch-up and previous meetup sessions
-
-Missed a session? Not to worry! All of our live stream and online meetup sessions are available on YouTube:
-
-* https://www.youtube.com/playlist?list=PL9Hl4pk2FsvVnz4oi0F8UXiD3nMNqsRO2[Neo4j Online Meetup playlist^]
-* https://www.youtube.com/watch?v=8jqQM3LPyyk&list=PL9Hl4pk2FsvXjk0hrerr78pLN-477pDLo[Twitch stream playlist^]
-
-[#join-us]
-== Join us!
-
-Working on an exciting graphy problem? Building out a community driver and you want to share the word? Thinking about a theory you want to discuss? We'd love to hear about it and share your initiatives with the community. Drop a message on our https://www.meetup.com/Neo4j-Online-Meetup/[Online Meetup group^], and/or tweet https://twitter.com/alexandererdl[Alexander^], and let's make it happen!
diff --git a/modules/ROOT/pages/perl.adoc b/modules/ROOT/pages/perl.adoc
deleted file mode 100644
index e31f9384..00000000
--- a/modules/ROOT/pages/perl.adoc
+++ /dev/null
@@ -1,73 +0,0 @@
-= Perl: Neo4j Community Drivers
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: perl
-:category: drivers
-:tags: perl, project, app-development, applications
-:description: This guide provides an overview of options for connecting to Neo4j using the Perl programming language.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/community-drivers/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-While this guide is not comprehensive, it will introduce some prominent drivers and link to the relevant resources.
-
-include::./_includes/common-language-prereq.adoc[]
-
-[role=expertise {level}]
-{level}
-
-include::./_includes/community-driver-notice.adoc[]
-
-[#neo4j-perl]
-== Neo4j for Perl Developers
-
-image::{img}perl.png[float=right]
-
-Members of the Perl community have invested a lot of time and love to develop drivers for Neo4j, so if you use them, please provide feedback to the authors.
-
-include::./_includes/sample_project.adoc[]
-
-[#neo4p-rest]
-== REST::Neo4p
-
-This Perl driver from Mark Jensen works with Neo4j’s REST API by using Perl5 objects in a consistent, idiomatic Perl-style.  There is also a related DBI-compliant wrapper (https://metacpan.org/pod/DBD::Neo4p[DBD::Neo4p])
-
-[cols="1,4"]
-|===
-| icon:user[] Author | https://www.linkedin.com/in/fortinbras[Mark A. Jensen]
-| icon:play-circle[] Example Project | {examples}/movies-perl-neo4p
-| icon:gift[] Package | http://metacpan.org/release/REST-Neo4p
-| icon:github[] Source | https://github.com/majensen/rest-neo4p
-| icon:book[] Docs | http://slideshare.net/majensen1/neo4p-dcbpw2015-46990541
-| icon:question-circle[] Community Site | https://community.neo4j.com/c/drivers-stacks/perl
-|===
-
-[#neo4j-driver]
-== Neo4j::Driver
-
-This is a Perl driver by Arne Johannessen. It enables interacting with a Neo4j server using the same classes and method calls as the official Neo4j drivers.  It also has (currently experimental) support for HTTPS and Bolt.
-
-[cols="1,4"]
-|===
-| icon:user[] Author | Arne Johannessen
-| icon:gift[] Package | https://metacpan.org/release/Neo4j-Driver
-| icon:github[] Source | https://github.com/johannessen/neo4j-driver-perl
-|===
-
-
-[#neo4j-bolt]
-== Neo4j::Bolt
-
-This is another driver from Mark Jensen. It's implemented as a Perl wrapper around the libneo4j-client C library, which implements the Bolt network protocol.
-
-[cols="1,4"]
-|===
-| icon:user[] Author | https://www.linkedin.com/in/fortinbras[Mark A. Jensen]
-| icon:github[] Source | https://github.com/majensen/perlbolt
-|===
-
diff --git a/modules/ROOT/pages/php.adoc b/modules/ROOT/pages/php.adoc
deleted file mode 100644
index 136ca717..00000000
--- a/modules/ROOT/pages/php.adoc
+++ /dev/null
@@ -1,131 +0,0 @@
-= Using Neo4j from PHP
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: php
-:category: drivers
-:tags: php, ogm, app-development, applications, driver, client, bolt
-:description: If you are a PHP developer, this guide provides an overview of options for connecting to Neo4j.
-:page-ad-overline-link: https://neo4j.com/cloud/aura/?ref=developer-guides
-:page-ad-overline: Neo4j Aura
-:page-ad-title: The Fastest Way to get Started with PHP & Graphs
-:page-ad-description: Free forever, no credit card required.
-:page-ad-link: https://neo4j.com/cloud/aura/?ref=developer-guides
-:page-ad-underline-role: button
-:page-ad-underline: Start on AuraDB
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/community-drivers/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-While this guide is not comprehensive it will introduce the different drivers and link to the relevant resources.
-
-include::./_includes/common-language-prereq.adoc[]
-When developing with Neo4j, please use Java 8 or 11 and your favorite IDE.
-
-[role=expertise {level}]
-{level}
-
-[#neo4j-php]
-== Neo4j for PHP Developers
-
-image::{img}php_logo.png[float=right,width=300]
-
-
-Alternatively, Neo4j can be installed on any system and then accessed via its bolt and HTTP APIs. We recommend the https://github.com/laudis-technologies/neo4j-php-client#roadmap[PHP Client^] for easiest development over bolt and HTTP APIs. You can also directly access the bolt protocol via the https://github.com/stefanak-michal/Bolt[PHP Bolt] library.
-
-
-include::./_includes/community-driver-notice.adoc[]
-
-[#Client]
-== Full client
-
-*Neo4j-php-client* is a client supporting multiple protocols. *Http* and *bolt* are supported, starting from neo4j 3.5 up until the most recent version.
-
-It boasts many features such as multiple connections, transactions functions, authentication and auto-routing. It also integrates with the psalm static analysis tool for complete type safety.
-
-It is being actively developed and has a big readme file on the https://github.com/laudis-technologies/neo4j-php-client[Github page^].
-
-[cols="1,4"]
-|===
-| icon:user[] Author | https://www.linkedin.com/in/ghlen-nagels-1b6663134/[Ghlen Nagels^]
-| icon:github[] Source | https://github.com/neo4j-php/neo4j-php-client
-| icon:gift[] Package | https://packagist.org/packages/laudis/neo4j-php-client
-| icon:code-fork[] Php    | 7.4 / 8.0+
-| icon:code-fork[] Neo4j    | 3.5 / 4.0+
-| Protocols | Bolt, HTTP
-| Example App | https://github.com/neo4j-examples/movies-neo4j-php-client
-|===
-
-.Installation
-[source,bash]
-----
-composer require laudis/neo4j-php-client
-----
-
-.Example
-[source,php]
-----
-$client = Laudis\Neo4j\ClientBuilder::create()->withDriver('default', 'bolt://neo4j:password@localhost')->build();
-
-$result = $client->run(<<<'CYPHER'
-MERGE (neo4j:Database {name: $dbName}) - [:HasRating] - (rating:Rating {value: 10})
-RETURN neo4j, rating
-CYPHER, ['dbName' => 'neo4j'])->first();
-
-$neo4j = $result->get('neo4j');
-$rating = $result->get('rating');
-
-// Outputs "neo4j is 10 out of 10"
-echo $neo4j->getProperty('name').' is '.$rating->getProperty('value') . ' out of 10!';
-----
-
-
-[#bolt]
-== Bolt
-
-A low level driver for the Bolt protocol in PHP.
-
-[cols="1,4"]
-|===
-| icon:user[] Author | https://www.linkedin.com/in/michalstefanak/[Michal Stefanak^]
-| icon:github[] Source | https://github.com/neo4j-php/Bolt
-| icon:code-fork[] Php    | 7.4+ / 8.0+
-| icon:code-fork[] Neo4j    | 3.0+ / 4.0+ / 5.0+
-| Protocols | Bolt
-|===
-
-
-.Installation
-[source,bash]
-----
-composer require stefanak-michal/bolt
-----
-
-.Example
-[source,php]
-----
-$bolt = new \Bolt\Bolt(new \Bolt\connection\Socket());
-$protocol = $bolt->setProtocolVersions(5.1)->build();
-$protocol->hello(\Bolt\helpers\Auth::basic('neo4j', 'neo4j'));
-
-$protocol
-  ->run(
-    'MERGE (neo4j:Database {name: $dbName})-[:HasRating]-(rating:Rating {value: 10}) RETURN neo4j, rating', 
-    ['dbName' => 'neo4j']
-  )
-  ->pull();
-
-foreach ($protocol->getResponses() as $response) {
-  if ($response->getSignature() == \Bolt\protocol\Response::SIGNATURE_RECORD) {
-    $neo4j = $response->getContent()[0];
-    $rating = $response->getContent()[1];
-    
-    // Outputs "neo4j is 10 out of 10"
-    echo $neo4j->properties()['name'] . ' is ' . $rating->properties()['value'] . ' out of 10!';
-  }
-}
-----
diff --git a/modules/ROOT/pages/procedures-gallery.adoc b/modules/ROOT/pages/procedures-gallery.adoc
deleted file mode 100644
index 24bbd1ac..00000000
--- a/modules/ROOT/pages/procedures-gallery.adoc
+++ /dev/null
@@ -1,396 +0,0 @@
-= Procedures & Functions Gallery
-:level: Intermediate
-:page-level: Intermediate
-:apoc: https://neo4j-contrib.github.io/neo4j-apoc-procedures/
-:author: Neo4j
-:category: cypher
-:tags: cypher, queries, apoc, spatial, rdf, custom-development
-:description: Our community created already a lot of useful user defined procedures and functions. This overview highlights which areas each library covers with some examples. This saves you from implementing existing ones yourself.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should know what user defined procedures and functions are and how to use them within Cypher.
-
-[role=expertise {level}]
-{level}
-
-[#apoc-library]
-== Awesome Procedures on Cypher (APOC)
-
-With the advent of user defined procedures, many capabilities could be added to Cypher with ease.
-That's how the APOC library got started, which developed into a "standard-library" for Neo4j built by the Neo4j community.
-
-Due to the use of internal APIs there are https://github.com/neo4j-contrib/neo4j-apoc-procedures#version-compatibility-matrix[dedicated APOC versions] for the different Neo4j versions.
-
-By now it contains 200+ procedures and 70+ functions.
-
-APOC covers much ground, here are some examples for the available functionality.
-
-=== Procedures
-
-[options="header",cols="3a,a,3m,a",subs=attributes]
-|===
-| package | # of procedures | popular procedure | docs link
-
-| data api access
-| 11
-| apoc.load.json(json-url)
-| icon:book[link={apoc}#_load_json]
-
-| database integration
-| 23
-| apoc.load.jdbc(jdbc-url,table-or-statement)
-| icon:book[link={apoc}#_load_jdbc]
-
-| graph algorithms
-| 18
-| apoc.algo.dijkstra(from,to,cost-prop) +
-apoc.algo.pageRankStats({iterations:10,write:true})
-| icon:book[link={apoc}#graph_algorithms]
-
-
-| (virtual) data creation
-| 16
-| CALL apoc.create.relationship(startNode,'TYPE',{key:value,…​}, endNode)
-| icon:book[link={apoc}#_creating_data]
-
-| transaction control
-| 8
-| apoc.periodic.iterate('source-query','work-statement',{batchSize:100, parallel:true})
-| icon:book[link={apoc}#_job_management_and_periodic_execution]
-
-| cypher operations
-| 9
-| apoc.cypher.runFile(file or url)
-| icon:book[link={apoc}#_cypher_operations]
-
-| graph data export
-| 9
-| apoc.export.cypher.query(query,file,config)
-| icon:book[link={apoc}#_export_import]
-
-| graph generation
-| 5
-| apoc.generate.ws(noNodes, degree, beta, 'label', 'TYPE') // Watts-Strogatz
-| icon:book[link={apoc}#_generating_graphs]
-
-| meta information
-| 4
-| apoc.meta.stats()
-| icon:book[link={apoc}#_meta_graph]
-
-| time to live, node-expiry
-| 1
-| CALL apoc.date.expire(node,100,'s');
-| icon:book[link={apoc}#_timetolive_ttl_expire_nodes]
-
-| monitoring & operations
-| 6
-| apoc.monitor.tx()
-| icon:book[link={apoc}#_warmup]
-
-| manual and schema indexes
-| 17
-| apoc.index.between(node1,'TYPE',node2,'prop:value*')
-| icon:book[link={apoc}#_monitoring]
-
-
-| cypher triggers on transaction end
-| 3
-| apoc.trigger.add(name, statement, selector)
-| icon:book[link={apoc}#_triggers]
-
-| search and expand
-| 5
-| apoc.path.expand(startNode, relationshipFilter, labelFilter, minDepth, maxDepth )
-| icon:book[link={apoc}#_path_expander]
-
-|===
-
-image::apoc.load.json-so-result.png[width=700]
-
-=== Functions:
-
-As mentioned before, functions can compute values in any kind of expression or predicate.
-
-[options="header",cols="2a,a,2m"]
-|===
-| package | # of functions | popular function
-| date & time conversion
-| 3
-| apoc.date.parse("time",["unit"],["format"])
-
-| number conversion
-| 3
-| apoc.number.parse("number",["format"])
-
-| general type conversion
-| 8
-| apoc.convert.toMap(value)
-
-| type information and checking
-| 4
-| apoc.meta.type(value)
-
-| collection and map functions
-| 25
-| apoc.map.fromList(["k1",v1,"k2",v2,"k3",v3])
-
-| JSON conversion
-| 4
-| apoc.convert.toJson(value)
-
-| string functions
-| 7
-| apoc.text.join(["s1","s2","s3"],"delim")
-
-| hash functions
-| 2
-| apoc.util.md5(value)
-
-|===
-
-Learn more about APOC by reading the https://neo4j.com/?s=apoc%20blog[blog post series on APOC] or watch the https://www.youtube.com/channel/UCvze3hU6OZBkB1vkhH2lH9Q/search?query=apoc[presentations about it].
-
-APOC itself comes also with a comprehensive and growing https://neo4j-contrib.github.io/neo4j-apoc-procedures/[documentation site].
-
-Recently added Neo4j Browser guides make parts of that documentation available within your working environment.
-
-Just run: `:play http://guides.neo4j.com/apoc` within your Neo4j browser to learn interactively.
-
-[#apoc-spatial]
-== Neo4j Spatial Procedures
-
-The http://github.com/neo4j-contrib/spatial[neo4j-spatial] library has been with Neo4j for a long time.
-
-In the past you used it with its Java or REST APIs.
-Now for Neo4j 3.0 the maintainer, Craig Taverner added procedures for the APIs to integrate the spatial capabilities closely with Cypher.
-
-=== Spatial Procedure Overview
-// TODO include from spatial docs
-
-Neo4j Spatial is packaged as a ZIP file that can be unzipped into the `$NEO4J_HOME/plugins` directory.
-After restarting the server, you should be able to use the following *procedure calls* from Cypher.
-
-Below are some of the most commonly used procedures grouped by type. You can find usage examples below in the legis-graph section.
-
-[options="header",cols="a,4m"]
-|===
-| type | procedures (spatial.*)
-| general  | procedures()
-| layers | layer(layer),layers(), layerTypes()
-| add layers | addLayer(layer, type, config), addLayerWithEncoder(), addPointLayer(layer), addWKTLayer(name, property)
-| remove layers | removeLayer(layer)
-| add nodes | addNode(layer,node), addNodes(layer,nodes), updateFromWKT(layer,geometry,geoNodeId)
-| add geometries | addWKT(layer, geometry), addWKTs(layer,geometries)
-| find / search | bbox(layer, min, max), closest(layer, coordinate, distance), intersects(layer, geometry), withinDistance(layer, coordinate, distance)
-| utilities | asGeometry, asExternalGeometry, decodeGeometry, getFeatureAttributes
-| import | importShapefile(uri), importShapefileToLayer(layer, uri)
-|===
-
-////
-
-// todo auto-generate
-.Available Spatial Procedures
-[options="headers",cols="2*m"]
-|===
-|name                           |signature
-|spatial.addLayer               |spatial.addLayer(name :: STRING?, type :: STRING?, encoderConfig :: STRING?) :: (node :: NODE?)
-|spatial.addLayerWithEncoder    |spatial.addLayerWithEncoder(name :: STRING?, encoder :: STRING?, encoderConfig :: STRING?) :: (node :: NODE?)
-|spatial.addNode                |spatial.addNode(layerName :: STRING?, node :: NODE?) :: (node :: NODE?)
-|spatial.addNodes               |spatial.addNodes(layerName :: STRING?, nodes :: LIST? OF NODE?) :: (node :: NODE?)
-|spatial.addPointLayer          |spatial.addPointLayer(name :: STRING?) :: (node :: NODE?)
-|spatial.addPointLayerWithConfig|spatial.addPointLayerWithConfig(name :: STRING?, encoderConfig :: STRING?) :: (node :: NODE?)
-|spatial.addPointLayerXY        |spatial.addPointLayerXY(name :: STRING?, xProperty :: STRING?, yProperty :: STRING?) :: (node :: NODE?)
-|spatial.addWKT                 |spatial.addWKT(layerName :: STRING?, geometry :: STRING?) :: (node :: NODE?)
-|spatial.addWKTLayer            |spatial.addWKTLayer(name :: STRING?, nodePropertyName :: STRING?) :: (node :: NODE?)
-|spatial.addWKTs                |spatial.addWKTs(layerName :: STRING?, geometry :: LIST? OF STRING?) :: (node :: NODE?)
-|spatial.asExternalGeometry     |spatial.asExternalGeometry(geometry :: ANY?) :: (geometry :: ANY?)
-|spatial.asGeometry             |spatial.asGeometry(geometry :: ANY?) :: (geometry :: ANY?)
-|spatial.bbox                   |spatial.bbox(layerName :: STRING?, min :: ANY?, max :: ANY?) :: (node :: NODE?)
-|spatial.closest                |spatial.closest(layerName :: STRING?, coordinate :: ANY?, distanceInKm :: FLOAT?) :: (node :: NODE?)
-|spatial.decodeGeometry         |spatial.decodeGeometry(layerName :: STRING?, node :: NODE?) :: (geometry :: ANY?)
-|spatial.getFeatureAttributes   |spatial.getFeatureAttributes(name :: STRING?) :: (name :: STRING?)
-|spatial.importShapefile        |spatial.importShapefile(uri :: STRING?) :: (node :: NODE?)
-|spatial.importShapefileToLayer |spatial.importShapefileToLayer(layerName :: STRING?, uri :: STRING?) :: (node :: NODE?)
-|spatial.intersects             |spatial.intersects(layerName :: STRING?, geometry :: ANY?) :: (node :: NODE?)
-|spatial.layer                  |spatial.layer(name :: STRING?) :: (node :: NODE?)
-|spatial.layerTypes             |spatial.layerTypes() :: (name :: STRING?, signature :: STRING?)
-|spatial.layers                 |spatial.layers() :: (name :: STRING?, signature :: STRING?)
-|spatial.procedures             |spatial.procedures() :: (name :: STRING?, signature :: STRING?)
-|spatial.removeLayer            |spatial.removeLayer(name :: STRING?) :: VOID
-|spatial.setFeatureAttributes   |spatial.setFeatureAttributes(name :: STRING?, attributeNames :: LIST? OF STRING?) :: (node :: NODE?)
-|spatial.updateFromWKT          |spatial.updateFromWKT(layerName :: STRING?, geometry :: STRING?, geometryNodeId :: INTEGER?) :: (node :: NODE?)
-|spatial.withinDistance         |spatial.withinDistance(layerName :: STRING?, coordinate :: ANY?, distanceInKm :: FLOAT?) :: (node :: NODE?, distance :: FLOAT?)
-|===
-
-////
-
-[[legis-graph]]
-=== Using Neo4j Spatial Procedures in legis-graph-spatial
-
-William Lyon demonstrates how to use them in the http://www.lyonwj.com/2016/08/09/neo4j-spatial-procedures-congressional-boundaries/[Legis-Graph-Spatial Blog post] with the https://github.com/legis-graph/legis-graph-spatial[code being available on GitHub]
-
-image::ca_san_mateo.png[width=500]
-
-.create a WKT layer
-[source,cypher]
-----
-call spatial.addWKTLayer('geom', 'wkt')
-----
-
-.match on all District nodes and add them to the WKT layer
-[source,cypher]
-----
-MATCH (d:District)
-WITH collect(d) AS districts
-CALL spatial.addNodes('geom', districts) YIELD node
-RETURN count(*)
-----
-
-.Find Geometry within distance and related
-[source,cypher]
-----
-WITH {latitude: 37.563440, longitude: -122.322265} AS coordinate
-CALL spatial.withinDistance('geom', coordinate, 1) YIELD node AS district
-MATCH (district)<-[:REPRESENTS]-(legislator:Legislator)
-RETURN district.state, legislator.govtrackID, legislator.lastName, l.currentParty AS party
-----
-
-[#apoc-rdf]
-== Semantic Web (RDF / Ontology) Procedures
-
-Neo4j Consultant Jesus Barrasa wrote a number of procedures for importing and managing semantic web data.
-
-
-.import RDF formats and convert them into the property graph model
-[source,cypher]
-----
-CALL semantics.importRDF(rdf-url-or-file,format, shorten-url-flag, batch-commit-size);
-----
-
-.import ontologies into Neo4j ontologies for graph generation and checking
-[source,cypher]
-----
-CALL semantics.LiteOntoImport(own-url-or-file,'RDF/XML')
-----
-
-image::https://jesusbarrasa.files.wordpress.com/2016/06/screen-shot-2016-06-09-at-00-45-38.png?w=700[]
-
-You can find them https://github.com/jbarrasa/neosemantics[here on GitHub], for more detail see his https://jesusbarrasa.wordpress.com/?s=procedure[blog post series].
-
-[#apoc-custom]
-== Developing your own Procedures and Functions
-
-=== Writing your first Function
-
-You can find details on writing and testing procedures in the link:/docs/java-reference/current/extending-neo4j/procedures-and-functions/introduction/[Neo4j Manual^].
-
-There is even an https://github.com/neo4j-examples/neo4j-procedure-template[example GitHub repository] with detailed documentation and comments that you can clone directly and use as a starting point.
-
-User-defined functions are simpler, so let's look at one here:
-
-* `@UserFunction` annotated, named Java Methods
-** default name is `class package + "." +  method-name`
-* take `@Name`'ed parameters (with optional default values)
-* return a single value
-* are read only
-* can use `@Context` injected `GraphDatabaseService` etc
-* run within Transaction of the Cypher Statement
-
-.simple user defined uuid function
-[source,java]
-----
-@UserFunction("create.uuid")
-@Description("creates an UUID (universally unique id)")
-public String uuid() {
-   return UUID.randomUUID().toString();
-}
-----
-
-////
-cp $GROOVY_HOME/lib/groovy-2.*.jar $NEO4J_HOME/plugins/
-$GROOVY_HOME/groovyc function.groovy && jar cf $NEO4J_HOME/plugins/uuid.jar UDF.class
-
-
-@Grab(value="org.neo4j:neo4j:3.1.0-BETA1",initClass=false)
-
-
-class UDF {
-   @UserFunction("create.uuid")
-   @Description("creates an UUID")
-   def String uuid() { UUID.randomUUID().toString() }
-}
-////
-
-
-.use the function like this
-[source,cypher]
-----
-CREATE (p:Person {id: create.uuid(), name: $name})
-----
-
-=== Testing the Function
-
-The Neo4j testing library `neo4j-harness` enables you to spin up a Neo4j server, provide fixtures for data setup and register your functions and procedures.
-
-You then call and test test the function via the bolt - `neo4j-java-driver`.
-
-[source,java]
-----
-@Rule
-public Neo4jRule neo4j = new Neo4jRule()
-                         .withFunction( UUIDs.class );
-...
-
-try( Driver driver = GraphDatabase.driver( neo4j.boltURI() , config ) {
-    Session session = driver.session();
-    String uuid = session.run("RETURN create.uuid() AS uuid")
-                         .single().get( 0 ).asString();
-    assertThat( uuid,....);
-}
-----
-
-=== Writing a Procedure
-
-User defined procedures are similar:
-
-* `@Procedure` annotated, Java methods
-* with an additional `mode` attribute (`Read, Write, Dbms`)
-* return a `Stream` of value objects (DTO) with `public` fields
-* value object fields are turned into result columns to be `YIELD`ed
-
-.Expose dijkstra algoritm from the Java API to Cypher
-[source,java]
-----
-@Procedure(mode = Write)
-@Description("apoc.algo.dijkstra(startNode, endNode, 'KNOWS', 'distance') YIELD path," +
-       " weight - run dijkstra with relationship property name as cost function")
-public Stream dijkstra(
-       @Name("startNode") Node startNode,
-       @Name("endNode") Node endNode,
-       @Name("type") String type,
-       @Name("costProperty") String costProperty) {
-
-
-   PathFinder algo = GraphAlgoFactory.dijkstra(
-           PathExpanders.forType(RelationshipType.withName(type)),
-           costProperty);
-   Iterable allPaths = algo.findAllPaths(startNode, endNode);
-   return Iterables.asCollection(allPaths).stream()
-           .map(WeightedPathResult::new);
-}
-
-public static class WeightedPathResult {
-   public final Path path;
-   public final double weight;
-   public WeightedPathResult(WeightedPath wp) { this.path = wp; this.weight = wp.weight(); }
-}
-----
-
-Use a build tool (like maven, gradle, ant) to package your code into a jar-file and copy that into `$NEO4J_HOME/plugins`
-Make sure required dependencies are added as well, either to your jar or the plugins directory.
diff --git a/modules/ROOT/pages/python-movie-app.adoc b/modules/ROOT/pages/python-movie-app.adoc
deleted file mode 100644
index 79c85e11..00000000
--- a/modules/ROOT/pages/python-movie-app.adoc
+++ /dev/null
@@ -1,815 +0,0 @@
-= Getting Started with Neo4j and Flask
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:category: documentation
-:tags: flask, python, react
-:description: This course provides an overview on everything that you need to build a Neo4j application with the link:https://www.python.org/[Python programming language^].
-The Flask framework is used as the backend, and React as front-end.
-:redirect: https://graphacademy.neo4j.com/courses/app-python/
-
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should have Python 3 installed on your system.
-You should have cloned https://github.com/neo4j-examples/neo4j-movies-template[Python Movie Demo repository] onto your machine.
-
-
-[#python-movie-course]
-This course provides an overview of what you need to know to write a Flask application using a Neo4j database.
-To keep concepts simple, the application is an IMDB clone with basic account authentication and movie recommendation functionality.
-
-++++
-
-++++
-
-[#neo4j-python]
-== Before We Start - Why Neo4j?
-
-Neo4j is the world's most popular _graph database_.
-Graph databases offer a number of advantages:
-
-* Neo4j provides a schema-optional representation of both entities and relationships between entities.
-* Relationships between entities are *traversed* rather than *joined*. Traversals explore the local subgraph, meaning that query times stay the same even as your database grows.
-* Because of the traversal paradigm, we can think in terms of the complex relationships in our data, without worrying as much about how to model it.
-
-Neo4j makes it easy to create nodes and relationships in an intuitive way, but you can also always change the structure of your database with a query.
-
-[#intro-neo4j]
-== Introduction to Neo4j
-
-Connected information is everywhere in our world.
-Neo4j was built to efficiently store, handle, and query highly-connected elements in your data model.
-With a powerful and flexible data model, you can represent your real-world, variably-structured information without a loss of fidelity.
-The property graph model is easy to understand and work with, especially for object-oriented and relational developers.
-
-The property graph model consists of:
-
-**Nodes**, which have:
-
-* _properties_: schemaless key/value pairs
-* _labels_: describe and group nodes much like tables group rows, except nodes can also have multiple labels
-
-**Relationships**, which connect two nodes directionally and have:
-
-* _properties_: schemaless key/value pairs
-* A _type_: describes how it connects the two nodes
-
-While relationships are directional, querying relationships in either direction has no associated performance cost.
-
-image::{img}model.png[][Python Movie App Model]
-
-=== Cypher
-
-Cypher is Neo4j's built-in query language.
-Cypher queries look like the following code block:
-
-[source, cypher,role=nocopy,noplay]
-----
-MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
-RETURN p, m.title
-----
-
-The `MATCH` clause is the most common starting point for Cypher queries.
-It defines a pattern to search the database for, and returns one result for each match.
-With the `RETURN` clause, we would end up returning a table such as:
-
-.Result of Cypher query
-[width="99%", options="header"]
-|=============================
-| p                | m.title
-| {name: "Denise"} | "Toy Story"
-| {name: "Denise"} | "Animal House"
-|=============================
-
-As you can see here, we can return entire entities from our database rather than just their properties.
-
-This is very handy, but it would also be nice to avoid the duplication of our `Person` node.
-So, you can perform the same match but instead use the `collect` function to aggregate the values:
-
-[source, cypher,role=nocopy,noplay]
-----
-MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
-RETURN p, collect(m.title)
-----
-
-.Result of Cypher aggregation query
-[width="99%", options="header"]
-|=============================
-| p                | m.title
-| {name: "Denise"} | ["Toy Story", "Animal House"]
-|=============================
-
-While it's possible to get started using Neo4j without learning Cypher, it is a very powerful tool to query a Neo4j database and is worth learning.
-Also, since this project works by making Cypher queries to Neo4j, it is helpful to understand Cypher as your queries get more complex.
-If you would like to learn more Cypher and Using Python with Neo4j:
-
-
-*Beginner Learning Path:*
-
-* https://graphacademy.neo4j.com/courses/neo4j-fundamentals/[Neo4j Fundamentals^]
-* https://graphacademy.neo4j.com/courses/cypher-fundamentals/[Cypher Fundamentals^]
-* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^]
-* https://graphacademy.neo4j.com/courses/importing-data/[Importing CSV Data into Neo4j^]
-
-*Cypher Learning Path:*
-
-* https://graphacademy.neo4j.com/courses/cypher-intermediate-queries/[Cypher Intermediate Queries^]
-
-*Developer Learning Path:*
-
-* https://graphacademy.neo4j.com/courses/app-python/[Building Neo4j Applications with Python^]
-
-
-
-[#project-setup]
-== The Neo4j Movie Flask/React App
-
-Let's jump right into it.
-You're a Python developer interested in Neo4j and want to build a web app, microservice, or mobile app.
-You've already read up on Neo4j, played around with some datasets, and learned enough Cypher to get started.
-Now you're looking for a demo app or template to start putting those skills into practice.
-
-If you haven't already, clone the https://github.com/neo4j-examples/neo4j-movies-template[Python Move Demo repository] onto your machine.
-This tutorial post will walk you through rating a movie on a sample movie rating application, from the initial setup to viewing the list of movies you've rated.
-The following video will walk briefly walk you through the various features of the movie app.
-
-++++
-
-++++
-
-=== The Database
-
-This project uses a classic Neo4j dataset: the movie database. It includes `Movie`, `Actor`, `Director`, and `Genre` nodes, connected by relationships as described below:
-
-[source, cypher, role=nocopy,noplay]
-----
-(:Movie)-[:HAS_GENRE]→(:Genre)
-(:Actor)-[:ACTED_IN]→(:Movie)
-(:Director)-[:DIRECTED]→(:Movie)
-----
-
-Additionally, users can create accounts, log in, and add their ratings to movies:
-
-[source, cypher, role=nocopy,noplay]
-----
-(:User)-[:RATES]->(:Movie)
-----
-
-=== The API
-
-The Flask portion of the application interfaces with the database and presents data to the React.js front-end via a RESTful API.
-You can find the flask API in the `/flask-api` directory in the repo.
-
-=== The Front-End
-
-The front-end, built in React.js, consumes the data provided by the Flask API and presents it through some views to the end user, including:
-
-* Home page
-* Movie detail page
-* Actor and Director detail page
-* User detail page
-* Sign-up and Login pages
-
-You can find the front-end code in the `web` directory.
-
-=== Setup
-
-To get the project running, clone the https://github.com/neo4j-examples/neo4j-movies-template[repository] and follow along with these instructions, which are recapped in the video:
-
-++++
-
-++++
-
-First, Start the Database!
-
-Your app will need a database, and the easiest way to access a database that's already full of data is by connecting directly to the "Recommendations" database in Neo4j Sandbox.
-
-Log in to Neo4j Sandbox by visiting https://sandbox.neo4j.com/, either using social authentication or your email and password.
-
-After logging in to Neo4j Sandbox, tap "New Project" and select "Recommendations," then tap the blue "Launch Project" button to start the database you will be connecting to.
-
-In order to connect to the database from the environment from which you'll be running the app (presumably your local machine), you'll need credentials. You can find those under the "Connection details" and/or the "Connect via drivers" tab:
-
-Note the section that looks like this - you'll need to copy+paste the credentials in the driver section to connect to the database from your local machine.
-For example, if the driver line contains the following:
-
-[source,python, role=nocopy]
-----
-driver = GraphDatabase.driver("bolt://52.72.13.205:47929", auth=basic_auth("neo4j", "knock-cape-reserve"))
-----
-
-Then, in your text editor, open and/or create `flask-api/.env` and enter the appropriate information into the variables: `DATABASE_USERNAME`, `DATABASE_PASSWORD`, and `DATABASE_URL`. Then save the file.
-
-----
-DATABASE_USERNAME = 'your usernamer'
-DATABASE_PASSWORD = 'your password'
-DATABASE_URL = 'your URL'
-----
-
-To start the Flask API, run:
-
-[source,shell]
-----
-cd flask-api
-pip3 install -r requirements.txt
-export FLASK_APP=app.py
-flask run
-----
-
-Verify that the endpoints are running as expected by taking a look at the docs at: http://localhost:5000/docs
-
-=== Start the React.js Front-End
-
-With the database and backend running, open a new terminal tab or window and move to the project's `/web` subdirectory.
-Run `nvm use` to ensure you're using the node version specified for this project.
-If you don't have the recommended version of node installed, follow the prompt to install the recommended version.
-After verifying you are using the recommended user, run:
-
-[source,shell]
-----
-npm install
-cp src/config/settings.example.js src/config/settings.js
-npm start
-----
-
-Navigate to view the app at http://localhost:3000/
-
-Click on a movie poster to see its corresponding movie detail page.
-
-Click on a cast or crew member to see that person's profile, which includes biographical information, related people, and more movies the person has acted in, directed, written, or produced:
-
-== Going Through The Endpoints
-
-++++
-
-++++
-
-Let's look at how we would request a list of all the established genres from the database.
-The GenreList class queries the database for all `Genre` nodes, serializes the results, and then returns them via `/api/v0/genres`:
-
-[source,python, role=nocopy]
-----
-class GenreList(Resource):
-    @swagger.doc({
-        'tags': ['genres'],
-        'summary': 'Find all genres',
-        'description': 'Returns all genres',
-        'responses': {
-            '200': {
-                'description': 'A list of genres',
-                'schema': GenreModel,
-            }
-        }
-    })
-    def get(self):
-        def get_genres(tx):
-            return list(tx.run('MATCH (genre:Genre) SET genre.id=id(genre) RETURN genre'))
-        db = get_db()
-        result = db.read_transaction(get_genres)
-        return [serialize_genre(record['genre']) for record in result]
-----
-
-[source,python,role=nocopy]
-----
-def serialize_genre(genre):
-    print(genre)
-    return {
-        'id': genre['id'],
-        'name': genre['name'],
-    }
-
-----
-
-[source,python,role=nocopy]
-----
-api.add_resource(GenreList, '/api/v0/genres')
-----
-
-=== What's Going on with the Serializer?
-
-If you've only used a non-Bolt Neo4j driver before, these bolt-driver responses may be different from what you're used to.
-In the "GET all Genres" example described above,
-`result = db.read_transaction(get_genres)`
-returns a series of records:
-
-----
-[>, >, >, >, >, >, >, >, >, >, >, >, >, >, >, >, >, >, >, (m)
-            SET r.rating = {rating}
-            RETURN m
-            ''', {'user_id': g.user['id'], 'movie_id': id, 'rating': rating}
-        )
-        return {}
-
-    @login_required
-    def delete(self, id):
-        db = get_db()
-        db.run(
-            '''
-            MATCH (u:User {id: {user_id}})
-                          -[r:RATED]->(m:Movie {id: {movie_id}}) DELETE r
-            ''', {'movie_id': id, 'user_id': g.user['id']}
-        )
-        return {}, 204
-----
-
-*Example: See All of My Ratings*
-
-.Request
-[source,shell]
-----
-curl -X GET --header 'Accept: application/json' --header 'Authorization: Token ce40f63e79344f017a48b205db27aeaa301ae2b6' 'http://localhost:5000/api/v0/movies/rated'
-----
-
-.Response
-----
-[
-  {
-    "duration": 101,
-    "id": "15602",
-    "my_rating": 4,
-    "poster_image": "https://image.tmdb.org/t/p/w440_and_h660_face/1FSXpj5e8l4KH6nVFO5SPUeraOt.jpg",
-    "rated": 6.6,
-    "released": "1995-12-22",
-    "summary": "John and Max resolve to save their beloved bait shop from turning into an Italian restaurant, just as its new female owner catches Max's attention.",
-    "tagline": "John and Max resolve to save their beloved bait shop from turning into an Italian restaurant, just as its new female owner catches Max's attention.",
-    "title": "Grumpier Old Men"
-  }
-]
-----
-
-.Python Implementation: See My Ratings
-[source,python,role=nocopy]
-----
-class MovieListRatedByMe(Resource):
-    @login_required
-    def get(self):
-        db = get_db()
-        result = db.run(
-            '''
-            MATCH (:User {id: {user_id}})-[rated:RATED]->(movie:Movie)
-            RETURN DISTINCT movie, rated.rating as my_rating
-            ''', {'user_id': g.user['id']}
-        )
-        return [serialize_movie(record['movie'],
-        record['my_rating']) for record in result]
-
-...
-
-def serialize_movie(movie, my_rating=None):
-    return {
-        'id': movie['tmdbId'],
-        'title': movie['title'],
-        'summary': movie['plot'],
-        'released': movie['released'],
-        'duration': movie['runtime'],
-        'rated': movie['imdbRating'],
-        'tagline': movie['plot'],
-        'poster_image': movie['poster'],
-        'my_rating': my_rating,
-    }
-----
-
-*Example: My Recommendations*
-
-.Request
-[source,shell]
-----
-curl -X GET --header 'Accept: application/json' --header 'Authorization: Token ce40f63e79344f017a48b205db27aeaa301ae2b6' 'http://localhost:5000/api/v0/movies/recommended'
-----
-
-.Response
-----
-[
-  {
-    "duration": 82,
-    "id": "45523",
-    "my_rating": null,
-    "poster_image": "https://image.tmdb.org/t/p/w440_and_h660_face/8mJMrrT4tkfZLMFvKQ0Hq6jlXbp.jpg",
-    "rated": 8.6,
-    "released": "2010-01-26",
-    "summary": "In this unique and dynamic live concert experience, Louis C.K.'s exploration of life after 40 destroys politically correct images of modern life with thoughts we have all had...but would rarely admit to.",
-    "tagline": "In this unique and dynamic live concert experience, Louis C.K.'s exploration of life after 40 destroys politically correct images of modern life with thoughts we have all had...but would rarely admit to.",
-    "title": "Louis C.K.: Hilarious"
-  },
-  {
-    "duration": 100,
-    "id": "38757",
-    "my_rating": null,
-    "poster_image": "https://image.tmdb.org/t/p/w440_and_h660_face/1uPxRO0iYwW02lzwatRhkugWBYs.jpg",
-    "rated": 7.8,
-    "released": "2010-11-24",
-    "summary": "The magically long-haired Rapunzel has spent her entire life in a tower, but now that a runaway thief has stumbled upon her, she is about to discover the world for the first time, and who she really is.",
-    "tagline": "The magically long-haired Rapunzel has spent her entire life in a tower, but now that a runaway thief has stumbled upon her, she is about to discover the world for the first time, and who she really is.",
-    "title": "Tangled"
-  },
-...
-]
-----
-
-[source,python,role=nocopy]
-----
-@login_required
-def get(self):
-    def get_movies_list_recommended(tx, user_id):
-        return list(tx.run(
-            '''
-            MATCH (me:User {id: $user_id})-[my:RATED]->(m:Movie)
-            MATCH (other:User)-[their:RATED]->(m)
-            WHERE me <> other
-            AND abs(my.rating - their.rating) < 2
-            WITH other,m
-            MATCH (other)-[otherRating:RATED]->(movie:Movie)
-            WHERE movie <> m
-            WITH avg(otherRating.rating) AS avgRating, movie
-            RETURN movie
-            ORDER BY avgRating desc
-            LIMIT 25
-            ''', {'user_id': user_id}
-        ))
-    db = get_db()
-    result = db.read_transaction(get_movies_list_recommended, g.user['id'])
-    return [serialize_movie(record['movie']) for record in result]
-----
-
-== The React Front-end
-
-++++
-
-++++
-
-You can take a look at the React front-end code at in the `/web/` subdirectory.
-The React front-end is very simple, and is composed of the following parts, as described in the video above:
-
-* Home Page
-* Authentication Page
-* Movie Detail Page
-* Person (Actor, Director) Page
-* User Profile Page
-
-=== Home Page
-
-The home page is a relatively simple page making calls to two endpoints: the "GET movies by genre endpoint" and the "GET movie by ID" endpoint.
-
-The "Featured Movies" portion at the top calls three hard-coded movies.
-
-[source,javascript,role=nocopy]
-----
-renderFeatured() {
-    var {movies} = this.props;
-
-    return (
-      
    
-        
    Featured Movies
    
-        
      
-          { _.compact(movies.featured).map(f => {
-            return (
-              
    • 
-                
-                  
-                
-              
      • 
-            );
-          })}
-        
    
-      
    
-    );
-  }
-----
-
-[source,javascript,role=nocopy]
-----
-static getFeaturedMovies() {
-    return Promise.all([
-        axios.get(`${apiBaseURL}/movies/13380`),
-        axios.get(`${apiBaseURL}/movies/15292`),
-        axios.get(`${apiBaseURL}/movies/11398`)
-    ]);
-}
-----
-
-=== Movie and Person Detail
-
-The Movie and Person detail are visually very similar pages - both with a poster image on the left and carousels on the bottom.
-However, the Movie page is different depending on whether the user is authenticated, as the authenticated user is able to mark their rating on each movie.
-
-=== User Profile
-
-The User Profile page allows the user to re-rate or un-rate their movies, and view more movie recommendations based on those ratings.
-
-== Deployment
-
-include::./_includes/using_aura.adoc[]
-
-=== Deploying the Backend and Front-end with Heroku
-
-You will have to create two apps on Heroku: one for the backend and one for the front-end.
-
-Starting with the backend, create a new app on Heroku.
-On your local machine, add the Heroku repo as a remote.
-On **Heroku > Settings > Config Vars**, add the credentials to connect to the database hosted Neo4j AuraDB (or the sandbox if you haven't migrated to AuraDB).
-
-
-Then, create another Heroku app for the front-end.
-Add another git remote pointed to the Heroku app dedicated to the front-end app.
-Under **Heroku > Settings > Config Vars**, add the environment variables for the `REACT_APP_API_BASE_URL` and `REACT_APP_PROXY_URL` fields.
-
-Under **Heroku > Settings > Buildpacks**, add `mars/create-react-app` to load dependencies.
-
-Check out the `Makefile` in the root directory of the project.
-It contains the commands needed to deploy the project.
-You can run `deploy-api` to deploy the Rlask API and `deploy-web` to run deployment on the React site.
-
-.Makefile
-----
-deploy-api:
-    git branch -f heroku-api
-    git branch -D heroku-api
-    git subtree split --prefix flask-api -b heroku-api
-    git push heroku-api heroku-api:master --force
-
-deploy-web:
-    git branch -f heroku-web
-    git branch -D heroku-web
-    git subtree split --prefix web -b heroku-web
-    git push heroku-web heroku-web:master --force
-----
-
-
-++++
-
-++++
-
-== Next Steps
-
-Fork the repo and hack away! Find directors that work with multiple genres, or find people who tend to work with each other frequently as writer-director pairs.
-Did you find a way to improve the https://github.com/neo4j-examples/neo4j-movies-template/[application] or the https://github.com/neo4j/neo4j-python-driver[Python driver]? Create a GitHub Issue and/or submit a pull request.
diff --git a/modules/ROOT/pages/python.adoc b/modules/ROOT/pages/python.adoc
deleted file mode 100644
index 44bc1aad..00000000
--- a/modules/ROOT/pages/python.adoc
+++ /dev/null
@@ -1,152 +0,0 @@
-= Using Neo4j from Python
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: python
-:category: drivers
-:tags: python, official-driver, app-development, applications
-:description: This guide provides an overview of how to connecting to Neo4j from Python.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/neo4j-python/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-include::./_includes/common-language-prereq.adoc[]
-
-[role=expertise {level}]
-{level}
-
-[#neo4j-python]
-== Neo4j and Python
-
-image::{img}python-logo.png[width=300,float="right"]
-
-Neo4j provides drivers which allow you to make a connection to the database and develop applications
-which create, read, update, and delete information from the graph.
-
-[.discrete.ad]
-== link:https://graphacademy.neo4j.com/?ref=guides[Learn with GraphAcademy^]
-
-image::https://graphacademy.neo4j.com/courses/app-python/badge/[float=left]
-
-[.discrete]
-=== link:https://graphacademy.neo4j.com/courses/app-python/?ref=guides[Building Neo4j Applications with Python^]
-
-In this free course, we walk through the steps to integrate Neo4j into your Python projects.
-You will learn about the Neo4j Python Driver, how sessions and transactions work and how to query Neo4j from an existing application.
-
-link:https://graphacademy.neo4j.com/courses/app-python/?ref=guides[Learn more^,role=button]
-
-[#python-driver]
-== Neo4j Python Driver
-
-include::./_includes/python-driver.adoc[]
-
-include::./_includes/sample_project.adoc[]
-
-include::./_includes/community-driver-notice.adoc[]
-
-For anyone working with https://www.python.org/[Python^], the Neo4j community have contributed a range of driver options.
-These range from lightweight to comprehensive driver packages as well as libraries designed for use with web frameworks such as https://www.djangoproject.com/[Django^].
-
-While Python 3 is preferred, some drivers still support Python 2, please check with the individual project if you need it.
-
-While we do not provide a specific web framework recommendation, both the lightweight http://flask.pocoo.org/[Flask^] and the more comprehensive https://www.djangoproject.com/[Django^] frameworks are known to work well.
-
-////
-[#py2neo-lib]
-=== Py2neo
-
-image::{img}py2neo.200x200.png[float="right",width=100]
-
-Py2neo is a client library and comprehensive toolkit for working with Neo4j from within Python applications and from the command line.
-It has been carefully designed to be easy and intuitive to use.
-
-[cols="1,4"]
-|===
-| icon:user[] Author         | https://twitter.com/technige[Nigel Small^]
-| icon:gift[] Package        | https://pypi.python.org/pypi/py2neo
-| icon:github[] Source       | https://github.com/technige/py2neo
-
-| icon:play-circle[] Example | {examples}/movies-python-py2neo
-| icon:book[] Docs           | http://py2neo.org/
-| icon:code-fork[] Python    | 2.7 / 3.4+
-| Protocols | Bolt, Http
-|===
-
-.Installation
-[source,bash]
-----
-pip install py2neo
-----
-
-.Example
-[source,python]
-----
-from py2neo import Graph
-graph = Graph()
-
-tx = graph.begin()
-for name in ["Alice", "Bob", "Carol"]:
-    tx.append("CREATE (person:Person name: $name) RETURN person", name=name)
-alice, bob, carol = [result.one for result in tx.commit()]
-----
-////
-
-[#neomodel-lib]
-=== Neomodel
-
-image::{img}neomodel.200x80.png[float="right",width=100]
-
-An Object Graph Mapper built on top of the Neo4j python driver.
-Familiar Django style node definitions with a powerful query API, thread safe and full transaction support.
-A Django plugin https://github.com/neo4j-contrib/django-neomodel[django_neomodel^] is also available.
-
-[cols="1,4"]
-|===
-| icon:user[] Author         | Athanasios Anastasiou and Robin Edwards
-| icon:gift[] Package        | https://pypi.python.org/pypi/neomodel
-| icon:github[] Source       | http://github.com/neo4j-contrib/neomodel
-
-| icon:book[] Docs           | https://neomodel.readthedocs.io/en/latest/
-| icon:code-fork[] Python    | 2.7 / 3.3+
-| Protocols | Bolt
-| Example App | https://github.com/neo4j-examples/neo4j-movies-python-neomodel
-|===
-
-.Installation
-[source,bash]
-----
-pip install neomodel
-----
-
-.Example
-[source,python]
-----
-from neomodel import StructuredNode, StringProperty, RelationshipTo, RelationshipFrom, config
-
-config.DATABASE_URL = 'bolt://neo4j:test@localhost:7687'
-
-class Book(StructuredNode):
-    title = StringProperty(unique_index=True)
-    author = RelationshipTo('Author', 'AUTHOR')
-
-class Author(StructuredNode):
-    name = StringProperty(unique_index=True)
-    books = RelationshipFrom('Book', 'AUTHOR')
-
-harry_potter = Book(title='Harry potter and the..').save()
-rowling =  Author(name='J. K. Rowling').save()
-harry_potter.author.connect(rowling)
-----
-
-== Resources
-
-* link:/docs/python-manual/current/[Docs: Driver Manual^]
-* https://medium.com/neo4j/neo4j-python-4-0-driver-the-latest-driver-for-the-next-gen-database-a5be6ecd481f[Blog post: Python 4.0 driver^]
-* https://medium.com/neo4j/neo4j-for-django-developers-efd0e39e5f2e[Blog Post: Neo4j for Django Developers: From zero to deployment with Django-Neomodel and Neo4j’s Paradise Papers dataset]
-* https://medium.com/neo4j/running-django-neomodel-within-a-docker-container-f0d68c1d38c7[Blog Post: Running Django-Neomodel within a Docker Container]
diff --git a/modules/ROOT/pages/r.adoc b/modules/ROOT/pages/r.adoc
deleted file mode 100644
index 802e5cfb..00000000
--- a/modules/ROOT/pages/r.adoc
+++ /dev/null
@@ -1,110 +0,0 @@
-= Neo4j from R
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: r
-:category: drivers
-:tags: r, app-development, applications
-:description: If you are an R developer or data scientist, this guide provides an overview of options for connecting from R to Neo4j and even using Neo4j from within R-Studio.
-
-.Goals
-[abstract]
-{description}
-
-include::./_includes/common-language-prereq.adoc[]
-
-[role=expertise {level}]
-{level}
-
-include::./_includes/community-driver-notice.adoc[]
-
-[#neo4j-r]
-== Neo4j for R Developers and Data Scientists
-
-image::{img}Rlogo.jpg[float=right]
-
-=== Neo4R
-
-The goal of \{neo4r\} is to provide a modern and flexible Neo4J driver for R.
-
-It’s modern in the sense that the results are returned as tibbles whenever possible, it relies on modern tools, and it is designed to work with pipes.
-Our goal is to provide a driver that can be easily integrated in a data analysis workflow, especially by providing an API working smoothly with other data analysis (`\{dplyr\}` or `\{purrr\}`) and graph packages (`\{igraph}`, `\{ggraph\}`, `\{visNetwork\}`…).
-
-It was developed by https://thinkr.fr/[ThinkR], led by Colin Fay.
-
-Currently it connects to Neo4j via Http but we plan to integrate it with the C-connector for Bolt, Seabolt.
-
-[cols="1,4"]
-|===
-| icon:user[] Author         | https://twitter.com/_ColinFay[Colin Fay]
-| icon:gift[] Package        |
-| icon:github[] Source       | https://github.com/neo4j-rstats/neo4r
-| icon:github[] Neo4j-Rstats | https://github.com/neo4j-rstats/
-| icon:comments[] Neo4j Online Community | https://community.neo4j.com/c/drivers-stacks/r
-
-// | icon:play-circle[] Example | {examples}/movies-rstats-neo4r
-| icon:book[] Docs           | https://neo4j-rstats.github.io/user-guide/
-| Protocols | Http
-|===
-
-[#neo4jshell]
-== neo4jshell
-
-image::{img}neo4jshell.png[float=right]
-
-=== neo4jshell
-
-The goal of neo4jshell is to provide rapid querying of Neo4j graph databases by offering a programmatic interface with 'cypher-shell'.
-
-It is developed by Keith McNulty.
-
-[cols="1,4"]
-|===
-| icon:user[] Author         | https://twitter.com/dr_keithmcnulty[Keith McNulty]
-| icon:gift[] Package        | https://CRAN.R-project.org/package=neo4jshell
-| icon:github[] Source       | https://github.com/keithmcnulty/neo4jshell
-
-| Protocols | Bolt (via 'cypher-shell')
-|===
-
-////
-=== RNeo4j
-
-RNeo4j was written by our own data scientists https://twitter.com/_nicolemargaret[Nicole White^], to combine the analytics abilities of R with the interconnected data in Neo4j.
-The RNeo4j package not only offers convenient access to Neo4j but also idiomatic integration with R the language and the R-Studio environment.
-
-Nicole also detailed the usage in a number of blog posts and applications.
-
-[cols="3*"]
-|===
-| Name
-| icon:tag[] Version
-| icon:user[] Authors
-
-| RNeo4j
-| {rneo4j-driver-version}
-| https://twitter.com/_nicolemargaret[Nicole White^]
-
-| http://www.rdocumentation.org/packages/RNeo4j[icon:gift[] Package]
-|
-| https://community.neo4j.com/c/drivers-stacks/r[Neo4j Online Community^]
-
-| http://nicolewhite.github.io/RNeo4j/[icon:book[] Docs]
-| http://github.com/nicolewhite/Rneo4j[icon:github[] Source]
-|===
-
-==== Presentations posts on RNeo4j
-
-* Graphs R Cool (http://watch.neo4j.org/video/105896138[video^], https://github.com/nicolewhite/graphs_r_cool[GitHub^])
-* Graph Visualization with R and Neo4j (https://youtu.be/5u4eT1OgB88[video^], http://nicolewhite.github.io/neo4j-presentations/RNeo4j/Visualizations/Visualizations.html[slides^])
-
-==== Blog posts on RNeo4j
-
-* http://nicolewhite.github.io/[Blog: Nicole White^]
-** http://nicolewhite.github.io/2014/12/17/whats-new-rneo4j.html[What's New in RNeo4j?^]
-** http://nicolewhite.github.io/2014/05/30/demo-of-rneo4j-part1.html[Demo of RNeo4j Part 1 - Building a Database^]
-** http://nicolewhite.github.io/2014/05/30/demo-of-rneo4j-part2.html[Demo of RNeo4j Part 2 - Plotting and Analysis^]
-** http://nicolewhite.github.io/2014/06/30/create-shiny-app-neo4j-graphene.html[Create a Shiny App Powered by a Neo4j Database^]
-** http://nicolewhite.github.io/2014/07/19/meetup-cluster-analysis.html[A Cluster Analysis of London NoSQL Meetup Groups^]
-* http://www.markhneedham.com/blog/?s=rneo4j[Blog: Mark Needham on RNeo4j^]
-////
diff --git a/modules/ROOT/pages/relational-to-graph-import.adoc b/modules/ROOT/pages/relational-to-graph-import.adoc
deleted file mode 100644
index 34e75619..00000000
--- a/modules/ROOT/pages/relational-to-graph-import.adoc
+++ /dev/null
@@ -1,160 +0,0 @@
-= Import: RDBMS to Graph
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: import-export
-:tags: data-import, graph-import, relational-graph, load-csv, apoc, etl-tool, kettle, driver-import
-:description: This guide shows the different ways you can import data from a relational database to Neo4j. Completing this guide will give you the tools to choose how to import your relational data and transform it to the graph.
-:page-pagination:
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-import/relational-to-graph-import/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with xref:graph-database.adoc[graph database] concepts and the xref:graph-database.adoc#property-graph[property graph model].
-It is also helpful to know the differences between xref:graph-db-vs-rdbms.adoc[relational and graph concepts] and their xref:relational-to-graph-modeling.adoc[data models].
-
-[role=expertise {level}]
-{level}
-
-[#import-relational]
-== Importing Data from a Relational Database
-
-Often, when in a company setting, you have existing data in a system that will need transferred or manipulated for a new project.
-It is rare to have cases where some or all of the data for a new project is not already captured somewhere.
-In order to get existing data where you need it for the new process, application, or system, you will need to perform an extract-transform-load (ETL) process.
-Very simply, you will need to export data from the existing system(s), handle any necessary manipulations on the data for the new structure, and then import the transformed data to the new data store.
-
-Depending on the particular environment you are working in, different tools for importing relational to graph may provide better or faster solutions than others.
-In this guide, we want to discuss all of the options and why you can or should choose some over others for your use case.
-
-[#relational-import-tools]
-== Relational to Graph Import Tools
-
-There are 3 main approaches to moving relational data to a graph.
-We will briefly cover how each operates on this page, but more detailed walkthroughs are in the linked pages.
-
-*1)* link:/developer/guide-import-csv/[LOAD CSV^]: possibly the simplest way to import data from your relational database.
-Requires a dump of individual entity-tables and join-tables formatted as CSV files.
-
-*2)* https://neo4j-contrib.github.io/neo4j-apoc-procedures/[APOC^]: Awesome Procedures on Cypher.
-Created as an extension library to provide common procedures and functions to developers.
-This library is especially helpful for complex transformations and data manipulations.
-Useful procedures include apoc.load.jdbc, apoc.load.json, and others.
-
-*3)* https://neo4j.com/developer/neo4j-etl/[ETL Tool^]: Neo4j Labs UI tool that translates relational to graph from a JDBC connection.
-Allows bulk data import for large data sets with a fast performance and simple user experience.
-
-*4)* https://medium.com/neo4j/getting-started-with-kettle-and-neo4j-32ff15b991f9[Kettle^]: open-source tool for enterprise-scale data export and import.
-Handles a variety of data sources and large data sets easily and organizes the data flow process.
-
-*5)* Other ETL tools: there are also a few vendor and community tools available for similar etl processes and GUI interaction for getting data in various formats into and out of Neo4j.
-Some of these tools also can map out the flow and transformation of data through the system.
-
-*6)* link:/developer/language-guides/[Programmatic via drivers]: ability to retrieve data from a relational database (or other tabular structure) and use the bolt protocol to write it to Neo4j through one of the drivers with your programming language of choice.
-
-[NOTE]
---
-You should create and understand your link:/developer/data-modeling/[graph data model] before transferring the data from an existing relational structure to a graph.
-If you do not have a good data model, then jumping into the import can cause frustration on data cleanup later.
---
-
-[#relational-load-csv]
-== LOAD CSV
-
-This built-in Cypher function allows users to take existing or exported CSV files and load them into Neo4j with Cypher statements to read, transform, and import the data to the graph database.
-It allows the user to run statements individually or run them batched in a Cypher script.
-Because this functionality is provided in Cypher out-of-the-box, you do not need any additional plugins or configuration, and those already familiar with Cypher may prefer this route.
-
-However, certain difficult or complex transformations may not be easily achievable or provided in Cypher.
-For those cases, you might need to add an `APOC` procedure to the `LOAD CSV` statements or use another import tool.
-
-=== LOAD CSV Resources
-* Cypher Manual: link:/docs/cypher-manual/current/clauses/load-csv/[LOAD CSV^]
-* Guide: link:/developer/guide-import-csv/[CSV Import]
-* Docs Tutorial: link:/docs/getting-started/current/cypher-intro/load-csv/[LOAD CSV for import^]
-
-[#relational-apoc]
-== APOC
-
-APOC is Neo4j's utility library for handling data import, as well as data transformations and manipulations.
-From converting values to altering the data model, this library can manage it all, allowing you to combine and chain procedures in order to get exactly the results you are looking for.
-
-For data import, APOC offers several options depending on your data source and format.
-It can import files or data from a URL in CSV, JSON, or XML formats, as well as loading data straight from a database (using JDBC).
-When you call these procedures, you can pass in the data source and use other procedures to manipulate data or regular Cypher to insert or update to the database.
-There are also procedures for batching data, adding wait/sleep commands, and handling large data sets or temperamental data sources.
-
-The transformation procedures in this library are nearly endless, allowing the developer to process dynamic labels or relationships, correct/skip null or empty values, format dates or other values, generate hashes, and handle other tricky data scenarios.
-If you are in need of a way for flexible and custom data handling, `APOC` could be the way to go.
-The downside to using this library for complicated scenarios is that it may result in many lines of code to handle multiple data transformations.
-
-=== APOC Resources
-* Documentation: https://neo4j-contrib.github.io/neo4j-apoc-procedures/index35.html[APOC^]
-* Videos: https://youtu.be/e8UfOHJngQA[APOC Video Series^]
-* Source code: https://github.com/neo4j-contrib/neo4j-apoc-procedures[Github project^]
-
-[#relational-etl-tool]
-== ETL Tool
-
-Neo4j's ETL tool provides a simple GUI that allows you to load data from nearly any type of relational database to a Neo4j instance.
-The process has you set up a JDBC connection to nearly any type of relational database, then does some auto-mapping to a graph data model rendered as a visualization that you can edit to your use case.
-Finally, you can choose whether the load occurs on a running or shutdown Neo4j instance and import the data.
-
-This tool provides a simple, straightforward process for an initial import from a relational database to Neo4j quickly and efficiently.
-However, it does not provide the ability at this point in time to handle incremental loads or updates to existing data.
-It is a community-driven tool, so updates are made as needed and not on a scheduled timeline.
-
-=== ETL Tool Resources
-* Developer guide: https://neo4j.com/developer/neo4j-etl/[Neo4j ETL Tool^]
-* Blog post: https://medium.com/neo4j/tap-into-hidden-connections-translating-your-relational-data-to-graph-d3a2591d4026[Translating Relational Data to Graph^]
-* Source code: https://github.com/neo4j-contrib/neo4j-etl[Github project^]
-
-[#relational-kettle]
-== Kettle
-
-This highly diverse and flexible data loading tool has several connection options to and from Neo4j, as well as capabilities to generate CSV files from other systems to load into your graph database.
-Its goal is to help you create and manage a simple, self-describing, and maintainable data integration process from beginning to end.
-
-Kettle builds a data loading process that is self-documenting and transparent.
-It is especially helpful if the data import requires data retrieval from multiple sources or if there are multiple dependent steps to build or update your graph.
-If you need to transformation the data coming in or going out, Kettle can handle different kinds of manipulations, including aggregations.
-Processes that need to log information to Neo4j or flexibility for embedding in various environments also make excellent cases for using Kettle.
-
-All of this functionality is bundled out-of-the-box through a simple, yet powerful GUI for your ETL developers.
-Cooperation with Neo4j simply requires the plugins for our graph data integration.
-
-=== Kettle Resoures
-* Kettle Download: https://sourceforge.net/projects/pentaho/files/[Open-source project on SourceForge^]
-* Neo4j Plugins: https://github.com/knowbi/knowbi-pentaho-pdi-neo4j-output/releases/[Integrate Kettle with Neo4j^]
-* Blog post: https://medium.com/neo4j/getting-started-with-kettle-and-neo4j-32ff15b991f9[Getting Started with Kettle and Neo4j^]
-
-[#relational-other-tools]
-== Other ETL Tools
-
-There are a few other data integration tools provided by other individuals or companies that work well with Neo4j.
-Open-source options such as Talend or Nifi offer simple processes for simple processes with already-familiar tools.
-
-=== Other Resources
-* Talend: https://help.talend.com/reader/mjoDghHoMPI0yuyZ83a13Q/x2QYS1x3sfOsjGD3s5RtkQ[Writing data to Neo4j^]
-* Documentation: http://sim51.github.io/neo4j-talend-component/index.html[Talend Neo4j Connector^]
-* Blog post: https://neo4j.com/blog/fun-with-music-neo4j-and-talend/[Fun with music, Talend, and Neo4j^]
-* Source code: https://github.com/jonathantelfer/nifi-neo4j[Apache Nifi / Neo4j Connector^]
-
-[#relational-drivers]
-== Import Programmatically with Drivers
-
-For importing data using a programming language, you can use the Neo4j driver for your preferred language and execute Cypher statements to/from the database.
-This process is also helpful if you do not have access to the Cypher shell or if the data is not available as an accessible file.
-
-You can set up the driver connection to Neo4j, and then execute Cypher statements that pass from the application-level through the driver and to the database for various operations - including large amounts of inserts and updates.
-Using the driver and programming language can be very useful for incremental updates to data passed from other systems into Neo4j.
-
-=== Driver Import Resources
-* Blog post: https://medium.com/neo4j/5-tips-tricks-for-fast-batched-updates-of-graph-structures-with-neo4j-and-cypher-73c7f693c8cc[Tips and Tricks for Fast-Batched Import with Neo4j^]
diff --git a/modules/ROOT/pages/relational-to-graph-modeling.adoc b/modules/ROOT/pages/relational-to-graph-modeling.adoc
deleted file mode 100644
index 9a21f3fc..00000000
--- a/modules/ROOT/pages/relational-to-graph-modeling.adoc
+++ /dev/null
@@ -1,123 +0,0 @@
-= Model: Relational to Graph
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: modeling
-:tags: graph-modeling, data-model, schema, relational-to-graph, translating-model
-:description: For those with a background in relational data modeling, this guide will help transfer your existing knowledge of the processes and components used for relational data modeling into graph data modeling.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/data-modeling/relational-to-graph-modeling/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-It will help to compare and contrast the steps of each process and help you identify where the data modeling is similar or different for each type of database.
-
-.Prerequisites
-[abstract]
-You should be familiar with Neo4j's link:/developer/graph-database#property-graph[property graph data model] and relational databases.
-
-[role=expertise {level}]
-{level}
-
-[#rdbms-to-graph-model]
-== Modeling: From Relational to Graph
-
-If you are familiar with the relational data model that has tables, columns, relationship cardinalities, and other components, graph data modeling will not seem entirely foreign.
-The design of the data model still needs to be based upon requirements for access, queries, performance expectation, and business logic.
-However, the structure of a graph data model is laid out slightly differently.
-These differences were discussed in the xref:graph-db-vs-rdbms.adoc[Concepts: RDBMS to Graph] section earlier in these guides.
-
-You may have an entirely new project that you want to create a graph data model, but are only familiar with how to create the relational model.
-Or you could already have an existing project with a relational model that you want to convert to graph.
-Either way, this guide will take existing knowledge of the relational data model and show you how to use that to create a graph model.
-
-[#rdbms-graph-architecture]
-== Relational and Graph Architecture
-
-As a quick overview, remember that relational databases rely upon index lookups and table joins to connect different entities.
-This quickly becomes a problem for performance, especially when there are several tables joined, millions of rows on tables, or complex queries that traverse various levels through subqueries.
-
-In our example from the concepts page, to find which departments Alice works for, you would need to query the `Person` table to find the row representing Alice, which is tied to a unique ID as the primary key.
-Then, your query would go to the associative entity table (`Person_Dept`) to find where her ID is tied to one or more department IDs.
-Finally, the query would check the `Department` table to find the actual values for those department IDs you found in the associative entity table.
-
-The image below reviews this example we just described.
-
-.Relational - Person and Department tables (click to zoom)
-image:{img}relational_model.jpg[role="popup-link"]
-
-In a graph, you do not need to worry about table joins and index lookups because graph data is structured by each, individual entity and its relationships with other individual entities.
-
-Ok, so how do we go from creating relational data models to a graph data model?
-
-[#model-transformation]
-== Data Model Transformation Tips
-
-Let us look at some of the key components in a relational data model and translate those into components of a graph data model.
-The steps to help you with the transformation of a relational diagram are listed below.
-
-- *_Table to Node Label_* - each entity table in the relational model becomes a label on nodes in the graph model.
-- *_Row to Node_* - each row in a relational entity table becomes a node in the graph.
-- *_Column to Node Property_* - columns (fields) on the relational tables become node properties in the graph.
-- *_Business primary keys only_* - remove technical primary keys, keep business primary keys.
-- *_Add Constraints/Indexes_* - add unique constraints for business primary keys, add indexes for frequent lookup attributes.
-- *_Foreign keys to Relationships_* - replace foreign keys to the other table with relationships, remove them afterwards.
-- *_No defaults_* - remove data with default values, no need to store those.
-- *_Clean up data_* - duplicate data in denormalized tables might have to be pulled out into separate nodes to get a cleaner model.
-- *_Index Columns to Array_* - indexed column names (like email1, email2, email3) might indicate an array property.
-- *_Join tables to Relationships_* - join tables are transformed into relationships, columns on those tables become relationship properties
-
-If you apply the items in the list above to our example finding Alice's departments, we can come to a graph like the one shown below.
-
-.Graph - Alice and 3 Departments as nodes (click to zoom)
-image:{img}relational_graph_model.jpg[role="popup-link"]
-
-Though the two models have similarities such as categorizing data by using either a table structure or a label, the graph model does not confine data to a pre-defined and strict table/column layout.
-We will look at another example in the next section.
-
-[#org-domain-model]
-== Organizational Domain Data Model
-
-To give us another chance to practice, we will use a standard organizational domain and show how it would be modeled in a relational database versus a graph database. To give yourself an extra challenge, try to create the graph data model on your own and then see how closely it lines up!
-
-.Organizational Domain - Relational Model
-image:{img}relational_org_chart.jpg[role="popup-link"]
-
-=== Conversion Steps
-
-First, we can categorize our tables by main domain tables and associative entity tables by colors.
-Then, we can turn our table names into node labels.
-In this case, `Project`, `Person`, `Department`, and `Organization` become labels in our graph model.
-
-The rows on our tables become their own nodes and the columns in those rows become the properties on those nodes.
-For example, your row on the Person table will become a node with your name and date of birth as the properties on your node.
-Any indexed columns that allow multiple similar values will become an array (such as skill1, skill2, skill3 columns translate to three values stored in an array property on a node).
-
-If there are any technical primary keys (in other words, primary keys that were created simply to make the row unique - like a project_id in case there are multiple projects with the same title), then remove those and only keep the properties that are needed for the business requirements.
-You will also need to add unique constraints for the business primary keys in order to ensure the database will not allow duplicates.
-
-Foreign keys that would aid in relational join lookups are transformed into relationships, as they show the links between the nodes.
-Join tables (or associative entity tables) become relationships, as well, with any join table columns moved to relationship properties.
-
-Since you only store the needed properties in Neo4j, you do not need to store nulls and empty values, so you can remove any default values that may have been created in a relational model.
-
-Finally, any duplicate data created to normalize tables or de-normalize for simplicity's sake needs removed, as it is unneeded in a graph.
-
-After this process, your graph data model should look something like the image below.
-
-.Answer: Organizational Domain - Graph Model
-image:{img}graph_org_chart.jpg[600,600,role="popup-link"]
-
-It is important to have an basic understanding of the graph model before you start to import data, as it becomes easier to hydrate that model or adjust it later, as needs change.
-In an upcoming guide, how you model your graph data can impact queries, performance, and model changes.
-
-[#modeling-resources]
-== Resources
-* https://dzone.com/refcardz/from-relational-to-graph-a-developers-guide?chapter=1[DZone Refcard: From Relational to Graph^]
-* xref:graph-db-vs-rdbms.adoc[Concepts: RDBMS to Graph]
-* xref:cypher:guide-sql-to-cypher.adoc[From SQL to Cypher]
-* xref:graph-database.adoc#property-graph[Review: Property Graph Model]
-* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^]
\ No newline at end of file
diff --git a/modules/ROOT/pages/resources.adoc b/modules/ROOT/pages/resources.adoc
deleted file mode 100644
index 8aded77a..00000000
--- a/modules/ROOT/pages/resources.adoc
+++ /dev/null
@@ -1,180 +0,0 @@
-= Neo4j Learning Resources
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:category: documentation
-:tags: resources, community, contribute, documentation, graphgists, training
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/appendix/getting-started-resources/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#communities]
-== Neo4j Communities
-
-For everything Neo4j, check out our https://community.neo4j.com[Neo4j Online Community^] to post or answer questions, review or create blog posts and other content, and see or share the latest project using Neo4j!
-
-There are also a few other ways to find Neo4j content listed below.
-
-* https://medium.com/neo4j[Neo4j Developer Blog on Medium^]
-* https://youtube.com/neo4j[Neo4j Videos on Youtube^]
-* https://community.neo4j.com/c/community-content-blogs[All Community Blogs & Posts^]
-* https://discord.gg/neo4j[Discord Chat^]
-* http://neo4j.meetup.com[Neo4j Meetups - worldwide^]
-* http://twitter.com/neo4j[Neo4j on Twitter^]
-* http://github.com/neo4j[Neo4j^] and http://github.com/neo4j[Neo4j-Contrib^] on GitHubs
-* http://stackoverflow.com/questions/tagged/neo4j[Neo4j on StackOverflow^]
-* http://www.quora.com/Neo4j[Neo4j on Quora^]
-* http://slideshare.net/neo4j/presentations[Neo4j Presentations^]
-
-[#neo4j-books]
-== Neo4j Books
-
-There are a link:/books[number of books^] available - the most popular of which are the O'Reilly link:https://neo4j.com/graph-databases-book[Graph Databases^] and the brand new https://neo4j.com/graph-algorithms-book[Graph Algorithms^] books.
-
-* https://neo4j.com/graph-algorithms-book[Graph Algorithms^] by Mark Needham and Amy Hodler (free)
-* link:https://neo4j.com/graph-databases-book[O'Reilly Graph Databases^] by Ian S. Robinson, Jim Webber and Emil Eifrem (free)
-* link:/learning-neo4j-book/[Learning Neo4j^] by Rik Van Bruggen (free)
-* link:/books/neo4j-2-0-eine-graphdatenbank-fur-alle/[Neo4j - Eine Graphdatenbank^] für alle by Michael Hunger (kostenlos)
-* link:/resources/rdbms-developer-graph-white-paper/[Graph Databases for RDBMS Developers^] (free)
-* https://www.packtpub.com/catalogsearch/result/?q=Neo4j&product_type_filter=Book&released=Available[9 Books on Neo4j from Packt^]
-* link:/books/practical-neo4j/[Practical Neo4j^] by Greg Jordan
-* link:/books/learning-cypher/[Learning Cypher^] by Onofrio Panzarino
-* https://neo4j.com/books/beginning-neo4j/[Beginning Neo4j^] by Chris Kemper
-* https://www.d-booker.fr/neo4j2-en/583-neo4j-a-graph-project-story.html[Neo4j : A Graph Project Story^] by Nicolas Mervaillie, Sylvain Roussy, Nicolas Rouyer, Frank Kutzler
-
-[#docs-manuals]
-== Neo4j Documentation
-
-Our https://neo4j.com/docs/[reference manuals^] are vey comprehensive and the https://neo4j.com/docs/cypher-refcard/current/[Cyphers reference card^] is good to keep handy if you're learning and using our query language.
-
-* https://neo4j.com/docs/getting-started/current/[Getting Started Guides^]
-* https://neo4j.com/docs/cypher-manual/current/[Cypher Manual^]
-* https://neo4j.com/docs/cypher-refcard/current/[Cypher Reference Card^]
-* https://neo4j.com/docs/graph-data-science/current/[Graph Algorithms^]
-* https://neo4j.com/docs/java-reference/current/[Extending Neo4j^]
-* https://neo4j.com/docs/operations-manual/current/[Neo4j Operations Manual^]
-
-=== Driver Manuals
-* https://neo4j.com/docs/dotnet-manual/current/[.NET Driver Manual^]
-* https://neo4j.com/docs/go-manual/current/[Go Driver Manual^]
-* https://neo4j.com/docs/java-manual/current/[Java Driver Manual^]
-* https://neo4j.com/docs/javascript-manual/current/[JavaScript Driver Manual^]
-* https://neo4j.com/docs/python-manual/current/[Python Driver Manual^]
-
-[#graphgist-examples]
-== Neo4j Graph Gist Examples
-
-Neo4j Graph Gists are interactive, live graph examples for a variety of use-cases.
-
-They are described by simple text documents containing data models and use cases in prose and pictures.
-More importantly they are visualized in interactive datasets (provided in Cypher) with live queries representing use-cases on that example data.
-
-* https://neo4j.com/graphgists[Featured Neo4j GraphGists^]
-* http://portal.graphgist.org/[Graph Gist Author Portal^]
-
-[#training-offers]
-== Neo4j Training Offerings
-
-The online course are a great way to get started in a self-paced mode:
-
-link:https://graphacademy.neo4j.com/[Neo4j Online Training Courses^]
-
-The instructor-led training classes give you access to Neo4j expert trainers:
-
-link:https://neo4j.com/events/world/training/[Neo4j Instructor-led Training^]
-
-As an experienced Neo4j user you can take the Neo4j Certification Exam to become a *Certified Neo4j Professional*.
-The exam is free of charge and can be retaken. It tests you on basic Neo4j knowledge and Cypher.
-
-link:https://neo4j.com/graphacademy/neo4j-certification/[Certified Neo4j Professional Exam^]
-
-[#external-tutorials]
-== External Neo4j Tutorials
-
-Provided by members of our community these courses on Neo4j cover different aspects in a variety of approaches.
-
-* https://learning.oreilly.com/learning-paths/learning-path-graph/9781492053620[O'Reilly Graph Algorithms by Mark Needham^]
-* https://www.lynda.com/Neo-j-training-tutorials/2055-0.html[Lynda.com Neo4j Training by Will Lyon^]
-* https://subscription-staging.packtpub.com/video/application_development/9781838555580[Packt Graph Algorithms by Estelle Scifo^]
-* https://www.udemy.com/topic/neo4j/[15 Neo4j Courses on Udemy from Intro to Data Science^]
-* https://www.pluralsight.com/courses/graph-databases-neo4j-introduction[Introduction to Neo4j on Pluralsight by Roland Guijt^]
-* https://egghead.io/courses/build-a-neo4j-graphql-api[GraphQL APIs with Neo4j EggHead by Matt Ross^]
-
-[#podcasts-interviews]
-== Neo4j Podcasts / Interviews
-
-* https://soundcloud.com/graphistania[Rik van Bruggens Amazing Podcast Series with many Neo4j Experts^] (http://blog.bruggen.com/search/label/podcast?view=flipcard[transcripts^])
-* https://softwareengineeringdaily.com/2015/08/22/graph-databases-with-ryan-boyd-of-neo4j/[Software Engineering Daily with Ryan Boyd^]
-* https://graphqlpatterns.simplecast.com/episodes/neo4j-and-graphql[GraphQL & Neo4j William Lyon^]
-* https://neo4j.com/blog/graphcast-emil-eifrem-exponential-view-podcast-azeem-azhar/[Exponential View Emil Eifrem^]
-* http://www.se-radio.net/2013/05/episode-194-michael-hunger/[Neo4j on Software Engineering Radio with Michael Hunger^]
-* https://programmierbar.buzzsprout.com/176239/3674173-folge-61-graphdatenbanken-mit-michael-hunger-von-neo4j[ProgrammierBar with Michael Hunger (german)^]
-* http://blog.tatham.oddie.com.au/2014/02/11/podcast-graph-databases-and-neo4j-with-richard-and-carl-from-net-rocks/[Podcast .Net Rocks with Tatham Oddie^]
-* http://www.infoq.com/interviews/jim-webber-neo4j-and-graph-database-use-cases[Jim Webber on Neo4J and Graph Database Use Cases^]
-
-[#videos-youtube]
-== Neo4j Videos
-
-We have a wide range of videos available which you can watch on your own time.
-Those videos support the learning experience and can deepen your understanding of certain topics.
-
-* https://neo4j.com/videos/[Neo4j Videos^]
-* https://neo4j.com/video/nodes-2020/[NODES 2020 Videos^]
-* https://neo4j.com/nodes-2019/[NODES 2019 Videos^]
-* https://neo4j.com/graphconnect-2018/[GraphConnect 2018 Videos^]
-* https://neo4j.com/graphtour/videos/[GraphTour Videos^]
-
-* https://www.youtube.com/watch?v=2b38TVg9ixg&list=PL9Hl4pk2FsvXjk0hrerr78pLN-477pDLo[Neo4j Live Streams^]
-* https://www.youtube.com/playlist?list=PL9Hl4pk2FsvVnz4oi0F8UXiD3nMNqsRO2[Neo4j Online Meetups^]
-* https://www.youtube.com/watch?v=5Tl8WcaqZoc&list=PL9Hl4pk2FsvWM9GWaguRhlCQ-pa-ERd4U[Neo4j Intro Series by Ryan Boyd^]
-* https://www.youtube.com/watch?v=V1DTBjetIfk&list=PL9Hl4pk2FsvXEww23lDX_owoKoqqBQpdq[APOC Series by Michael Hunger^]
-* https://www.packtpub.com/catalogsearch/result/?q=Neo4j&product_type_filter=Video&released=Available[3 Neo4j Videos from Packt]
-
-[#neo4j-integrations]
-== Neo4j Ecosystem Integrations
-
-* http://structr.com[structr Graph Application Framework - CMS, Graph-Server, Schema, User Management & more^]
-* http://graphaware.com/products/[GraphAware Framework Libraries - Recommendation, Algorithms, Tools, Drivers^]
-* http://irregular-bi.tumblr.com/tagged/neo4j[QlikView/QlikSense Integration^]
-* http://fbiville.github.io/liquigraph[LiquiGraph - Database Migration Tool^]
-* http://jqassistant.org[jQAssistant Software Quality Assurance^]
-
-[#presentations]
-== Neo4j Presentations
-
-There are more presentations available on our http://slideshare.net/neo4j/presentations[slideshare account^].
-
-Here are two recordings of our Chief Scientist which are not only informative but also entertaining.
-
-* http://www.infoq.com/presentations/Complex-Data-graph-Neo4j[Tackling Complex Data with Neo4j by Jim Webber on InfoQ^]
-* http://www.infoq.com/presentations/neo4j-graph-theory[A Little Graph Theory for the Busy Developer by Jim Webber on InfoQ^]
-
-[#recommended-experts]
-== Neo4j Staff
-
-* https://markhneedham.com/blog/[Mark Needham on Cypher, Python, and R^]
-* https://www.adamcowley.co.uk[Adam Cowley on JavaScript, Cypher, Recommendations^]
-* http://maxdemarzi.com/[Max de Marzi on Performance, Modeling, Neo4j Extensions^]
-* https://xclave.co.uk/[Charlotte Skardon on .Net, Power BI^]
-* https://faboo.org/tags/neo4j[Bert Radke on Open Data, Import, Modeling^]
-* http://blog.bruggen.com/?view=magazine[Rik van Bruggen on Modeling, Cypher and Use-Cases^]
-* http://jexp.de/blog/[Michael Hunger on Spring Data, Import, Cypher & more^]
-* http://www.lyonwj.com/[William Lyon on Twitter & Spatial^]
-* http://nigelsmall.com/[Nigel Small on Python^]
-* http://www.intelliwareness.org/category/neo4j/[Dave Fauth on Import and BitCoin^]
-* http://blog.armbruster-it.de/tag/neo4j/[Stefan Armbruster on Indexing, Extensions and Deployment^]
-* http://www.ibridge.be[Matt Casters on ETL, Kettle, Data Integration^]
-
-[#friends-neo4j]
-== Friends of Neo4j
-
-* http://tbgraph.wordpress.com/[Tomasz Bratanic on Graph Algorithms, APOC and Data Import^]
-* http://blog.brian-underwood.codes/[Brian Underwood Neo4j & Ruby^]
-* https://www.kennybastani.com/search/label/neo4j[Kenny Bastani on Modeling, Architecture, and Graph Processing^]
-* http://nicolewhite.github.io/[Nicole White on R, Python and Cypher^]
-* https://medium.com/@christophewillemsen[Christophe Willemsen on NLP, PHP, Elasticsearch^]
-* http://jqassistant.org/blog/[Dirk Mahler on Software-Analytics^]
-* https://leanjavaengineering.wordpress.com/?s=neo4j[Robin Bramley on Data Import, Access Control^]
-* http://allthingsgraphed.com/[Caleb Jones on Data Visualization, Astronomical Data^]
-* https://inserpio.wordpress.com/[Lorenzo Speranzoni on Art, Museums, and Spatial^]
diff --git a/modules/ROOT/pages/ruby-course.adoc b/modules/ROOT/pages/ruby-course.adoc
deleted file mode 100644
index f5a57ea6..00000000
--- a/modules/ROOT/pages/ruby-course.adoc
+++ /dev/null
@@ -1,766 +0,0 @@
-= Getting Started with Neo4j and Ruby
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:category: documentation
-:tags: resources, ruby, training
-:description: This course provides an overview on everything that you need to build a Neo4j application with the link:https://www.ruby-lang.org[Ruby programming language^]. Ruby on Rails and Sinatra examples are given but any web framework (or lack thereof) can be used.
-
-
-[subs="attributes"]
-++++
-
-++++
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should have Ruby installed on your system.
-Some experience with Ruby and/or Rails is suggested.
-
-[#ruby-course]
-Let's say you would like to write a web application to track entities for yourself or your organization.
-Being a good link:https://en.wikipedia.org/wiki/Don%27t_repeat_yourself[DRY^] programmer you might decide that what you want is an _asset portal_: an application which gives you an GUI for browsing and editing entities while also making it easy to define new assets.
-This guide will show you how you might start creating such an application using Ruby on Rails and Neo4j.
-
-[#neo4j-ruby]
-== Why Neo4j?
-
-Neo4j is the world's most popular _graph database_.
-This offers a number of advantages:
-
- * Neo4j provides a schemaless representation of both entities and relationships between entities.
- * Relationships between entities are *traversed* rather than *joined*.
- Traversals explore the local subgraph meaning that query times stay the same as your database grows.
- * Because of the traversal paradigm we think in terms of the complex relationships in our data without worrying as much how to model it
-
-Rubyists generally prefer tools which are developer friendly and which don't bother you with details until it's neccessary.
-Neo4j makes it easy to create nodes and relationships in whatever way seems most natural, but you can also change the structure of your database with a query.
-
-[#intro-neo4j]
-== Introduction to Neo4j
-
-Connected information is everywhere in our world.
-Neo4j was built to efficiently store, handle, and query highly-connected elements in your data model.
-With a powerful and flexible data model, you can represent your real-world, variably-structured information without a loss of fidelity.
-The property graph model is easy to understand and handle, especially for object-oriented and relational developers.
-
-[role="pull-right"]
---
-image::{img}simple_graph.png[]
---
-
-The property graph model consists of:
-
-**Nodes**, which have:
-
- * _properties_: schemaless key/value pairs
- * _labels_: describe and group nodes much like tables group rows, but nodes can have multiple labels
-
-**Relationships**, which connect two nodes directionally and have:
-
- * _properties_: schemaless key/value pairs
- * A _type_: gives a description of how it connects the two nodes
-
-While relationships are directional, querying relationships in either direction has no associated performance cost.
-
-=== Cypher
-
-Cypher is Neo4j's built-in query language.
-Cypher queries look like the following code block:
-
-[source,cypher]
-----
-MATCH (p:Person)-[:LIKES]->(f:Fruit)
-RETURN p, f.name
-----
-
-The `MATCH` clause is the most common starting point for Cypher queries.
-It defines a pattern for which to search and returns one result per match.
-For example, we might get the following two matches:
-
-image:{img}cypher_match_1.png[width=49%]
-image:{img}cypher_match_2.png[width=49%]
-
-With the `RETURN` clause, we would end up returning a table such as:
-
-.Result of Cypher query
-[width="99%", options="header"]
-|=============================
-| p                | f.name
-| {name: "Denise"} | "Mango"
-| {name: "Denise"} | "Banana"
-|=============================
-
-Here, you see we can return entire entities in our database rather than just properties.
-This might be returned as a `Hash` in Ruby, though by default in the Neo4j.rb gems these are wrapped in an object.
-
-This is very handy, but it would also be nice to avoid the duplication of our `Person` node.
-You can perform the same match but instead use the `collect` function to aggregate the values:
-
-[source,cypher]
-----
-MATCH (p:Person)-[:LIKES]->(f:Fruit)
-RETURN p, collect(f.name)
-----
-
-.Result of Cypher aggregation query
-[width="99%", options="header"]
-|=============================
-| p                | f.name
-| {name: "Denise"} | ["Mango", "Banana"]
-|=============================
-
-While it's possible to get started using the Neo4j.rb without learning Cypher, it is a very powerful way to query a Neo4j database and is worth learning.
-Also, since the Neo4j.rb project works by making Cypher queries to Neo4j it is good to understand Cypher as your queries get more complex.
-There is a link:/developer/cypher[Cypher tutorial] if you would like to learn more.
-
-[#rb-project]
-== Neo4j in Ruby
-
-For this guide, we will be using the Neo4j.rb project.
-The project consists of the following gems:
-
-include::ruby.adoc[tags=neo4.rb-lib]
-
-Specifically in this guide, we will be using the `ActiveNode` and `ActiveRel` modules from the `neo4j` gem to model nodes and relationships from our database.
-
-[#project-setup]
-== Setup
-
-The following example is in Ruby on Rails, but there is a link:#_an_example_sinatra_application[Sinatra example^] below.
-
-Here, we describe how to create a fresh Rails application with Neo4j as the database.
-If you have an existing Rails application, you can refer to the link:http://neo4jrb.readthedocs.org/en/5.2.x/Setup.html#adding-the-gem-to-an-existing-project[Neo4j.rb documentation^].
-
-Here is how you would setup your asset portal Rails app:
-
-[source,bash]
-----
-rails new asset_portal -m http://neo4jrb.io/neo4j/neo4j.rb -O
-cd asset_portal
-rake neo4j:install[community-latest]
-rake neo4j:start
-----
-
-What do these commands do?
-
-The first creates a new Rails app skipping `ActiveRecord` (the `-O` flag) and setting up Neo4j.rb in your project (the `-m` flag).
-Then, we change into our directory and install the latest version of the community edition of Neo4j into our app directory (into `db/neo4j/development/`).
-Last, we start up our copy of Neo4j.
-
-Next, you should open up your `config/application.rb` file and find the `config.neo4j.\*` lines.
-Here, you have a choice between *embedded* and *server* modes:
-
- * Server mode allows you to connect to Neo4j via it's HTTP JSON APIs.
- * Embedded mode requires JRuby and allows you to run Neo4j as part of your JRuby process.
- This gives you access to the link:http://neo4j.com/docs/stable/javadocs/[Neo4j Java APIs] directly.
-
-By default, you will be configured to Neo4j in server mode on the default port (7474).
-If you would like something other than the default console, take a look at the link:http://neo4jrb.readthedocs.org/en/5.2.x/Setup.html#rails-configuration[documentation^].
-
-****
-[WARNING]
-By default the `rake neo4j:install` command disables Neo4j's authentication.
-It is suggested that you enable the authentication for any exposed Neo4j instances.
-****
-
-To see an example of setting up Neo4j in Rails, check out this short screencast.
-
-++++
-
    
-
-
    
-++++
-
-[#create-models]
-== Create Basic Models
-
-In this guide, we will be setting up different `ActiveNode` models, which will serve as assets.
-This is a textbook example of where we can use class inheritance in Neo4j.rb.
-First, we create some basic models:
-
-[source,bash]
-----
-rails generate scaffold User name:string email:string
-rails generate scaffold Category name:string
-rails generate scaffold Asset title:string
-----
-
-This will generate link:http://guides.rubyonrails.org/command_line.html#rails-generate[scaffolds^] just like any Rails application, with the exception that the models will be `ActiveNode` models rather than `ActiveRecord` models and will look like this:
-
-.app/models/user.rb
-[source,ruby]
-----
-class User
-  include Neo4j::ActiveNode
-  property :name, type: String
-  property :email, type: String
-end
-----
-
-Since Neo4j is schemaless, we need to define our properties in our model.
-
-****
-[NOTE]
-By default there will be a `uuid` property created on our model.
-If you would like to define your own unique identifier you can use the `id_property` method.
-Either can be accessed or changed via the `\#id` and `\#id=` methods.
-****
-
-To learn more about properties, check out this short screencast.
-
-++++
-
    
-
-
    
-++++
-
-Once we've set up those models, we can define our asset models like so:
-
-[source,bash]
-----
-rails generate scaffold Book isbn:string title:string year_published:integer author:references category:references
-----
-
-That should generate a model that looks like this:
-
-.app/models/book.rb
-[source,ruby]
-----
-class Book
-  include Neo4j::ActiveNode
-  property :isbn, type: String
-  property :title, type: String
-  property :year_published, type: Integer
-
-  has_one :in_or_out_or_both, :author, type: :FILL_IN_RELATIONSHIP_TYPE_HERE
-  has_one :in_or_out_or_both, :category, type: :FILL_IN_RELATIONSHIP_TYPE_HERE
-
-end
-----
-
-You should change that to look like the following (note the `Asset` superclass definition):
-
-.app/models/book.rb
-[source,ruby]
-----
-class Book < Asset
-  id_property :isbn
-  property :year_published, type: Integer
-
-  has_one :in, :author, type: :CREATED, model_class: :User
-  has_one :out, :category, type: :HAS_CATEGORY
-end
-----
-
-****
-[NOTE]
-You can remove the `title` property because it is inherited from the `Asset` model.
-****
-
-****
-[NOTE]
-We also need to specify our Neo4j relationship directions and types here.
-Since `author` isn't enough for `ActiveNode` to understand that we want to reference users, we specify a `model_class` option.
-****
-
-By inheriting from `Asset`, our `Book` model will create nodes with two labels (`Book` and `Asset`).
-Likewise, when you query for nodes via the `Book` model, it will only find nodes which have both labels.
-
-Lastly, we just need to make a couple of small fixes.
-Change these lines to get the names of book authors and categories:
-
-.app/views/books/index.html.erb
-[source,erb]
-----
-<%= book.author.try(:name) %>
-<%= book.category.try(:name) %>
-----
-
-And change these lines to be able to choose the author when creating or editing books:
-
-.app/views/books/_form.html.erb
-[source,erb]
-----
-
    
-  <%= f.label :author %>
    
-  <%= f.select :author, options_from_collection_for_select(User.all, :id, :name, @book.author.try(:id)), include_blank: true %>
-
    
-
    
-  <%= f.label :category %>
    
-  <%= f.select :category, options_from_collection_for_select(Category.order(:name), :id, :name, @book.category.try(:id)), include_blank: true %>
-
    
-----
-
-So that you can set your associations, change the `book_params` method in the `BooksController` to remove the `\_id`:
-
-.app/controllers/books_controller.rb
-[source,ruby]
-----
-def book_params
-  params.require(:book).permit(:isbn, :title, :year_published, :author, :category)
-end
-----
-
-[#run-migrations]
-== Running the migrations
-
-Now that we have created our scaffolding, let's run the migrations to create constraints for our models and start up our Rails server:
-
-[source,bash]
-----
-rake neo4j:migrate
-rails s
-open http://localhost:3000/books
-----
-
-From there, you can create, update, browse, and delete books via the scaffolding.
-You can visit `/books`, `/users`, and `/categories` to get entry points into the various sections.
-
-[#power-up]
-== The Fun Stuff
-
-If you just wanted to do simple CRUD operations, there are plenty of other databases to choose from.
-How can we do something a bit more fun using the power of Neo4j?
-
-=== Eager Loading
-
-First, let's look at a performance improvement, which is not available from `ActiveRecord`.
-When you go to your list of books, you should see something like this in your log:
-
-.log/development.log
-[source,log]
-----
-Started GET "/books/" for ::1 at 2016-12-23 14:50:39 -0500
-Processing by BooksController#index as HTML
-  Rendering books/index.html.erb within layouts/application
- HTTP REQUEST: 6ms GET http://localhost:7474/db/data/schema/constraint (0 bytes)
- HTTP REQUEST: 3ms GET http://localhost:7474/db/data/schema/index (0 bytes)
- Book MATCH (n:`Book`:`Asset`) RETURN n HTTP REQUEST: 5ms POST http://localhost:7474/db/data/transaction/commit (1 bytes)
-
- Book#author MATCH (previous:`Book`:`Asset`) WHERE (ID(previous) = $ID_previous) OPTIONAL MATCH (previous)<-[rel1:`CREATED`]-(next:`User`) RETURN ID(previous), collect(next) | {:ID_previous=>4}
- HTTP REQUEST: 5ms POST http://localhost:7474/db/data/transaction/commit (1 bytes)
-
- Book#category MATCH (previous:`Book`:`Asset`) WHERE (ID(previous) = $ID_previous) OPTIONAL MATCH (previous)-[rel1:`HAS_CATEGORY`]->(next:`Category`) RETURN ID(previous), collect(next) | {:ID_previous=>4}
- HTTP REQUEST: 4ms POST http://localhost:7474/db/data/transaction/commit (1 bytes)
-  Rendered books/index.html.erb within layouts/application (32.7ms)
-Completed 200 OK in 84ms (Views: 81.5ms)
-----
-
-If you find that a bit hard to read, then you can add the following line to your application's configuration:
-
-.config/application.rb
-[source,ruby]
-----
-config.neo4j.pretty_logged_cypher_queries = true
-----
-
-****
-[IMPORTANT]
-Don't forget to restart your Rails server!
-****
-
-Once you complete that, your log will look more like this:
-
-.log/development.log
-[source,log]
-----
-Started GET "/books/" for ::1 at 2016-12-23 14:51:34 -0500
-Processing by BooksController#index as HTML
-  Rendering books/index.html.erb within layouts/application
- Book
-  MATCH (n:`Book`:`Asset`)
-  RETURN n
- HTTP REQUEST: 5ms POST http://localhost:7474/db/data/transaction/commit (1 bytes)
- Book#author
-  MATCH (previous:`Book`:`Asset`)
-  WHERE (ID(previous) = $ID_previous)
-  OPTIONAL MATCH (previous)<-[rel1:`CREATED`]-(next:`User`)
-  RETURN
-    ID(previous),
-    collect(next) | {:ID_previous=>4}
- HTTP REQUEST: 4ms POST http://localhost:7474/db/data/transaction/commit (1 bytes)
- Book#category
-  MATCH (previous:`Book`:`Asset`)
-  WHERE (ID(previous) = $ID_previous)
-  OPTIONAL MATCH (previous)-[rel1:`HAS_CATEGORY`]->(next:`Category`)
-  RETURN
-    ID(previous),
-    collect(next) | {:ID_previous=>4}
- HTTP REQUEST: 3ms POST http://localhost:7474/db/data/transaction/commit (1 bytes)
-  Rendered books/index.html.erb within layouts/application (20.2ms)
-Completed 200 OK in 51ms (Views: 48.3ms)
-----
-
-First, the books are loaded, and then separate queries are made to get all of the authors and categories for those books.
-
-With `ActiveRecord`, you would need to specify an `includes` in order to make this happen rather than having each entity loaded individually.
-`ActiveNode`, on the other hand, makes the assumption that if you refer to an association from a list of items, you are almost certainly going to want that association for all of the objects.
-
-[#step-further]
-== One Step Further
-
-But we can do better!
-Now, modify the `index` action of the `BooksController` like so:
-
-.app/controllers/books_controller.rb
-[source,ruby]
-----
-def index
-  @books = Book.all.with_associations(:author, :category)
-end
-----
-
-The `with_associations` method is similar to `includes`, except that our associations are loaded *_in the same query_* using the `collect()` function demonstrated earlier.
-
-.log/development.log
-[source,log]
-----
-Started GET "/books/" for ::1 at 2016-12-23 14:52:16 -0500
-Processing by BooksController#index as HTML
-  Rendering books/index.html.erb within layouts/application
- Book
-  MATCH (n:`Book`:`Asset`)
-  WITH n
-  OPTIONAL MATCH (n)<-[:`CREATED`]-(author)
-  WHERE (author:`User`)
-  WITH
-    n,
-    collect(author) AS author_collection
-  OPTIONAL MATCH (n)-[:`HAS_CATEGORY`]->(category)
-  WHERE (category:`Category`)
-  WITH
-    n,
-    collect(category) AS category_collection,
-    author_collection
-  RETURN
-    n,
-    [author_collection,category_collection]
- HTTP REQUEST: 6ms POST http://localhost:7474/db/data/transaction/commit (1 bytes)
-  Rendered books/index.html.erb within layouts/application (11.8ms)
-Completed 200 OK in 46ms (Views: 43.6ms)
-----
-
-What we get is a list of `Book` objects that are pre-populated with authors and categories.
-
-[#recommendations]
-== Recommendations
-
-You may have heard that Neo4j makes building recommendations from your data easy.
-Let's take a look at how we might make some recommendations.
-For this, we are going to introduce `has_many` associations.
-Since entities are connected via relationships in Neo4j, the database doesn't draw any distinction for when we want to have a single relationship or many to/from a node.
-In our Ruby apps, however, it is often convenient to be able to draw this distinction.
-
-To learn more about associations, check out this short screencast.
-
-++++
-
    
-
-
    
-++++
-
-First, change the `category` association for the `Book` model:
-
-.app/models/book.rb
-[source,ruby]
-----
-has_many :out, :categories, type: :HAS_CATEGORY
-----
-
-Don't forget to change the `:category` argument in the `with_associations` call in the controller to `:categories` like below.
-
-.app/controllers/books_controller.rb
-[source,ruby]
-----
-  def index
-    @books = Book.all.with_associations(:author, :categories)
-----
-
-Then add a `books` association to the `Category` model:
-
-.app/models/category.rb
-[source,ruby]
-----
-has_many :in, :books, origin: :categories
-----
-
-Since we have changed the `has_one` to `has_many` for the book categories, we should update our scaffold UI to match:
-
-.app/controllers/books_controller.rb
-[source,ruby]
-----
-def index
-  @books = Book.all.with_associations(:author, :categories)
-end
-
-... Further down ...
-
-def book_params
-  params.require(:book).permit(:isbn, :title, :year_published, :author, :category_ids => [])
-end
-----
-
-.app/views/books/index.html.erb
-[source,erb]
-----
-Categories
-
-... Further down ...
-
-
-  
      
-  <% book.categories.each do |category| %>
-    
    • <%= link_to category.name, category %>
      • 
-  <% end %>
-  
    
-
-----
-
-.app/views/books/_form.html.erb
-[source,erb]
-----
-
    
-  <%= f.label :categories %>
    
-  <%= f.select :category_ids, options_from_collection_for_select(Category.order(:name), :id, :name, @book.categories.map(&:id)), {include_blank: true}, {multiple: true, size: 5} %>
-
    
-----
-
-.app/views/books/show.html.erb
-[source,erb]
-----
-
    
-  Categories:
-  <%= @book.categories.map(&:name).to_sentence %>
-
    
-----
-
-Whew!
-
-Now, with the ability for a book to have many categories and for a category to have many books, you can have a much better picture about recommending books.
-
-[#query-recommendations]
-== Querying for Recommendations
-
-It is simple to get a start on querying potential recommendations.
-Try running this in your Rails console:
-
-[source,ruby]
-----
-Book.all.categories.books.to_a
-----
-
-It should show you the query which was made and it should look something like:
-
-[source,cypher]
-----
-MATCH (n:`Book`:`Asset`)
-MATCH (n)-[rel1:`HAS_CATEGORY`]->(node3:`Category`)
-MATCH (node3)<-[rel2:`HAS_CATEGORY`]-(result_books:`Book`:`Asset`)
-RETURN result_books
-----
-
-This query is finding all of the books that share categories with all other books.
-This is not particularly useful until we start introducing some variables.
-In the `neo4j` gem, this is called _association chaining_.
-For more information about association chaining, check out this short screencast:
-
-++++
-
    
-
-
    
-++++
-
-[#category-query]
-== Finding Shared Categories
-
-What if you wanted to list every book and find out, for every other book with which it shares a category, how many categories it shares?
-
-[source,ruby]
-----
-Book.as(:book).
-  categories(:category).
-  books(:other_book).
-  pluck('book', 'other_book', 'count(category)')
-----
-
-Notice how we are starting to assign variables.
-These eventually become the variables in the cypher query made to Neo4j.
-
-Taking it a step further, let's create a query which finds, all the books that share at least two categories.
-We can also display these recommendations in our app like so:
-
-.app/controllers/books_controller.rb
-[source,ruby]
-----
-@recommendations = Book.as(:book).
-                    categories(:category).
-                    books(:other_book).
-                    where('book <> other_book').
-                    query.
-                    with('book, other_book, count(category) AS count').
-                    where('count > 1').
-                    pluck('book.isbn', 'collect(other_book)')
-@recommendations = Hash[*@recommendations.flatten(1)]
-----
-
-.app/views/books/index.html.erb
-[source, erb]
-----
-Recommendations
-
-... Further down ...
-
-<%= (@recommendations[book.isbn] || []).map(&:title).to_sentence %>
-----
-
-Of course, we don't want to put too much logic in the controller, so we can extract this to a model class method:
-
-.app/controllers/books_controller.rb
-[source,ruby]
-----
-Book.recommendations
-----
-
-.app/controllers/books_controller.rb
-[source,ruby]
-----
-def self.recommendations
-  recommendations = all(:book).
-                      categories(:category).
-                      books(:other_book).
-                      where('book <> other_book').
-                      query.
-                      with('book, other_book, count(category) AS count').
-                      where('count > 1').
-                      pluck('book.isbn', 'collect(other_book)')
-
-  Hash[*recommendations.flatten(1)]
-end
-----
-
-Because the `all` method starts it off, we can actually add this to an existing chain rather than just calling it on the `Book` model.
-For example, if we had a `recent` scope which only gave us books from the past ten years:
-
-[source,ruby]
-----
-Book.recent.recommendations
-----
-
-[#doing-more]
-== Doing More
-
-Are you getting into the idea of using Neo4j?
-Great!
-If you still have a lot of questions, there are a number of resources to help you along with your journey.
-
-For the fastest help or answers to questions, take a look at or reach out to us on our https://community.neo4j.com[Neo4j Online Community^]!
-
-=== An example Sinatra application
-
-.app.rb
-[source,ruby]
-----
-require 'sinatra'
-require 'neo4j'
-
-neo4j_url = ENV['NEO4J_URL'] || 'http://localhost:7474'
-neo4j_username = ENV['NEO4J_USERNAME']
-neo4j_password = ENV['NEO4J_PASSWORD']
-
-Neo4j::Session.open(:server_db, neo4j_url, basic_auth: {username: neo4j_username, password: neo4j_password})
-
-class Asset
-  include Neo4j::ActiveNode
-  property :title, type: String
-end
-
-class Book < Asset
-  id_property :isbn
-  property :year_published, type: Integer
-
-  has_one :in, :author, type: :CREATED, model_class: :User
-  has_many :out, :categories, type: :HAS_CATEGORY
-
-  def self.recommendations
-    recommendations = all(:book).
-                        categories(:category).
-                        books(:other_book).
-                        where('book <> other_book').
-                        query.
-                        with('book, other_book, count(category) AS count').
-                        where('count > 1').
-                        pluck('book.isbn', 'collect(other_book)')
-
-    Hash[*recommendations.flatten(1)]
-  end
-end
-
-get '/books' do
-  @books = Book.all.with_associations(:author, :categories)
-
-  @recommendations = Book.recommendations
-
-  erb :books_index
-end
-
-----
-
-For the example view, see the one from link:https://github.com/neo4j-examples/neo4jrb_guide_asset_portal/blob/master/app/views/books/index.html.erb[the result^] of the Rails example above.
-
-You can also view Sintra example applications for the link:https://github.com/neo4j-examples/movies-ruby-neo4j-core[neo4j-core^] and link:https://github.com/neo4j-examples/movies-ruby-neo4jrb[neo4j^] gems.
-
-=== The asset_portal application
-
-You can find the result of this guide in its link:https://github.com/neo4j-examples/neo4jrb_guide_asset_portal[GitHub repository^].
-If you would like to play with a more developed application, check out our link:https://github.com/neo4j-examples/asset_portal[asset_portal] app.
-The project introduces a single `AssetController` to avoid the duplication from this guide and also uses other tools like link:http://semantic-ui.com/[Semantic UI^] for a cleaner interface.
-
-=== Getting Help
-
-The maintainers of the `Neo4j.rb` project love to help!
-There is a wonderful link:https://community.neo4j.com[Neo4j Online Community^] if you have questions about installation, configuration, Cypher, or any topic.
-You can also check out the link:http://neo4jrb.io/[Neo4j Ruby website^] and the link:http://neo4jrb.readthedocs.org/[documentation^].
-
-=== More Screencasts
-
-In addition to the screencasts embedded in this guide, there are two others to help you learn more about the `Neo4j.rb` project:
-
-++++
-
    
-
-
    
-
-
    
-
-
    
-++++
-
-=== Integrations with other gems
-
-* link:/developer/ruby/[Building Applications with Ruby]
-
-////
-== Unimplemented ideas:
-
-* When the engine gem is available, refer to that along with the `asset_portal` repo
-
-* Options
-** Switching entirely to Neo4j
-** Using Neo4j and another database for separate entities
-** Using Neo4j to replicate some or all of an existing database
-*** Mongo: https://github.com/neo4j-contrib/neo4j_doc_manager
-*** SQL:
-**** https://github.com/neo4jrb/neo4apis-activerecord
-**** WAY OUT OF DATE: https://github.com/neoid-gem/neoid
-** Doesn't matter too much as the neo4j.rb gems don't conflict with other object mappers
-
-* Usage
-** Cool things that you can do:
-*** Realize that you need to add a node / relationship (just add it to the models)
-*** Connected data query
-////
diff --git a/modules/ROOT/pages/ruby.adoc b/modules/ROOT/pages/ruby.adoc
deleted file mode 100644
index be268b71..00000000
--- a/modules/ROOT/pages/ruby.adoc
+++ /dev/null
@@ -1,191 +0,0 @@
-= Using Neo4j from Ruby
-:slug: ruby
-:level: Intermediate
-:section: Develop with Neo4j
-:section-link: language-guides
-:sectanchors:
-:author: Neo4j
-:programming-language: ruby
-:category: drivers
-:tags: ruby, gem, app-development, applications
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/community-drivers/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-This guide provides an overview of options for connecting to Neo4j using Ruby.
-It will introduce the different drivers and link to relevant resources.
-
-include::./_includes/common-language-prereq.adoc[]
-
-[role=expertise]
-{level}
-
-include::./_includes/community-driver-notice.adoc[]
-
-[#neo4j-ruby]
-== Neo4j for Rubyists
-
-Neo4j has been accessible from Ruby long before version 1.0, first using JRuby on the JVM and then on MRI via the HTTP and now Bolt protocols.
-
-// tag::neo4.rb-lib[]
-[#neo4jrb-lib]
-=== Neo4j.rb
-
-The link:http://neo4jrb.io/[Neo4j.rb project^] is made up of the following Ruby gems:
-
-neo4j-ruby-driver::
-A neo4j driver for ruby with an api consistent with the official drivers. It is based on seabolt and ffi. Available on all rubies
-(including jruby) and all platforms supported by seabolt.
-neo4j-java-driver::
-A neo4j driver for ruby based on the official java implementation. It provides a thin wrapper over the java driver (only
-in jruby).
-activegraph::
-A Object-Graph-Mapper (OGM) for the Neo4j graph database. It tries to follow API conventions established by ActiveRecord
-but with a Neo4j flavor. It requires one of the above drivers.
-neo4j-rake_tasks::
-A set of rake tasks for installing and managing a Neo4j database within your project.
-// end::neo4.rb-lib[]
-
-[cols="1,4"]
-|===
-| icon:browser[] Website                     | http://neo4jrb.io/
-| icon:users[] Authors         | https://twitter.com/klobuczek[Heinrich^], https://twitter.com/klobuczek[Amit^], http://twitter.com/cheerfulstoic[Brian^], http://twitter.com/subvertallchris[Chris^], http://twitter.com/ronge[Andreas^]
-| icon:gift[] Package        | https://rubygems.org/gems/neo4j-ruby-driver[neo4j-ruby-driver^], https://rubygems.org/gems/neo4j-java-driver[neo4j-java-driver^], https://rubygems.org/gems/activegraph[activegraph^]
-| icon:github[] Source       | https://github.com/neo4jrb
-
-| icon:play-circle[] Example | https://github.com/neo4j-examples?q=movies-ruby
-| icon:book[] Docs           | http://neo4jrb.readthedocs.org/en/latest/
-| icon:route[] Tutorial      | link:/developer/ruby-course/[Ruby Tutorial]
-| icon:rss[] Blog            | http://blog.brian-underwood.codes/
-| Protocols | bolt
-|===
-
-History:
-
-1. https://github.com/andreasronge[Andreas Ronge^], one of our Swedish friends from Malmö, started writing his canonical Neo4j Ruby driver since before we hit 1.0.
-
-2. https://github.com/cheerfulstoic[Brian Underwood^] and https://github.com/subvertallchris[Chris Grigg^] joined the project and together released http://neo4j.com/blog/neo4j-rb-3-0-released/[version 3.0^] in September 2014.
-
-3. Starting in 2017, the team around https://github.com/klobuczek[Heinrich Klobuczek] contributed to the project and from *fall 2018 Heinrich took over the Neo4j.rb project as the primary maintainer*.
-
-=== Gem: neo4j-ruby-driver
-
-A ruby driver for neo4j based on seabolt protocol. It provides database connection, manages sessions and transactions.
-
-[source,ruby]
-----
-Neo4j::Driver::GraphDatabase.driver('bolt://localhost:7687',
-                                    Neo4j::Driver::AuthTokens.basic('neo4j', 'password')) do |driver|
-  driver.session do |session|
-    greeting = session.write_transaction do |tx|
-      result = tx.run("CREATE (a:Greeting) SET a.message = $message RETURN a.message + ', from node ' + id(a)",
-                      message: 'hello, world')
-      result.single.first
-    end # session auto closed at the end of the block if one given
-    puts greeting
-  end
-end # driver auto closed at the end of the block if one given
-----
-
-Check https://github.com/neo4jrb/neo4j-ruby-driver/blob/master/docs/dev_manual_examples.rb[Examples^] for more usage.
-
-=== Gem: activegraph
-
-The activegraph gem uses  as an API to connect to the server and provides an ActiveRecord-like experience for use in frameworks.
-It adds modules allowing the creation of models that look and feel very similar to those found in vanilla Ruby on Rails.
-The gem builds on neo4j-ruby-driver's foundation to streamline all aspects of node and relationship CRUD and provides an extremely advanced, intuitive, flexible DSL for generating Cypher.
-
-How to get running:
-
-[source,ruby]
-----
-# See the documentation for setup instructions
-
-class Person
-  include ActiveGraph::Node
-
-  property :name
-
-  has_many :out, :books, type: :OWNS_BOOK
-  has_many :both, :friends, type: :HAS_FRIEND
-end
-
-person = Person.find_by(name: 'Jim')
-
-# Get the books owned by Jim's friends:
-person.friends.books.to_a
-----
-
-[#ruby-deploy]
-== Deployment Considerations
-
-Very often, your gem choice may come down to how you want to deploy:
-
-server::
-  Using Ruby with a separate Neo4j Server over the http or bolt
-
-embedded::
-  Connecting directly to the Neo4j database files from within your Ruby process (this requires JRuby)
-
-A separate Neo4j server is go-to choice for most developers, especially if they are used to other relational or NoSQL databases in frameworks such as Ruby on Rails.
-
-It allows you to have separate web and database servers and allows compatibility with popular PaaSes such as Heroku.
-If this sounds good to you, any of the popular gems are solid choices and you are free to consider whether you want a thin wrapper or the full framework experience.
-
-Neo4j Embedded and JRuby is less common but offers blazing fast performance and access to our core Java API.
-The downside is that JRuby has its own configuration and compatibility demands and hosting a Java app can be difficult.
-Thankfully, modern app servers such as Torquebox and a strong community provide far more options and resources now than ever before.
-If this is right for you, the neo4j-ruby and activegraph will equally offer support via the bolt protocol.
-
-[#gem-integrations]
-== ActiveGraph gem integrations
-
-There are many common gems that you'll want to use with your Neo4j database.
-Many are supported for the Neo4j.rb project:
-
-Authentication
-
- * link:https://github.com/neo4jrb/devise-activegraph[devise-activegraph^]
-
-Authorization
-
- * link:https://github.com/CanCanCommunity/cancancan-activegraph[cancancan-activegraph^]
-
-File Attachment
-
- * link:https://github.com/neo4jrb/neo4jrb-paperclip[neo4jrb-paperclip^] (*)
- * link:https://github.com/neo4jrb/carrierwave-neo4j[carrierwave-neo4j^] (*)
-
-Pagination
-
- * link:https://github.com/neo4jrb/neo4j-will_paginate_redux[neo4j-will_paginate_redux^] (*)
- * link:https://github.com/megorei/kaminari-neo4j[kaminari-neo4j^] (*)
-
-ElasticSearch Integration
-
- * link:https://github.com/neo4jrb/neo4j-searchkick[neo4j-searchkick^] (*)
-
-
-Admin User Interface
-
- * link:https://github.com/cheerfulstoic/rails_admin[rails_admin^] (*)
-
-Integration With the link:https://github.com/neo4j-contrib/spatial[Neo4j Spatial Plugin^]
-
- * link:https://github.com/neo4jrb/neo4jrb_spatial[neo4jrb_spatial^] (*)
-
-Ruby Object Manager
-
- * link:https://github.com/rom-rb/rom-neo4j[rom-neo4j^]
-
-Misc.
-
- * link:https://github.com/subvertallchris/neo4j-even_easier_id[neo4j-even_easier_id^] (BSON UUIDs)
-
-Note: (*) not (yet) compatible with activegraph
-
-[#ruby-example]
-include::./_includes/sample_project.adoc[]
diff --git a/modules/ROOT/pages/slack.adoc b/modules/ROOT/pages/slack.adoc
deleted file mode 100644
index e5b1b27b..00000000
--- a/modules/ROOT/pages/slack.adoc
+++ /dev/null
@@ -1,18 +0,0 @@
-= Neo4j-Users Slack
-:members: 8800
-:author: Neo4j
-:category: documentation
-:tags: resources, slack, community
-
-[#join-slack]
-== Neo4j-Users Slack Group has been retired
-
-image::{img}neo4j-users-slack-group.png[link=http://neo4j.com/slack,float=right,width=300,window="neo4j-users-slack"]
-
-The Neo4j Slack group has been retired. Please join us on https://discord.gg/neo4j[Neo4j Discord].
-
-For dedicated assistance or content posting/announcements, please visit our https://community.neo4j.com[Neo4j Online Community^] instead!
-
-Slack is awesome and has been an incredible way to connect with everyone in the community. 
-As we've grown, connected across different communities, and engaged in more online events,
-Discord has emerged as a better way to keep the conversation going.
diff --git a/modules/ROOT/pages/spring-data-neo4j.adoc b/modules/ROOT/pages/spring-data-neo4j.adoc
deleted file mode 100644
index 0cabe9d6..00000000
--- a/modules/ROOT/pages/spring-data-neo4j.adoc
+++ /dev/null
@@ -1,486 +0,0 @@
-= Spring Data Neo4j
-:level: Intermediate
-:page-level: Intermediate
-:author: Jennifer Reif
-:programming-language: java
-:category: drivers
-:tags: spring, spring-data, SDN, reactive, app-development, applications
-:description: For Java developers who use the Spring Framework or Spring Boot and want to take advantage of reactive development principles, this guide introduces Spring integration through the Spring Data Neo4j project.
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/languages-guides/java/spring-data-neo4j/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-The library provides convenient access to Neo4j including object mapping, Spring Data repositories, conversion, transaction handling, reactive support, and more.
-
-[role=expertise {level}]
-{level}
-
-[#prerequisites]
-== Prerequisites
-
-[abstract]
-* Familiarity with xref:graph-database.adoc[graph database] concepts and the xref:graph-database.adoc#property-graph[property graph model].
-* link:{aura_signup}[Create an AuraDB Free instance] and familiarity with the link:/developer/cypher-query-language[Cypher query language]
-* Some knowledge/experience with Spring.
-Knowing https://spring.io/projects/spring-data/[Spring Data^] and https://spring.io/projects/spring-boot/[Spring Boot^] are both great additions to your toolbox, as well.
-* For this library, please use JDK 11 or later and your favorite IDE.
-
-
-[#reactive-development]
-== Reactive Development
-
-Neo4j (version 4.0+) incorporated the principles of the https://www.reactivemanifesto.org/[reactive manifesto^] for passing data between the database and client with the drivers.
-Developers can take advantage of the reactive approach to process queries and return results.
-This means that communication between the driver, and the database can be managed and adjusted dynamically according to data needs of the client.
-
-Reactive programming principles allow the consuming side (applications and other systems) to specify the amount of data received within a certain window of time.
-Neo4j's database driver will also maintain rate limits for requesting data from the server, providing flow control throughout the entire Neo4j stack.
-
-No matter the volume of transactions or data (even during times of high activity), the system can maintain limits on how much it can send and receive at once based on available resources.
-This prevents overloads and collapses or failures, as well as lost transmissions or later catch up loads during the downtime.
-
-https://projectreactor.io/[Project Reactor^] is the core foundation of many implementations of reactive development, including https://spring.io/reactive[Spring's^].
-Neo4j uses the Spring implementation of Project Reactor components to provide reactive support in related applications with the graph database.
-
-[#spring-data]
-== Spring Data Neo4j
-
-The Spring Data Neo4j 6 is the new major version of the Spring Data Neo4j project.
-One of its feature benefit is the capability and support for reactive transactions, though there are other improvements and additions
-such as fully immutable entity and https://docs.oracle.com/en/java/javase/14/docs/api/java.base/java/lang/Record.html[Java record]-based mapping support.
-
-While SDN provides both imperative and reactive application development, this guide will focus on the reactive implementation.
-Imperative application code and documentation in SDN is available on the https://github.com/spring-projects/spring-data-neo4j[Github project^].
-
-We can see some of the most prominent features and changes in the SDN library listed below.
-
-=== Features
-
-* Support for both imperative and reactive application development
-* Lightweight mapping with built-in OGM (object graph mapping) library
-* Immutable entities (for both Java and Kotlin languages)
-* New Neo4j client and reactive client feature for template-over-driver architecture
-
-SDN has full support for the well-known and understood imperative programming model (much like Spring Data JDBC or JPA).
-It also provides full support for the newer reactive programming based on http://www.reactive-streams.org[Reactive Streams^], including https://spring.io/blog/2019/05/16/reactive-transactions-with-spring[reactive transactions^].
-Both functionalities are included in the same binary.
-
-[NOTE]
---
-The reactive programming model requires a 4.0+ Neo4j instance (previous versions do not support reactive drivers) and reactive Spring on the application side.
---
-
-One key difference of SDN 6 from the previous version of Spring Data Neo4j is that the OGM (object-graph mapping) layer is no longer a separate library.
-Instead, the Spring Data infrastructure now handles OGM's functionality.
-
-[#getting-started]
-== Getting started
-
-Over the next few sections, we will walk through all of the steps for creating a reactive application.
-
-[#prepare-db]
-== Prepare the database
-
-For this example, we will use the Neo4j-standard movie graph data set because it comes for free with every Neo4j instance and is a small size.
-
-If you haven't already, link:/download/[download Neo4j Desktop^] and xref:neo4j-desktop.adoc#desktop-create-DBMS[create/start a database].
-
-You can interact with the database and load the data in a web browser with the URL http://localhost:7474/browser/?cmd=play&arg=movies[http://localhost:7474^].
-Note the command ready to run in the prompt (`:play movies`).
-Execute that command, and an interactive slidedeck will appear just below the command line.
-On the second slide of that guide, execute the long Cypher statement to fill your database with our movie test data.
-
-[#create-project]
-== Create a new Spring Boot project
-
-The easiest way to set up a Spring Boot project is with the Spring Initializr at https://start.spring.io[start.spring.io^].
-It is also integrated in the major IDEs, in case you prefer not to use the website.
-
-Then, you can change the default group, artifact, name, and description for the project.
-Next, we can choose our project dependencies.
-We can search for and add the `Neo4j` and `Spring Reactive Web` starter to get what we need to create a reactive, Spring-based web application.
-
-Once those steps are complete, we can click the `Generate` button at the bottom to create the skeleton for our project and download it.
-The Spring Initializr will take care of creating the project structure for you, with the basic files and settings in place for the selected build tool.
-
-=== Other dependencies
-
-If you are looking at the project in Github, you might notice that there are some other dependencies in the `pom.xml`.
-A couple are for adding tests to the project, then one dependency for developer tools, and a couple more for test containers.
-
-More information on the testing functionality can be found in the https://docs.spring.io/spring-data/neo4j/docs/current/reference/html/#sdn.testing[documentation^].
-
-.Testing and dev tools dependencies
-[source,xml,subs="verbatim,attributes"]
-----
-
-    org.springframework.boot
-    spring-boot-starter-test
-    test
-
-
-    io.projectreactor
-    reactor-test
-    test
-
-
-    org.testcontainers
-    junit-jupiter
-    {testcontainers-version}
-    test
-
-
-    org.testcontainers
-    neo4j
-    {testcontainers-version}
-    test
-
-----
-
-[#adding-config]
-== Adding configurations
-
-Now, we need to add a few configurations to connect to the database.
-We can find the `application.properties` file and configure what we need.
-
-[source,properties]
-----
-spring.neo4j.uri=neo4j+s://abcd.databases.neo4j.io
-spring.neo4j.authentication.username=neo4j
-spring.neo4j.authentication.password=secret
-----
-
-[NOTE]
---
-You will need to adjust the password to whatever you set when you created your instance of Neo4j.
---
-
-The first three lines are our Neo4j database URI and credentials.
-The username and password you enter here should match for your individual database.
-This is the bare minimum of what you need to connect to a Neo4j instance.
-
-We do not need to add any other configuration for the driver, thanks to the Spring Boot Driver autoconfiguration provided out of the box with SDN 6.
-
-=== Other configurations
-
-==== Logging
-
-There is also one additional property we could define.
-It is not a required property, but does allow us to see the Cypher statements and see better insight into what is running behind our application.
-
-[source,properties]
-----
-logging.level.org.springframework.data.neo4j=DEBUG
-----
-
-==== Database selection
-
-Since version 4.0, Neo4j is https://neo4j.com/developer/multi-tenancy-worked-example/[multi-tenant].
-We can statically select the database by providing a property:
-
-[source,properties]
-----
-spring.data.neo4j.database = my-database
-----
-
-For more advanced use cases, it is possible to perform a dynamic selection, as documented https://docs.spring.io/spring-data/neo4j/docs/current/reference/html/#faq.multidatabase.dynamically[here].
-
-[#create-domain]
-== Create the domain
-
-With our project dependencies defined and configurations set, we are ready to start defining our entities for our data domain!
-The domain layer should accomplish two things - 1. Map the graph to objects, 2. Provide access to those objects.
-
-Our data contains movie and person entities that show how people were involved in various films, such as who acted in, directed, wrote, produced, etc.
-We will need to define a domain class for each of our entities - `Movie` and `Person`.
-
-[NOTE]
---
-SDN supports all data types that the Neo4j Java Driver supports.
-To find out how to map Neo4j types to native language types, see link:/docs/java-manual/current/cypher-workflow/#driver-type-mapping[this section^] in the documentation.
---
-
-[#movie-entity]
-=== Movie entity
-
-[source,java]
-----
-@Node("Movie")
-public class MovieEntity {
-	@Id
-	private final String title;
-	@Property("tagline")
-	private final String description;
-	@Relationship(type = "ACTED_IN", direction = INCOMING)
-	private Set actors = new HashSet<>();
-	@Relationship(type = "DIRECTED", direction = INCOMING)
-	private Set directors = new HashSet<>();
-	public MovieEntity(String title, String description) {
-		this.title = title;
-		this.description = description;
-	}
-	//Getters omitted for brevity
-}
-----
-
-In the first line, the `@Node` annotation is used to mark the class as a managed entity.
-It also configures the Neo4j label, which defaults to the name of the class, but you can define a custom one, as well.
-
-The first couple of lines inside the class definition sets up the id field of the entity as the `title` attribute.
-The title is a unique business key in this domain, but if you don't have a unique key in another domain, you can use the combination of `@Id` and `@GeneratedValue` annotations on a field to generate a unique technical key.
-There are also generators provided for UUIDs.
-
-The two lines below those set up the `tagline` (or `description`) property.
-The `@Property` annotation is used as a way for mapping a different name for the field than for the graph property.
-This way, you can map differences between application entities and database domains.
-
-At the next annotation, the `@Relationship` defines a relationship between the movie and person entities with an `ACTED_IN` type for showing which persons acted in a particular movie.
-The two lines below that define another relationship between `MovieEntity` and `PersonEntity` for those who directed movies.
-
-Then, the next code block defines a constructor for the entity with the properties of the node (`title` and `description`).
-
-As mentioned above, you can use SDN with https://kotlinlang.org/[Kotlin^] and model your domain with Kotlin's data classes.
-https://projectlombok.org/[Project Lombok^] is also available to shortcut definitions and boilerplate, if you want or need to stay purely within Java.
-
-[#person-entity]
-=== Person entity
-
-[source,java]
-----
-@Node("Person")
-public class PersonEntity {
-	@Id
-	private final String name;
-	private final Integer born;
-	public PersonEntity(Integer born, String name) {
-		this.born = born;
-		this.name = name;
-	}
-    //Getters omitted
-}
-----
-
-This class for person entities looks very similar to our `MovieEntity` class above.
-The `@Node` annotation defines that it is a database domain entity.
-A unique key field is identified (in this case, the `name` property), and a `born` property is defined as another attribute on this class.
-The constructor for the class follows the properties.
-
-Notice that we have not defined the relationships from a person back to a movie.
-In our use case, we only want to retrieve movies and the people involved in them.
-Our application does not need us to pull information for person entities separately, so we do not need to define the relationships back in the other direction.
-
-[NOTE]
---
-If a domain needs to pull related entities on both sides, we would need to add the annotations and attributes from both sides.
---
-
-[#define-repository]
-== Define a Spring Data repository
-
-Our repositories in the application will extend a repository provided out-of-the-box called the `ReactiveNeo4jRepository`.
-
-[NOTE]
---
-If building an imperative application, you can extend the `Neo4jRepository`.
-Also, while technically not prohibited, it is not recommended or supported to mix imperative and reactive database access in the same application.
---
-
-Because our repositories are implementing reactive capabilities, we have access to the https://projectreactor.io/docs/core/release/reference/#mono[Mono^] and https://projectreactor.io/docs/core/release/reference/#flux[Flux^] reactive types from https://projectreactor.io/[Project Reactor^] for method returns.
-The `Mono` type returns 0 or 1 results, while the `Flux` returns 0 or n results.
-We would use a return type of `Mono` if we were expecting a single object back from the query and use a `Flux` type if we were expecting potentially multiple objects back from the query.
-
-[#movie-repository]
-=== Movie repository
-
-[source,java]
-----
-public interface MovieRepository extends ReactiveNeo4jRepository {
-	Mono findOneByTitle(String title);
-}
-----
-
-For our application, we need to interact with a Neo4j graph database, so we will create an interface that extends the repository for Neo4j.
-
-Since we want to use the reactive features for the application, we will extend the `ReactiveNeo4jRepository`, which provides reactive, Neo4j-specific implementation details on top of several extended Spring repositories.
-The ReactiveNeo4jRepository requires two types to be specified — our class type and its id type.
-Once we add our `MovieEntity` and `String` (our movie id field is the `title`) values here, we can start defining methods we want to use.
-
-Inside the interface definition, there is one method we will define for `findOneByTitle()`.
-This method will let us search the database based on a movie title, and we expect to see a single movie return or none at all for the movie we are interested in.
-
-To get that 0 or 1 return result, we can use the reactive return type of `Mono`.
-We will also pass a title (a String) to the method because we want to allow the user to enter any movie title as the search value.
-
-[#person-repository]
-=== Person repository
-
-While there is a `PersonRepository` interface in the Github code, it serves testing purposes for that application, so we will not go into detail on it here.
-More information on testing in SDN with this application is in the https://docs.spring.io/spring-data/neo4j/docs/current/reference/html/#sdn.testing[documentation^].
-
-However, it does demonstrate using a custom query and the `Flux` return type, so it may be of interest as an example or for a template for other applications.
-
-[#controller-setup]
-== Setting up the controllers
-
-With the repository, we have our methods for accessing movie data in our database.
-Let us now define endpoints allowing users to access those methods and query the database.
-
-The controller acts as the messenger between the data layer and the user interface to accept requests from the user and return responses.
-This is where the code logic and data manipulation is typically placed, coordinating different responses based on the kind of input it receives.
-
-Because our use case scope is interested in movies, we only need to create a controller to access movie data.
-
-[#movie-controller]
-=== MovieController.java
-
-[source,java]
-----
-@RestController
-@RequestMapping("/movies")
-public class MovieController {
-	private final MovieRepository movieRepository;
-	public MovieController(MovieRepository movieRepository) {
-		this.movieRepository = movieRepository;
-	}
-	//method implementations with walkthroughs below
-}
-----
-
-First, we need to have a couple of annotations to declare this as a controller for REST requests (`@RestController`) and map requests to controller methods for a certain path (`@RequestMapping` with an endpoint of `/movies`).
-
-Within our class definition, we start by injecting our repository interface and creating a constructor for it.
-This gives us access to the data layer from our repository interface and domain class.
-
-Now we need to add more code to define endpoints and implement our data methods.
-
-[source,java]
-----
-@PutMapping
-Mono createOrUpdateMovie(@RequestBody MovieEntity newMovie) {
-	return movieRepository.save(newMovie);
-}
-----
-
-Up first is the implementation for `createOrUpdateMovie()`.
-We start with a `@PutMapping` annotation to specify a put request (overwrite or replace an object).
-We want to specify a single movie to overwrite or create, so we use the return type of `Mono` and pass in the movie object with all of its expected fields.
-Within the method, we will save that new or updated movie by calling the movie repository's `save()` method.
-
-Now, if you scroll back up to our defined link:#movie-repository[`MovieRepository`] interface above, you may notice that we did not define a save method there.
-This is because Spring Data repositories provide a few default methods for us out-of-the-box.
-Methods for `save()`, `findAll()`, etc are methods that nearly every application wants or needs, so Spring provides them, and we do not have to implement those basic methods each time we create data access.
-
-Let us add another method to our controller for `getMovies()`.
-
-[source,java]
-----
-@GetMapping(value = { "", "/" }, produces = MediaType.TEXT_EVENT_STREAM_VALUE)
-Flux getMovies() {
-	return movieRepository.findAll();
-}
-----
-
-The `@GetMapping` annotation tells us we are only retrieving data from the database and not modifying or inserting.
-We have two parameters for the annotation, where we pass any additional depth on the url path (in this case, no additional depth - just `/movies`) and that we want to return a text event stream.
-This is our media type because we are expecting a `Flux` of results (0 to n amount), and we want to return those as they come in (reactive stream), rather than aggregating and returning all the results at once (imperative json object).
-Just like our previous method, we call the movie repository and access an out-of-the-box `findAll()` method to return all of the movies in our database.
-
-The next method is the one we defined in our `MovieRepository` interface.
-
-[source,java]
-----
-@GetMapping("/by-title")
-Mono byTitle(@RequestParam String title) {
-	return movieRepository.findOneByTitle(title);
-}
-----
-
-The starting `@GetMapping` specifies a subpath of `/by-title`.
-Since we are searching for a single movie where the user will input a title as the search string, we expect 0 or 1 result back with the type `Mono` and pass the user-defined parameter of the movie's title into the method.
-In the return, we call the movie repository again and access our defined `findOneByTitle()` method, passing in the search title.
-
-For the last method definition, we want to allow users to delete a movie from our database.
-
-[source,java]
-----
-@DeleteMapping("/\{id\}")
-Mono delete(@PathVariable String id) {
-	return movieRepository.deleteById(id);
-}
-----
-
-We use the `@DeleteMapping` annotation and specify the subpath endpoint as `/movies/\{id\}` (where id stands for the id of the movie we want to delete).
-We only want one movie to be deleted at a time, and we don't expect an object to return (since it will be deleted and no longer in the database), so we specify the `Mono` as the return type.
-The method is defined and passes in a path variable (where user input defines the url path) for the id of the movie to delete, then calls the movie repository with the out-of-the-box `deleteById()` method and the movie id.
-
-[#run-application]
-== Running the application
-
-With all of our code in place, we should be ready to build and run our application and try out the endpoints we set up!
-We can run the application (from a menu option in our IDE or from the command line) and then either open a web browser or command line to interact with the endpoints.
-For this example, we will show how to interact from the command line perspective.
-
-Either way you connect, we will use the `localhost:8080/movies` path to access the `findAll()` method and retrieve all movies in our database, and then add any defined subpaths to drill down into other methods.
-We can hit each of these endpoints shown below and verify everything is working as expected.
-
-=== Interacting from a command line
-
-Here is the syntax for each of the endpoints from a command line:
-
-* `localhost:8080/movies` for getMovies() method
-
-[source,bash]
-----
-curl http://localhost:8080/movies
-----
-
-Results: retrieve all movies in our database
-
-* `localhost:8080/movies ` for createOrUpdateMovie() method
-
-[source,bash]
-----
-curl -X "PUT" "http://localhost:8080/movies" \
-     -H 'Content-Type: application/json; charset=utf-8' \
-     -d $'{
-  "title": "Aeon Flux",
-  "description": "Reactive is the new cool"
-}'
-----
-
-Results: create new movie `Aeon Flux` in our database
-
-* `localhost:8080/movies/by-title` for byTitle() method
-
-[source,bash]
-----
-curl http://localhost:8080/movies/by-title\?title\=Aeon%20Flux
-----
-
-Results: retrieve information about the specific movie (in this query, `Aeon Flux`)
-
-* `localhost:8080/movies/\{id\}` for delete() method
-
-[source,bash]
-----
-curl -X DELETE http://localhost:8080/movies/847
-----
-
-Results: delete the movie using its id (in this case, the `Aeon Flux` movie)
-
-[#sdn-resources]
-== Resources
-
-[cols="1,4"]
-|===
-| icon:code-fork[] Projects | https://spring.io/projects/spring-data-neo4j/[Spring Data Neo4j^]
-| icon:github[] Source | https://github.com/spring-projects/spring-data-neo4j
-| icon:medkit[] Issues | https://github.com/spring-projects/spring-data-neo4j/issues[GitHub Issues^]
-| icon:book[] Docs | https://docs.spring.io/spring-data/neo4j/docs/current/reference/html/[Reference^], http://docs.spring.io/spring-data/data-neo4j/docs/current/api/[JavaDoc^], https://docs.spring.io/spring-data/data-neo4j/docs/current/changelog.txt[ChangeLog^]
-| icon:book[] Articles | https://medium.com/neo4j/spring-data-neo4j-6-0-8b92164fff32[Introducing SDN 6^]
-| icon:play-circle[] Examples | https://github.com/spring-projects/spring-data-neo4j[SDN Example from Spring^], {examples}/movies-java-spring-data-neo4j[Movies Application with SDN^], {examples}/sdn-migration[Migration Example from SDN 5/OGM to SDN 6^]
-|===
diff --git a/modules/ROOT/pages/swift.adoc b/modules/ROOT/pages/swift.adoc
deleted file mode 100644
index a3601fab..00000000
--- a/modules/ROOT/pages/swift.adoc
+++ /dev/null
@@ -1,123 +0,0 @@
-= Using Neo4j from Swift
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:programming-language: swift
-:category: drivers
-:tags: swift, app-development, applications
-:description: If you are an iOS, macOS or Linux developer using Swift, this guide provides instructions for how to use the https://github.com/GraphStory/neo4j-ios[Theo^] framework to interact with Neo4j.
-
-.Goals
-[abstract]
-{description}
-
-include::./_includes/common-language-prereq.adoc[]
-
-[role=expertise {level}]
-{level}
-
-include::./_includes/community-driver-notice.adoc[]
-
-[#neo4j-swift]
-== Neo4j for Swift Developers
-
-image::{img}swift-logo.png[float=right,width=300]
-
-Swift is a great language for writing iOS and macOS apps, and with the introduction of Swift 3.0, it has become a great language for writing server side applications as well. All of which are excellent places to use Neo4j.
-
-[#theo-lib]
-=== Theo
-
-https://github.com/GraphStory/neo4j-ios[Theo^], the Swift framework for interfacing Neo4j, is now available in it’s version 3.0.
-
-With this version, Theo gets full support for Swift 3, Linux support, and better integration with popular package managers. This on top of features you already expect, like CRUD operations for nodes and relationships, cypher support and transaction support.
-
-Apart from reading the detailed instructions, you can take a look at our https://github.com/niklassaers/theo-example[example project^] for how to use Theo in your iOS project.
-
-Theo is available for iOS, macOS and Linux, using package managers such as the https://github.com/GraphStory/neo4j-ios#swift-package-manager[Swift Package Manager^], https://github.com/GraphStory/neo4j-ios#cococapods[CocoaPods^] and https://github.com/GraphStory/neo4j-ios#carthage[Carthage^], as well as a https://github.com/GraphStory/neo4j-ios#git-submodule[git submodule^], regardless if your app is written in Objective-C or Swift.
-
-You can also easily integrate it in server side swift project on macOS or Linux as a git submodule or the Swift Package Manager. If you are new to server side swift development, we recommend you try Theo out together with http://qutheory.io[Vapor^]. Other suggested web frameworks you can integrate Theo with are http://www.kitura.io[Kitura^] and http://perfect.org[Perfect^].
-
-Theo has been available since Swift 1.2, and has followed the Swift versions since then. For the future we also plan to stay on top of the latest Swift versions. Theo interfaces Neo4j via the REST protocol, support for Bolt is absolutely a priority on our roadmap ahead.
-
-We look forward to hearing what you create using Theo and Neo4j. Feel free to ask any developer questions on the https://community.neo4j.com[Neo4j Online Community^].
-
-[cols="1,5"]
-|===
-| icon:github[] Source                | https://github.com/GraphStory/neo4j-ios
-| icon:book[] Docs                    | https://github.com/GraphStory/neo4j-ios#usage
-| icon:play-circle[] Example          | https://github.com/niklassaers/theo-example
-| icon:user[] Authors                 | http://twitter.com/kwylez[Cory Wiles^], http://twitter.com/niklassaers[Niklas Saers^]
-|                                     | https://community.neo4j.com[Neo4j Online Community^]
-| icon:book[] Blog Post               | https://neo4j.com/blog/theo-3-0-swift-framework-neo4j/[^]
-|===
-
-[#theo-starter]
-=== Getting started with Theo
-After installing https://github.com/GraphStory/neo4j-ios[Theo^] in your project, you are ready to
-
-[source, swift]
-----
-import Theo
-----
-
-Then you can instantiate your client like so
-
-[source, swift]
-----
-let theo: Client = Client(baseURL: "hostname.com", user: "username", pass: "password")
-----
-
-create a node
-
-[source, swift]
-----
-let node = Node()
-let randomString: String = UUID().uuidString
-
-node.setProp("propertyKey_1", propertyValue: "propertyValue_1" + randomString)
-node.setProp("propertyKey_2", propertyValue: "propertyValue_2" + randomString)
-
-theo.createNode(node, completionBlock: { (node, error) in
-    print("new node \(node)")
-})
-----
-
-fetch a node
-
-[source, swift]
-----
-theo.fetchNode("IDToFetch", completionBlock: { (node, error) in
-    print("meta in success \(node!.meta) node \(node) error \(error)")
-})
-----
-
-do a cypher query
-
-[source, swift]
-----
-let cypherQuery: String = "MATCH (node1:Label1)-->(node2:Label2) WHERE node1.propertyA = {value} RETURN node2.propertyA, node2.propertyB"
-let cypherParams: Dictionary = ["value" : "someValue"]
-
-theo.executeCypher(cypherQuery, params: cypherParams, completionBlock: { (cypher, error) in
-    println("response from cypher \(cypher)")
-})
-----
-
-or a transaction
-
-[source, swift]
-----
-let createStatement: String = "CREATE ( bike:Bike { weight: 10 } ) CREATE ( frontWheel:Wheel { spokes: 3 } ) CREATE ( backWheel:Wheel { spokes: 32 } ) CREATE p1 = bike -[:HAS { position: 1 } ]-> frontWheel CREATE p2 = bike -[:HAS { position: 2 } ]-> backWheel RETURN bike, p1, p2"
-let resultDataContents: Array = ["row", "graph"]
-let statement: Dictionary  = ["statement" : createStatement, "resultDataContents" : resultDataContents]
-let statements: Array> = [statement]
-
-theo.executeTransaction(statements, completionBlock: {(response, error) in
-    print("response \(response) and error \(error?.description")
-})
-----
-
-As you can see, it is very much how you would expect Swift code to read, and it integrates with Neo4j very much how you would expect a Neo4j integration to be. So no hard learning curves, meaning you can start being productive right away.
-
-Happy coding!
diff --git a/modules/ROOT/pages/tools-graph-visualization.adoc b/modules/ROOT/pages/tools-graph-visualization.adoc
deleted file mode 100644
index 12177bf8..00000000
--- a/modules/ROOT/pages/tools-graph-visualization.adoc
+++ /dev/null
@@ -1,343 +0,0 @@
-= Graph Visualization Tools
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: visualization
-:tags: visualization, tools, neovis-js, d3-js, graphxr, yfiles, linkurious
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/graph-visualization/graph-visualization/#neo4j-vis-tools
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-image::{img}graph_cluster_distant_wide.jpg[]
-
-
-[role=expertise {level}]
-{level}
-[#bloom]
-
-:cta-header: Neo4j Bloom is now available for free in AuraDB!
-
-[#graph-vis-types]
-== Types of Graph Visualization
-
-There are 3 architectural categories into which most of our graph visualization tools fall.
-We will discuss how each of these categories handles the exported data and provide some pros and cons of the different architectures.
-Depending on the visualization needs, one of these categories may define the set of tools you can choose to implement as a solution to your business needs.
-
-[#embed-graph-vis]
-=== 1. Embeddable tools with built-in Neo4j connections
-
-These kinds of tools can be included as a dependency within an application and can easily be configured and styled for your application and Neo4j.
-Each is easily connected to an instance of the graph database using configuration properties and allows you to style the visualization based on nodes, relationships, or specific properties.
-
-Embedding the visualization within the application allows the developer to create applications that include the visualization as part of the user interface.
-This also means that the developer can write other components and customize the application experience and other components involved in the application to the exact business requirements.
-
-On the downside, these libraries don’t often support extremely complex or heavy workloads and do not have vendor support or SLAs for functionality requests.
-Because they are managed by the community, the tools depend on the community for support and feature improvements.
-Also, this typically means that our client application is connecting directly to the database, which might not always be the desired architecture.
-
-Let us look at some of the tools in this category.
-
-==== icon:flask[] *Neovis.js*
-This library was designed to combine JavaScript visualization and Neo4j in a seamless integration.
-Connection to Neo4j is simple and straightforward, and because it is built with Neo4j’s property graph model in mind, the data format Neovis expects aligns with the database.
-Customizing and coloring styles based on labels, properties, nodes, and relationships is defined in a single configuration object.
-Neovis.js can be used without writing Cypher and with minimal JavaScript for integrating into your project.
-
-====
-[TIP]
-icon:flask[size=2x] The Neovis library is one of our Neo4j Labs projects.
-To learn more about Neo4j Labs, visit our https://neo4j.com/labs/[Labs page^].
-====
-
-To maximize functionality and data analysis capabilities through visualization, you can also combine this library with the graph algorithms library in Neo4j to style the visualization to align with results of algorithms such as page rank, centrality, communities, and more.
-Below, we see a graph visualization of Game Of Thrones character interactions rendered by neovis.js, and enhanced using Neo4j graph algorithms by applying link:/docs/graph-algorithms/current/algorithms/page-rank/[pagerank^] and link:/docs/graph-algorithms/current/algorithms/community/[community detection^] algorithms to the styling of the visualization.
-
-image:{img}example-viz.png[role="popup-link"]
-
-An advantage of enhancing graph visualization with these algorithms is that we can visually interpret the results of these algorithms.
-
-===== Neovis.js Resources
-* Blog post: https://medium.com/neo4j/graph-visualization-with-neo4j-using-neovis-js-a2ecaaa7c379[Neovis.js^]
-* Download neovis.js: https://www.npmjs.com/package/neovis.js[npm package^]
-
-==== Popoto.js
-Popoto.js is a JavaScript library that is built upon D3.js.
-Popoto.js will help users build queries in a visual way to execute against Neo4j.
-Users can also customize the results and the visual display.
-Along with the visualization, you can include auto-complete searches for potential queries, see the Cypher translations that are generated from the visualization, review text results from queries, and more.
-
-To use Popoto.js in your application, you simply need to include each component independently bound to a container id in an HTML page.
-The rest of the content will be generated from that.
-
-===== Popoto.js Resources
-* Documentation: https://github.com/Nhogs/popoto/wiki[Popoto.js^]
-* Website: http://www.popotojs.com/[popoto.js^]
-
-[#embed-lib-vis]
-=== 2. Embeddable libraries without direct Neo4j connection
-
-These libraries offer the ability to embed graph visualization in an application, but without connecting directly to Neo4j.
-An advantage here is that we can populate our visualization with data sent from an API application that connects to the database, ensuring the client application is not querying the database directly.
-The downside, however, is that we often must transform the results to export from Neo4j into the format expected by these libraries.
-
-We can get a closer look at these tools in the next paragraphs.
-
-==== D3.js
-As the first line on D3’s website states "D3.js is a JavaScript library for manipulating documents based on data."
-You can bind different kinds of data to a DOM and then execute different kinds of functions on it.
-One of those functions includes generating an SVG, canvas, or HTML visualization from the data in the DOM.
-
-image::{img}d3_visualization.jpg[role="popup-link",float="right",width=350]
-
-Neo4j’s movie example applications use d3.js, and you can find a variety of other projects using Neo4j and d3.
-The complicated part of D3 (or any embeddable library that doesn’t have direct Neo4j connection) is converting your graph data into the expected map format for export.
-D3 expects two different collections of graph data - one for nodes[] and one for links[] (relationships).
-Each of these maps includes arrays of properties for each node and relationship that d3 then converts into circles and lines.
-Version 4 and 5 of d3.js also support force-directed graphs, where the visualization adjusts to the user’s view pane.
-
-===== D3.js Resources
-* Website: https://d3js.org/[D3.js^]
-* D3 and graphs example: http://thinkingonthinking.com/Getting-Started-With-D3/[D3 Examples^]
-* Neo4j Github examples with d3: https://github.com/neo4j-examples?utf8=%E2%9C%93&q=movie&type=&language=[Examples with Neo4j^]
-
-==== *Vis.js*
-This library offers a variety of visualizations designed to handle large, dynamic data sets.
-There are a variety of formats to style your data, including timeline, dataset, graph2d, graph3d, and network.
-The most common format seen with Neo4j is the network visualization.
-
-Even with the network format, there are numerous customizations available for styling nodes, labels, animations, coloring, grouping, and others.
-For additional information and to see everything that is available, check out their docs and examples linked in the resources below.
-
-===== Vis.js Resources
-* Vis.js website: http://visjs.org/[Vis.js^]
-* Network format examples: http://visjs.org/network_examples.html[Format Examples^]
-* Source code project: https://github.com/almende/vis[Vis.js Github^]
-
-==== Sigma.js
-While some libraries are meant to include all the capabilities in one bundle, Sigma.js touts a highly-extensible environment where users can add extension libraries or plugins to provide additional capability.
-This library takes exported data in either https://github.com/jacomyal/sigma.js/tree/master/plugins/sigma.parsers.json[JSON^] or https://github.com/jacomyal/sigma.js/tree/master/plugins/sigma.parsers.gexf[GEXF^] formats.
-
-image::{img}sigmajs_visualization.jpg[role="popup-link",float="right",width=350]
-
-Users can start from a very basic visualization right out of the box, and then begin adding custom functions and rendering for styling preferences.
-Once the requirements surpass what is possible there, users can write and use their own custom plugins for specific functionality.
-Be sure to check out the repository, though, for any existing extensions!
-
-===== Sigma.js Resources
-* Website: http://sigmajs.org/[Sigma.js^]
-* Source code: https://github.com/jacomyal/sigma.js/[Sigma.js Github^]
-* Blog post: https://medium.com/neo4j/how-to-use-sigmajs-to-display-your-graph-3eedd75275bb[Sigma.js+Neo4j^]
-
-==== Vivagraph.js
-Vivagraph.js was built to handle different types of layout algorithms for arranging nodes and edges.
-It manages data set sizes from very small to very large and also renders in WebGL, SVG, and CSS-based formats.
-Customizations and styling are available through CSS modifications and extension libraries.
-It also can track changes in the graph that update the visualization accordingly.
-
-===== Vivagraph.js Resources
-* Source code: https://github.com/anvaka/VivaGraphJS[Vivagraph.js Github^]
-* Blog post: https://maxdemarzi.com/2013/05/29/visualizing-the-news-with-vivagraph-js/[Viavgraph.js+Neo4j^]
-
-==== Cytoscape.js
-This library is also meant to visualize and render network node graphs and offers customization and extensibility for additional features.
-Cytoscape.js responds to user interaction and works on touch screen interfaces, allowing users to zoom, tap, and explore in the method that is relevant to them.
-You can customize styling and web page view with a variety of style components.
-
-===== Cytoscape.js Resources
-* Website: http://js.cytoscape.org/[Cytoscape.js^]
-* Source code: https://github.com/cytoscape/cytoscape.js[Cytoscape.js Github^]
-
-[#neo4j-vis-vendors]
-=== 3. Standalone product tools
-
-Certain tools and products are designed as standalone applications that can connect to Neo4j and interact with the stored data without involving any code.
-These applications are built with non-developers in mind - for business analysts, data scientists, managers, and other users to interact with Neo4j in a node-graph format.
-
-Many of these tools involve commercial licenses and support but can be configured specifically to your use case and custom requirements.
-They also require little or no developer integration hours and setup.
-
-The next paragraphs will help us get a feel for the types of products in this area.
-
-==== *Bloom* by
-image:{img}neo4j_logo.png[width=200]
-
-image::{img}bloom_screen.jpg[role="popup-link",float="right",width=350]
-
-Neo4j Bloom is a data exploration tool that visualizes data in the graph and allows users to navigate and query the data without any query language or programming.
-
-Users can write patterns similar to natural language questions to retrieve data and traverse layers of the graph. Bloom also allows appropriate users to edit, update, or correct the graph when missing information or bad data is found.
-
-Bloom is available in the following formats:
-
-* Neo4j Bloom local with users accessing Bloom via Neo4j Desktop (free for local database instances)
-* Neo4j Bloom server with users accessing Bloom via a web browser
-* Neo4j Bloom through the https://sandbox.neo4j.com/?usecase=bloom&ref=developer-vis-tools[sandbox^]
-* Neo4j Bloom through Neo4j Database as a Service, link:/aura/[AuraDB^]
-* Included in link:/startup-program/[Neo4j Startup Program^]
-
-===== Bloom Resources
-* Developer Guide: https://neo4j.com/blog/graphxr-graph-app-neo4j-desktop/[Neo4j Bloom User Interface Guide^]
-* Blog post: https://medium.com/neo4j/bloom-ing-marvellous-a2be0c3702bb[Bloom-ing marvellous! Introducing Bloom 1.3^]
-* Product information: https://neo4j.com/bloom/[Neo4j Bloom landing page^]
-
-==== *NeoDash* by
-image:labs-logo.jpg[width=200]
-
-image::https://neo4j.com/labs/neodash/_images/neodash.png[role="popup-link",float="right",width=350]
-
-NeoDash is an open-source, low-code Dashboard Builder for Neo4j. As a part of https://neo4j.com/labs[Neo4j Labs], NeoDash is developed and supported via the online https://community.neo4j.com[Community]. 
-
-NeoDash lets you build an interactive dashboard with tables, graphs, bar charts, line charts, maps and more.
-Dashboards can be saved and shared directly from your Neo4j database.
-
-* A low-code dashboard builder with a drag-and-drop interface
-* Create visualizations directly from Cypher
-* The ability to add customization and interactivity to dashboards
-* Build and publish dashboards for read-only access
-
-===== NeoDash Resources
-* User Guide: https://neo4j.com/labs/neodash/2.1/user-guide/[NeoDash User Guide^]
-* Blog Post: https://neo4j.com/developer-blog/neodash-2-0-a-brand-new-way-to-visualize-neo4j/[NeoDash 2.0 – A Brand New Way to Visualize Neo4j^]
-* Try NeoDash: http://neodash.graphapp.io/[NeoDash Online Demo^]
-
-==== *GraphXR* by
-image:{img}kineviz-logo.png[width=200]
-
-image::{img}kineviz_visualization.jpg[role="popup-link",float="right",width=350]
-
-GraphXR is a start-to-finish web-based visualization platform for interactive analytics.
-For technical users, it's a highly flexible and extensible environment for conducting ad hoc analysis.
-For business users, it's an intuitive tool for code-free investigation and insight.
-
-* Collect data from Neo4j, SQL dbs, CSVs, and Json.
-* Cleanse and enrich with built-in tools as well as API calls.
-* Analyze links, properties, time series, and spatial data within a unified, animated context.
-* Save back to Neo4j, output as a report, or embed in your webpage.
-
-GraphXR supports a wide range of applications including law enforcement, medical research, and knowledge management.
-
-Kineviz also has a graph app version of this tool that can be installed in Neo4j Desktop.
-The blog post about the graph app is included in the resources below.
-
-===== GraphXR Resources
-* Blog post: https://neo4j.com/blog/graphxr-graph-app-neo4j-desktop/[Adding GraphXR as a Graph App in Neo4j Desktop^]
-* Blog post: https://neo4j.com/blog/evaluating-investor-performance-using-neo4j-graphxr-and-ml/[Evaluating Investor Performance Using Neo4j, GraphXR and MLl^]
-* Product information: https://static1.squarespace.com/static/5c58b86e8dfc8c2d0d700050/t/5c6f46559140b7665401785b/1550796373803/GraphXR%2BDatasheet.pdf[GraphXR Datasheet^]
-
-==== *yFiles* by
-image:{img}yWorks.png[width=200]
-
-image::{img}yfiles-neo.jpg[role="popup-link",float="right",width=350]
-
-yWorks provides sophisticated solutions for the visualization of graphs, diagrams, and networks with yFiles, a family of high-quality, commercial software programming libraries.
-The yFiles libraries enable you to easily create sophisticated graph-based applications powered by Neo4j.
-They support the widest range of desktop and web technologies and layout algorithms with the highest quality and performance.
-With the wide-ranging extensibility and large feature set, all your visualization needs can be satisfied.
-
-yWorks also provides a free graph explorer app that is based on the yFiles technology.
-It can be installed in Neo4j Desktop.
-
-===== yFiles Resources
-* Blog post: https://www.yworks.com/blog/neo4j-Custom-Visualization-Solutions[Custom Visualization Solutions with yFiles and Neo4j^]
-* Blog post: https://www.yworks.com/blog/neo4j-visualization-like-a-pro[Visualizing Neo4j Database Content Like a Pro^]
-* Webinar: https://www.youtube.com/watch?v=uDZD3tOTrFc[Technical intro to yFiles with Neo4j^]
-* Product information: https://www.yworks.com/products/yfiles[yFiles Visualization Libraries^]
-
-==== *Linkurious Enterprise* by
-image:{img}Linkurious_logo_large.png[width=200]
-
-image::{img}linkurious_vis_Apr2019.png[role="popup-link",float="right",width=350]
-
-Linkurious Enterprise is an on-premises and browser-based platform that works on top of graph databases.
-It brings graph visualization and analysis capabilities to analysts tasked to detect and analyze threats in large volumes of connected data.
-Organizations such as the French Ministry of Economy and Finance, Zurich Insurance or Bank of Montreal use Linkurious Enterprise to fight financial crime, terror networks or cyber threats.
-
-===== Linkurious Resources
-* Blog post: https://linkurio.us/blog/panama-papers-how-linkurious-enables-icij-to-investigate-the-massive-mossack-fonseca-leaks/[Panama Papers Discovery with Neo4j and Linkurious^]
-* Blog post: https://linkurio.us/blog/stolen-credit-cards-and-fraud-detection-with-neo4j/[Fraud detection with Neo4j and Linkurious^]
-* Blog post: https://neo4j.com/blog/detect-investigate-financial-crime-patterns-linkurious/[Detect and Investigate Financial Crime with Neo4j and Linkurious^]
-* Webinar: https://www.youtube.com/watch?v=SM8JlhFbi1s[How to visualize Neo4j with Linkurious^]
-* Solution: https://linkurio.us/solution/neo4j/[Linkurious Enterprise + Neo4j^]
-* Product datasheet https://linkurio.us/wp-content/uploads/2019/04/Linkurious_Enterprise_Technical_Datasheet.pdf[Linkurious Enterprise^]
-
-==== *Graphistry* by
-image:{img}graphistry-logo-rough.png[width=200]
-
-image::{img}graphistry_vis.jpg[role="popup-link",float="right",width=350]
-
-Graphistry brings a human interface to the age of big and complex data.
-It automatically transforms your data into interactive, visual investigation maps built for the needs of analysts.
-Quickly surface relationships between events and entities without writing queries or wrangling data.
-Harness all of your data without worrying about scale, and pivot on the fly to follow anywhere your investigation leads you.
-
-Ideal for everything from security, fraud, and IT investigations to 3600 views of customers and supply chains, Graphistry turns the potential of your data into human insight and value.
-
-===== Graphistry Resources
-* Source code: https://github.com/graphistry[Graphistry on Github^]
-* Product information: https://www.graphistry.com/[Graphistry graph visualization^]
-
-==== *Graphlytic* by
-image:{img}graphlytic_logo.png[width=200]
-
-image::{img}graphlytic_vis.png[role="popup-link",float="right",width=400]
-
-Graphlytic is a highly customizable web application for graph visualization and analysis. Users can interactively explore the graph, look for patterns with the Cypher language, or use filters to find answers to any graph question. Graph rendering is done with the Cytoscape.js library which allows Graphlytic to render tens of thousands of nodes and hundreds of thousands of relationships. 
-
-The application is provided in three ways: Desktop, Cloud, and Server. Graphlytic Desktop is a free Neo4j Desktop application installed in just a few clicks. Cloud instances are ideal for small teams that need them get up and running in very little time. Graphlytic Server is used by corporations and agencies with highly sensitive data typically in closed networks.
-
-===== Graphlytic Resources
-* Product webpage: https://graphlytic.biz[https://graphlytic.biz^]
-* Online Demo: https://graphlytic.biz/demo[Graphlytic Demo^]
-* Free Desktop Installation: https://graphlytic.biz/blog/how-to-install-graphlytic-in-neo4j-desktop[How To Install And Use Graphlytic In Neo4j Desktop^]
-* Features: https://graphlytic.biz/features[Graphlytic Feature Clips^]
-* Blog post: https://graphlytic.biz/blog/parallel-relationships-models[Parallel Relationship Models with Graphlytic^]
-
-==== *Perspectives* by
-image:{img}tom-sawyer-logo.png[width=200]
-
-Tom Sawyer Perspectives is a robust platform for building enterprise-class graph and data visualization and analysis applications.
-It is a complete graph visualization software development kit (SDK) with a graphics-based design and preview environment.
-The platform integrates enterprise data sources with the powerful graph visualization, layout, and analysis technology to solve big data problems.
-Enterprises, system integrators, technology companies, and government agencies use Tom Sawyer Perspectives to build a wide range of applications.
-
-===== Perspectives Resources
-* Product information: https://www.tomsawyer.com/perspectives/[Perspectives graph visualization^]
-
-==== *Keylines* by
-image:{img}Cambridge-Intelligence-logo.jpg[width=200]
-
-KeyLines makes it easy to build and deploy high-performance network visualization tools quickly.
-Every aspect of your application can be tailored to suit you, your data and the questions you need to answer.
-KeyLines applications work on any device and in all common browsers, to reach everyone who needs to use them.
-It is also compatible with any IT environment, letting you deploy your network visualization application to an unlimited number of diverse users.
-You can build a custom application that is scalable and easy to use.
-
-===== Keylines Resources
-* Product information: https://cambridge-intelligence.com/keylines/[Keylines graph visualization^]
-
-==== *Semspect* by derivo
-image:https://dist.neo4j.com/wp-content/uploads/20220517002751/semspect-neo4j-viz.png[width=200]
-
-image::https://dist.neo4j.com/wp-content/uploads/20220517003656/SemSpect-for-Neo4j-Viz.png[role="popup-link",float="right",width=400]
-
-SemSpect is a highly scalable knowledge graph exploration tool that uses visual aggregation to solve the hairball problem faced by standard graph visualization approaches.
-The data guided construction of the exploration tree empowers the users to build complex requests intuitively without query syntax. 
-Its meta level approach is very effective for grasping the overall structure of the graph data, while flexible access to node and relationship details ensures easy inspection and filtering.
-SemSpect furthermore allows to define query-based node labels during exploration to refine the graph data schema.
-
-SemSpect is available as follows:
-
-*	SemSpect as Graph App for Neo4j Desktop (free for local database instances)
-*	SemSpect as Web App for Neo4j database servers
-
-===== Semspect Resources
-* Product information: https://doc.semspect.de/docs/neo4j-graph-app[SemSpect for Neo4j^]
-* Blog post: https://neo4j.com/blog/semspect-different-approach-graph-visualization/[A Different Approach to Graph Visualization^]
-
-== Visualization Resources
-* Blog series: https://medium.com/neo4j/tagged/data-visualization[Neo4j Visualization^]
-* Blog: https://maxdemarzi.com/?s=visualization[Max de Marzi on Visualization with Neo4j^]
-* Neo4j Visualiation: https://www.youtube.com/channel/UCvze3hU6OZBkB1vkhH2lH9Q/search?query=visualization[YouTube videos^]
diff --git a/modules/building-applications/pages/using-drivers.adoc b/modules/building-applications/pages/using-drivers.adoc
deleted file mode 100644
index 096b1f03..00000000
--- a/modules/building-applications/pages/using-drivers.adoc
+++ /dev/null
@@ -1,495 +0,0 @@
-[[building-applications]]
-= Building Applications on Neo4j
-:author: Adam Cowley
-:description: In the Building Applications section we discuss how to build an application on Neo4j.
-:uri: bolt://localhost:7687
-:username: neo4j
-:password: letmein
-:database: northwind
-
-{description}
-The guides contained in this section will explain how an application can be built on top of Neo4j using the official driver.
-
-For more detailed information on each language you can visit the Developer Guide: +
-xref:ROOT:java.adoc[Java] |
-xref:ROOT:dotnet.adoc[.NET] |
-xref:ROOT:javascript.adoc[JavaScript] |
-xref:ROOT:python.adoc[Python] |
-xref:ROOT:go.adoc[Go,title="Go Programming Language"]
-
-
-In the 3.0 release of Neo4j, a set of Official Drivers were released with the aim of standardising the way that applications are built on top of Neo4j.
-Aside from a few language specific variations the process of connecting to Neo4j and querying the data using Cypher should be similar.
-The process, regardless of language will be as follows:
-
-1. xref:driver[Create an instance of the Driver]
-2. xref:session[Opening a Session]
-3. xref:transaction[Start a Transaction]
-4. xref:cypher[Running Cypher Queries]
-5. xref:close[Closing the Driver]
-
-
-
-
-[#driver]
-== Instantiating the Driver
-
-All interaction with Neo4j will take place using a Driver, communicating with a Neo4j Server of cluster using the Bolt Protocol.
-
-You should create a single instance of the driver which can then be shared across the application.
-The Driver is a thread-safe object.
-
-// The code within your application will remain the same whether you are using Neo4j Community Edition or Enterprise Edition, and will be the same regardless of whether you are connecting to a single instance or a xref:ROOT:guide-clustering-neo4j[Causal Cluster].
-
-
-The driver accepts two arguments:
-
-* A connection string which represents the scheme, URI of the server and port number
-* An authentication method
-// — by default this will be a *basic* authentication token but Neo4j also supports link:https://neo4j.com/docs/add-on/kerberos/current/[Kerberos tokens^]
-
-
-=== Connection Strings
-
-Regardless of whether you are connecting to a single instance of Neo4j or a link:/docs/operations-manual/current/clustering/[Causal Cluster] your application code will remain the same - the only difference will be the connection string.
-
-A connection string consists of a **scheme** and the URI and port of the Neo4j instance.  For example, by default a connection to a Neo4j Desktop instance will be:
-
-[source,text,role=noheader]
-neo4j://localhost:7687
-
-==== Scheme
-
-The following table should help you to to choose the appropriate scheme to prepend to your query string.
-
-// This allows you to switch between development, test and production environments easily using configuration files or environment variables.
-
-include::ROOT:page$_includes/driver-warning.adoc[tags=table]
-
-==== URI
-
-For single instances of Neo4j the address should be the IP address or domain name of the Neo4j instance.
-For Causal Clusters you can provide the address of any member of the cluster.
-
-
-=== Auth Tokens
-
-If you are connecting to Neo4j using native link:https://neo4j.com/docs/operations-manual/current/authentication-authorization/[Users and Roles^] or using LDAP, you will connect using a **Basic** auth token.
-This token accepts a username (or principal) and password (or credentials).
-
-If you are using Kerberos, the auth token accepts a single parameter representing the Base 64 encoded token.
-
-[TIP]
-link:https://neo4j.com/aura/[Neo4j AuraDB] users can find their connection string in the link:https://console.neo4j.io[Neo4j AuraDB Console].
-
-=== Basic Driver Example
-
-[.tabs]
-[source,Java]
-----
-import org.neo4j.driver.GraphDatabase;
-import org.neo4j.driver.AuthTokens;
-
-Driver driver = GraphDatabase.driver( uri, AuthTokens.basic( user, password ) );
-----
-
-.JavaScript (ES5)
-[source,javascript]
-----
-const neo4j = require('neo4j-driver')
-
-const driver = neo4j.driver( uri, neo4j.auth.basic( username, password ) )
-----
-
-.JavaScript (ES6)
-[source,javascript]
-----
-import neo4j from 'neo4j-driver'
-
-const driver = neo4j.driver( uri, neo4j.auth.basic( username, password ) )
-----
-
-[source,Python]
-----
-from neo4j import GraphDatabase
-
-driver = GraphDatabase.driver(uri, auth=(user, password))
-----
-
-[source,DotNet]
-----
-using Neo4j.Driver.Internal;
-
-driver = GraphDatabase.Driver(uri, AuthTokens.Basic(user, password));
-----
-
-[source,Go]
-----
-import (
-    "github.com/neo4j/neo4j-go-driver/neo4j"
-)
-
-neo4j.NewDriver(uri, neo4j.BasicAuth("user", "password", ""))
-----
-
-
-=== Verifying the connection
-
-To verify the connection details passed to the driver you can call the `.verifyConnectivity()` method.
-If the server address or credentials are incorrect, an error or exception will be thrown.
-
-[source,Java]
-----
-driver.verifyConnectivity()
-----
-
-[source,JavaScript]
-----
-driver.verifyConnectivity()
-----
-
-[source,Python]
-----
-driver.verifyConnectivity()
-----
-
-[source,DotNet]
-----
-driver.verifyConnectivity()
-----
-
-[source,Go]
-----
-driver.verifyConnectivity()
-----
-
-[#session]
-== Opening a Session
-
-From the driver you can open up many sessions.
-Sessions are lightweight objects that can be opened and closed freely.
-
-
-[.tabs]
-[source,Java]
-----
-try ( Session session = driver.session() ) {
-  // ...
-}
-----
-
-[source,JavaScript]
-----
-const session = driver.session()
-----
-
-[source,Python]
-----
-// ...
-----
-
-[source,DotNet]
-----
-// ...
-----
-
-[source,Go]
-----
-// ...
-----
-
-
-Sessions can also be used provide additional context to any queries run within it, for example the Default Access Mode or which database the query should be executed against.
-
-
-=== Default Access Mode
-
-The Default Access Mode defines the way in which the session should interact with the database.
-When the driver is connected to a Causal Cluster all write queries will be directed to the **leader** of the cluster while read queries will be distributed across the cluster.
-
-[.tabs]
-[source,Java]
-----
-Session writeSession = driver.session( builder().withDefaultAccessMode( AccessMode.WRITE ).build() )
-----
-
-[source,JavaScript]
-----
-const writeSession = driver.session({
-  defaultAccessMode: neo4j.session.WRITE
-})
-----
-
-[source,Python]
-----
-// ...
-----
-
-[source,DotNet]
-----
-// ...
-----
-
-[source,Go]
-----
-// ...
-----
-
-If you do not specify a Default Access Mode it will default to a `READ` session.
-
-=== Database
-
-Since version 4.0, Neo4j has supported xref:ROOT:manage-multiple-databases[multiple databases].
-In order to query a specific database, this must be defined when creating a new session.
-Otherwise the query will be executed on the default database (set to `neo4j` by default and defined in neo4j.conf as `dbms.default_database`).
-
-[.tabs]
-[source,Java]
-----
-SessionConfig sessionConfig = SessionConfig.builder()
-        .withDatabase( "northwind" )
-        .withDefaultAccessMode( AccessMode.READ )
-        .build();
-try ( Session session = driver.session( sessionConfig ) )
-{
-  // ...
-}
-----
-
-[source,JavaScript]
-----
-const session = driver.session({
-  database: 'northwind',
-  defaultAccessMode: neo4j.session.READ
-})
-----
-
-[source,Python]
-----
-// ...
-----
-
-[source,DotNet]
-----
-// ...
-----
-
-[source,Go]
-----
-// ...
-----
-
-You must create a new session for each database when using multiple databases.
-
-
-== Creating a Transaction
-
-Neo4j is an link:https://neo4j.com/top-ten-reasons/[ACID Compliant Database] which values data integrity above all else.
-As such all interactions with Neo4j take place within the context of a **Transaction**.
-// A Transaction is a single, atomic, consistent, isolated and durable unit of work.
-Transactions allow you to complete an entire unit of work, committing once all statements have succeeded, or performing roll back should anything go wrong, leaving the database in it’s original state.
-
-Running multiple queries within the same transaction will ensure that the data you receive is not modified by any other processes.
-This will also reduce the number of transactions are committed to the database, each of which have an overhead.
-
-There are three types of transaction.
-
-
-=== Auto-commit Transactions
-
-Auto-commit transactions are run directly against the session.
-Within the session a new transaction is opened, the query executed and the transaction committed.
-If Neo4j encounters any errors while processing this query, the transaction will be automatically rolled back, leaving the database in it's initial state.
-
-[.tabs]
-[source,Java]
-----
-String cypher = "CREATE (t:Test {id: $id}) RETURN t";
-Map params = new HashMap<>();
-map.put("id", 1)
-
-Result res = session.run(cypher, params);
-Node node = res.single().get("t");
-----
-
-[source,JavaScript]
-----
-const cypher = "CREATE (t:Test {id: $id}) RETURN t";
-const params = { id: neo4j.int(1) };
-
-session.run(cypher, params)
-  .then(result => {
-    const node = res.records[0].get('t')
-  })
-----
-
-[source,Python]
-----
-// ...
-----
-
-[source,DotNet]
-----
-// ...
-----
-
-[source,Go]
-----
-// ...
-----
-
-
-[CAUTION]
-These transactions are useful for running small samples or simple queries but can struggle at high volumes.
-It is recommended that you create a specific read or write transaction when building a production-ready application.
-
-// The Default Access Mode of the session becomes important when using Auto-commit transactions.
-// Attempting to run a query that writes data against a Causal Cluster within a `READ` transaction will cause Neo4j to throw an error.
-
-
-=== Read Transactions
-
-If you only wish to read data from Neo4j, you can open a read transaction.
-In a clustered environment the driver will distribute read queries across the cluster.
-
-
-[TIP]
-Opening a specific read or write transaction will override the Default Access Mode set when initialising the query.
-
-
-
-[.tabs]
-[source,Java]
-----
-Transaction readTx = session.readTransaction()
-----
-
-[source,JavaScript]
-----
-const readTx = session.beginTransaction()
-----
-
-[source,Python]
-----
-// ...
-----
-
-[source,DotNet]
-----
-// ...
-----
-
-[source,Go]
-----
-// ...
-----
-
-
-=== Write Transactions
-
-When writing data to Neo4j you should open a write transaction.
-In a clustered environment the driver will direct all write queries to the leader of the cluster, which then becomes responsible for committing the transaction and distributing the changes to the other cluster members.
-
-[.tabs]
-[source,Java]
-----
-Transaction writeTx = session.writeTransaction()
-----
-
-[source,JavaScript]
-----
-// Open the session
-const writeTx = session.writeTransaction()
-----
-
-[source,Python]
-----
-// ...
-----
-
-[source,DotNet]
-----
-// ...
-----
-
-[source,Go]
-----
-// ...
-----
-
-Only use write transactions when writing data to Neo4j.  Otherwise this will cause unnecessary load on the leader of the cluster and lead to performance problems.
-
-
-[#cypher]
-== Running a Query
-
-Regardless of whether you are executing an auto-commit transaction or running the query within a transaction you will call the .`run()` method.
-The `run` method takes two arguments; the cypher query and a map of parameters, each of which are referenced inside the query with a `$`.
-
-[.tabs]
-[source,Java]
-----
-String cypher = "CREATE (t:Test {id: $id}) RETURN t";
-Map params = new HashMap<>();
-map.put("id", 1)
-
-// Auto-commit transaction
-Result res = session.run(cypher, params)
-
-// Within a Transaction
-Result res = tx.run(cypher, params)
-----
-
-[source,JavaScript]
-----
-const cypher = "CREATE (t:Test {id: $id}) RETURN t";
-const params = { id: neo4j.int(1) };
-
-// Auto-commit transaction
-session.run(cypher, params)
-  .then(res => /* ... */)
-
-// Within a Transaction
-tx.run(cypher, params)
-  .then(res => /* ... */)
-----
-
-[source,Python]
-----
-// ...
-----
-
-[source,DotNet]
-----
-// ...
-----
-
-[source,Go]
-----
-// ...
-----
-
-
-[#close]
-== Closing the Session and Driver
-
-Once you have finished with the session you can call the `.close()` method to release any connections currently held by the session.
-There is no overhead to closing a session.
-
-[source]
-----
-session.close()
-----
-
-
-
-
-== Closing the Driver
-
-Calling the `close()` method on the driver will shut down the driver and release any resources currently being consumed by the driver.
-You should only do this when
-
-[source]
-----
-driver.close()
-----
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--- a/modules/cypher/pages/controlling-query-processing.adoc
+++ /dev/null
@@ -1,401 +0,0 @@
-= Controlling Query Processing
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: cypher
-:tags: cypher, queries, aggregation, chaining, loops, order-by, distinct, limit
-:description: Building on the previous Cypher guides, this guide shows how to handle returning results in various ways, aggregating data, and using Cypher functions. Upon finishing this guide, you should be able to write and understand queries using these capabilities.
-:page-pagination:
-:page-aliases: ROOT:aggregation-returns-functions.adoc
-:page-newsletter: true
-:page-ad-overline-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-overline: Neo4j GraphAcademy
-:page-ad-title: Cypher Fundamentals
-:page-ad-description: Learn Cypher in this free, hands-on course
-:page-ad-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-underline-role: button
-:page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/results/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with xref:ROOT:get-started.adoc[graph database] concepts and the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-This guide is a continuation of the concepts discussed in the previous Cypher sections.
-You should be familiar with xref:index.adoc[MATCH], xref:updating.adoc[Create/Update/Delete], and xref:filtering-query-results.adoc[Filtering] concepts before walking through this guide.
-
-[role=expertise {level}]
-{level}
-
-[#cypher-example]
-== Recap: Our Example Graph
-
-All of our examples will continue with the graph example we have been using in the previous guides.
-Below is an image of the graph to familiarize yourself with the nodes and relationships used throughout this page.
-
-image::{img}cypher_graph_v2.jpg[role="popup-link"]
-
-[#cypher-aggregate]
-== Aggregation in Cypher
-
-Helpful aggregation operations, such as calculating averages, sums, percentiles, minimum/maximum, and counts are available in Cypher.
-You may find many of these have similar syntax to other query language operations, but Cypher does work slightly differently with aggregation.
-
-In Cypher, you do not need to specify a grouping key.
-It implicitly groups by a non-aggregate field in the return clause.
-This might seem much easier than more verbose syntax in other languages, but opinions may vary.
-
-[#aggregate-count]
-=== Aggregating by Count
-
-Sometimes you only need to return a count of the results found in the database, rather than returning the objects themselves.
-The `count()` function in Cypher allows you to count the number of occurences of entities, relationships, or results returned.
-
-There are two different ways you can count return results from your query.
-The first is by using `count(n)` to count the number of occurences of `n` and does not include `null` values.
-You can specify nodes, relationships, or properties within the parentheses for Cypher to count.
-The second way to count results is with `count({empty}*)`, which counts the number of result rows returned (including those with `null` values).
-
-In our data set, some of our `Person` nodes have a Twitter handle, but others do not.
-If we run the first example query below, you will see that we have the `twitter` property has a value for four people and is `null` for the other five people.
-The second and third queries show how to use the different count options.
-
-[source, cypher]
-----
-//Query1: see the list of Twitter handle values for Person nodes
-MATCH (p:Person)
-RETURN p.twitter;
-----
-
-.Query1 Results:
-image:{img}cypher_agg_count.jpg[role="popup-link"]
-
-[source, cypher]
-----
-//Query2: count of the non-null `twitter` property of the Person nodes
-MATCH (p:Person)
-RETURN count(p.twitter);
-----
-
-.Query2 Results:
-image:{img}cypher_agg_count_prop.jpg[role="popup-link"]
-
-[source, cypher]
-----
-//Query3: count on the Person nodes
-MATCH (p:Person)
-RETURN count(*);
-----
-
-.Query3 Results:
-image:{img}cypher_agg_count_results.jpg[role="popup-link"]
-
-[#aggregate-collect]
-=== Aggregating Values
-
-The `collect()` function in Cypher gives you the capability to aggregate values into a list.
-You can use this to group a set of values based on a particular starting node, relationship, property.
-
-For instance, if we listed each person in our example data with each of their friends (see the Cypher below), you would see duplicate names in the left column because each `Person` might have multiple friends, and you need a result for each relationship from the starting person.
-To aggregate all of a person's friends by the starting person, you can use `collect()`.
-This will group the friend values by the non-aggregate field (in our case, `p.name`).
-
-[source, cypher]
-----
-MATCH (p:Person)-[:IS_FRIENDS_WITH]->(friend:Person)
-RETURN p.name, collect(friend.name) AS friend
-----
-
-image::{img}cypher_agg_collect.jpg[role="popup-link"]
-
-[#aggregate-size]
-=== Counting Values in a List
-
-If you have a list of values, you can also find the number of items in that list or calculate the size of an expression using the `size()` function.
-The examples below return the number of items or patterns found.
-
-[source, cypher]
-----
-//Query5: find number of items in collected list
-MATCH (p:Person)-[:IS_FRIENDS_WITH]->(friend:Person)
-RETURN p.name, size(collect(friend.name)) AS numberOfFriends;
-----
-
-.Query5 Results:
-image:{img}cypher_agg_size.jpg[role="popup-link"]
-
-[source, cypher]
-----
-//Query6: find number of friends who have other friends
-MATCH (p:Person)-[:IS_FRIENDS_WITH]->(friend:Person)
-WHERE size((friend)-[:IS_FRIENDS_WITH]-(:Person)) > 1
-RETURN p.name, collect(friend.name) AS friends, size((friend)-[:IS_FRIENDS_WITH]-(:Person)) AS numberOfFoFs;
-----
-
-.Query6 Results:
-image:{img}cypher_agg_sizePath.jpg[role="popup-link"]
-
-[#cypher-results-output]
-== Manipulating Results and Output
-
-Results from a query may only show part of the answer you were looking for in the data or may not be in the best format for easily viewing and understanding.
-This is where capabilities to link multiple queries together or to sort or limit the output can help you avoid sifting through the results manually to find value.
-
-In the next few paragraphs, we will show how to use certain clauses and keywords to help you write queries to fully answer your data questions and format the results in a way that is simple and quickly valuable.
-
-[#results-with]
-=== Chaining Queries Together
-
-The syntax for the queries above might look a bit intimidating, but there are better ways to write it.
-One of those ways is to use the `WITH` clause to pass values from one section of a query to another.
-This allows you to execute some intermediate calculations or operations within your query to use later.
-
-You must specify the variables in the `WITH` clause that you want to use later.
-Only those variables will be passed to the next part of the query.
-There are a variety of ways to use this functionality (e.g. count, collect, filter, limit results), but we will show a couple, including a cleaner version of our `size()` query from above.
-For more information and ways to use `WITH`, check out the link:https://neo4j.com/docs/cypher-manual/current/clauses/with/[Cypher Manual section^].
-
-[source, cypher]
-----
-//Query7: find and list the technologies people like
-MATCH (a:Person)-[r:LIKES]-(t:Technology)
-WITH a.name AS name, collect(t.type) AS technologies
-RETURN name, technologies;
-----
-
-.Query7 Results:
-image:{img}cypher_results_with.jpg[role="popup-link"]
-
-[source, cypher]
-----
-//Query8: find number of friends who have other friends - cleaner Query6
-MATCH (p:Person)-[:IS_FRIENDS_WITH]->(friend:Person)
-WITH p, collect(friend.name) AS friendsList, size((friend)-[:IS_FRIENDS_WITH]-(:Person)) AS numberOfFoFs
-WHERE numberOfFoFs > 1
-RETURN p.name, friendsList, numberOfFoFs;
-----
-
-.Query8 Results:
-image:{img}cypher_results_with_filter.jpg[role="popup-link"]
-
-In the first query, we pass the `Person` name, and a collected list of the `Technology` types.
-Only these items can be referenced in the `RETURN` clause.
-We cannot use the relationship (`r`) or even the `Person` birthdate because we did not pass those values along.
-
-In the second query, we can only reference `p` and any of its properties (name, birthdate, yrsExperience, twitter), the collection of friends (as a whole, not each value), and the number of friend-of-friends.
-Since we passed those values in the `WITH` clause, we can use those in our `WHERE` or `RETURN` clauses.
-
-`WITH` requires all values passed to have a variable (if they do not already have one).
-Our `Person` nodes were given a variable (`p`) in the `MATCH` clause, so we do not need to assign a variable there.
-
-[NOTE]
---
-`WITH` is also very helpful for setting up parameters before the query.
-Often useful for parameter keys, url strings, and other query variables when importing data.
-
-[source,cypher]
-----
-//find people with 2-6 years of experience
-WITH 2 AS experienceMin, 6 AS experienceMax
-MATCH (p:Person)
-WHERE experienceMin <= p.yrsExperience <= experienceMax
-RETURN p
-----
---
-
-[#results-unwind]
-=== Looping through List Values
-
-If you have a list that you want to inspect or separate the values, Cypher offers the `UNWIND` clause.
-This does the opposite of `collect()` and separates a list into individual values on separate rows.
-
-Using `UNWIND` is frequently used for looping through JSON and XML objects when importing data, as well as everyday arrays and other types of lists.
-Let us look at a couple of examples where we assume that the technologies someone likes also mean they have some experience with each one.
-We are interested in hiring people who are familiar with `Graphs` or `Query Languages`, so we can write a query to find people to interview.
-
-[source, cypher]
-----
-//Query9: for a list of techRequirements, look for people who have each skill
-WITH ['Graphs','Query Languages'] AS techRequirements
-UNWIND techRequirements AS technology
-MATCH (p:Person)-[r:LIKES]-(t:Technology {type: technology})
-RETURN t.type, collect(p.name) AS potentialCandidates;
-----
-
-.Query9 Results:
-image:{img}cypher_results_unwind_strList.jpg[role="popup-link"]
-
-[source, cypher]
-----
-//Query10: for numbers in a list, find candidates who have that many years of experience
-WITH [4, 5, 6, 7] AS experienceRange
-UNWIND experienceRange AS number
-MATCH (p:Person)
-WHERE p.yearsExp = number
-RETURN p.name, p.yearsExp;
-----
-
-.Query10 Results:
-image:{img}cypher_results_unwind_numList.jpg[role="popup-link"]
-
-[#results-ordering]
-=== Ordering Results
-
-Our list of potential hiring candidates from our last example might be more useful if we could order the candidates by most or least experience.
-Or perhaps we want to rank all of our people by age.
-
-The `ORDER BY` keyword will sort the results based on the value you specify and in ascending or descending order (ascending is default).
-Let's use the same queries from our example with `UNWIND` and see how we can order our candidates.
-
-[source, cypher]
-----
-//Query11: for a list of techRequirements, look for people who have each skill - ordered Query9
-WITH ['Graphs','Query Languages'] AS techRequirements
-UNWIND techRequirements AS technology
-MATCH (p:Person)-[r:LIKES]-(t:Technology {type: technology})
-WITH t.type AS technology, p.name AS personName
-ORDER BY technology, personName
-RETURN technology, collect(personName) AS potentialCandidates;
-----
-
-.Query11 Results:
-image:{img}cypher_results_order_names.jpg[role="popup-link"]
-
-[source, cypher]
-----
-//Query12: for numbers in a list, find candidates who have that many years of experience - ordered Query10
-WITH [4, 5, 6, 7] AS experienceRange
-UNWIND experienceRange AS number
-MATCH (p:Person)
-WHERE p.yearsExp = number
-RETURN p.name, p.yearsExp ORDER BY p.yearsExp DESC;
-----
-
-.Query12 Results:
-image:{img}cypher_results_order_experience.jpg[role="popup-link"]
-
-Notice that our first query has to order by `Person` name before collecting the values into a list.
-If you do not sort first (put the `ORDER BY` after the `RETURN`), you will sort based on the size of the list and not by the first letter of the values in the list.
-We also sort on two values - technology, then person.
-This allows us to sort our technology so that all the persons that like a technology are listed together.
-
-You can try out the difference in sorting by both values or one value by running these queries:
-
-[source,cypher]
-----
-//only sorted by person's name in alphabetical order
-WITH ['Graphs','Query Languages'] AS techRequirements
-UNWIND techRequirements AS technology
-MATCH (p:Person)-[r:LIKES]-(t:Technology {type: technology})
-WITH t.type AS technology, p.name AS personName
-ORDER BY personName
-RETURN technology, personName;
-----
-
-[source,cypher]
-----
-//only sorted by technology (person names are out of order)
-WITH ['Graphs','Query Languages'] AS techRequirements
-UNWIND techRequirements AS technology
-MATCH (p:Person)-[r:LIKES]-(t:Technology {type: technology})
-WITH t.type AS technology, p.name AS personName
-ORDER BY technology
-RETURN technology, personName;
-----
-
-[source,cypher]
-----
-//sorted by technology, then by person's name
-WITH ['Graphs','Query Languages'] AS techRequirements
-UNWIND techRequirements AS technology
-MATCH (p:Person)-[r:LIKES]-(t:Technology {type: technology})
-WITH t.type AS technology, p.name AS personName
-ORDER BY technology, personName
-RETURN technology, personName;
-----
-
-[#results-distinct]
-=== Returning Unique Results
-
-Over the last couple of guides, there have been a few queries that have returned duplicate results due to multiple paths to the node or a node met multiple criteria.
-This redundancy can clutter results and make sifting through a long list difficult to find what you need.
-
-To trim out duplicate entities, we can use the `DISTINCT` keyword.
-We will use past examples from queries, as well as a query from a previous page to show how to use this to remove repetitive results.
-
-[source, cypher]
-----
-//Query13: find people who have a twitter or like graphs or query languages
-MATCH (user:Person)
-WHERE user.twitter IS NOT null
-WITH user
-MATCH (user)-[:LIKES]-(t:Technology)
-WHERE t.type IN ['Graphs','Query Languages']
-RETURN DISTINCT user.name
-----
-
-.Query13 Results:
-image:{img}cypher_results_distinct_user.jpg[role="popup-link"]
-
-For Query13, our use case is that we are launching a new Twitter account for tips and tricks on Cypher, and we want to notify users who have a Twitter account and who like graphs or query languages.
-The first two lines of the query look for `Person` nodes who have a Twitter handle.
-Then, we use `WITH` to pass those users over to the next `MATCH`, where we find out if the person likes graphs or query languages.
-Notice that running this statement without the `DISTINCT` keyword will result in "Melissa" shown twice.
-This is because she likes graphs, and she also likes query languages.
-When we use `DISTINCT`, we only retrieve unique users.
-
-[#results-limit]
-=== Limiting Number of Results
-
-There are times where you want a sampling set or you only want to pull so many results to update or process at a time.
-The `LIMIT` keyword takes the output of the query and limits the volume returned based on the number you specify.
-
-For instance, we can find each person's number of friends in our graph.
-If our graph were thousands or millions of nodes and relationships, the number of results returned would be massive.
-What if we only cared about the top 3 people who had the most friends?
-Let's write a query for that!
-
-[source, cypher]
-----
-//Query14: find the top 3 people who have the most friends
-MATCH (p:Person)-[r:IS_FRIENDS_WITH]-(other:Person)
-RETURN p.name, count(other.name) AS numberOfFriends
-ORDER BY numberOfFriends DESC
-LIMIT 3
-----
-
-image::{img}cypher_results_limit.jpg[role="popup-link"]
-
-Our query pulls persons and the friends they are connected to and returns the person name and count of their friends.
-We could run just that much of the query and return a messy list of names and friend counts, but we probably want to order the list based on the number of friends each person has starting with the biggest number at the top (`DESC`).
-You could also run that much of the query to see the friends and counts all in order, but we only want to pull the top 3 people with the most friends.
-The `LIMIT` pulls the top results from our ordered list.
-
-[TIP]
---
-Try mixing up the query by removing the `ORDER BY` and `LIMIT` lines and then add each one separately.
-Notice that only removing the `ORDER BY` line pulls the starting 3 values from the list, getting a random sampling of the return results.
---
-
-[#cypher-next-steps]
-=== Next Steps
-
-This guide has shown how to do more with Cypher by combining clauses and keywords for aggregating and returning data.
-We have seen how to use functions in Cypher and some of the operations offered.
-In the next section, we will learn how to maintain data integrity by using constraints and increase query performance with indexes.
-
-[#cypher-resources]
-=== Resources
-
-* link:/docs/cypher-manual/current/clauses/[Neo4j Cypher Manual: WITH, UNWIND, & More^]
-* link:/docs/cypher-manual/current/functions/aggregating/[Neo4j Cypher Manual: Aggregation^]
-* link:/docs/cypher-manual/current/functions/scalar/#functions-size[Neo4j Cypher Manual: Size()^]
-* link:https://graphacademy.neo4j.com/courses/cypher-aggregation/[GraphAcademy Cypher Aggregations course^]
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/courses.adoc b/modules/cypher/pages/courses.adoc
deleted file mode 100644
index d263f03c..00000000
--- a/modules/cypher/pages/courses.adoc
+++ /dev/null
@@ -1,37 +0,0 @@
-// obsolete
-= Courses
-:category: cypher
-:tags: cypher, queries, graph-queries, resources, documentation, cypher-training, cypher-help, training
-:page-pagination: previous
-:page-newsletter: true
-
-== Courses
-
-Neo4j has a number of courses to teach you how to use Neo4j. Whether you are a developer, administrator, or data scientist, you will need to understand the basics of Neo4j and Cypher.
-
-We have several learning paths that you can follow:
-
-*Beginner Learning Path:*
-
-* https://graphacademy.neo4j.com/courses/neo4j-fundamentals/[Neo4j Fundamentals^]
-* https://graphacademy.neo4j.com/courses/cypher-fundamentals/[Cypher Fundamentals^]
-* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^]
-* https://graphacademy.neo4j.com/courses/importing-data/[Importing CSV Data into Neo4j^]
-
-*Cypher Learning Path:*
-
-* https://graphacademy.neo4j.com/courses/cypher-intermediate-queries/[Cypher Intermediate Queries^]
-
-*Developer Learning Path:*
-
-* https://graphacademy.neo4j.com/courses/app-nodejs/[Building Neo4j Applications with Node.js^]
-* https://graphacademy.neo4j.com/courses/app-python/[Building Neo4j Applications with Python^]
-* https://graphacademy.neo4j.com/courses/app-java/[Building Neo4j Applications with Java^]
-* https://graphacademy.neo4j.com/courses/app-dotnet/[Building Neo4j Applications with .NET^]
-* https://graphacademy.neo4j.com/courses/app-go/[Building Neo4j Applications with Go^]
-
-*Data Scientist Learning Path:*
-
-* https://graphacademy.neo4j.com/courses/gds-product-introduction/[Introduction to Neo4j Graph Data Science^]
-* https://graphacademy.neo4j.com/courses/graph-data-science-fundamentals/[Neo4j Graph Data Science Fundamentals^]
-
diff --git a/modules/cypher/pages/dates-datetimes-durations.adoc b/modules/cypher/pages/dates-datetimes-durations.adoc
deleted file mode 100644
index 564aa366..00000000
--- a/modules/cypher/pages/dates-datetimes-durations.adoc
+++ /dev/null
@@ -1,303 +0,0 @@
-= Dates, datetimes, and durations
-:level: Intermediate
-:page-level: Intermediate
-:author: Jennifer Reif
-:neo4j-versions: 3.4, 3.5, 4.0
-:category: cypher
-:tags: cypher, queries, graph-queries, dates, times, compare-dates, calculate-duration
-:description: Building on the Cypher Basics guides, this guide covers the temporal date types introduced in Neo4j 3.4. Upon finishing this guide, you should be able to create, compare, and format these values.
-:page-pagination:
-:page-aliases: ROOT:dates-datetimes-durations.adoc
-:page-newsletter: true
-:page-ad-overline-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-overline: Neo4j GraphAcademy
-:page-ad-title: Cypher Fundamentals
-:page-ad-description: Learn Cypher in this free, hands-on course
-:page-ad-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-underline-role: button
-:page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/dates-datetimes-durations/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with xref:ROOT:get-started.adoc[graph database] concepts and the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-This guide is a continuation of the concepts discussed in the previous Cypher sections.
-You should be familiar with xref:index.adoc[MATCH], xref:updating.adoc[Create/Update/Delete], and xref:filtering-query-results.adoc[Filtering] concepts before walking through this guide.
-
-[role=expertise {level}]
-{level}
-
-[#creating-updating-values]
-== Creating and updating values
-
-Let's start by creating some nodes that have a Datetime property.
-We can do this by executing the following Cypher query:
-
-[source, cypher]
-----
-UNWIND [
-    { title: "Cypher Basics I",
-      created: datetime("2019-06-01T18:40:32.142+0100"),
-      datePublished: date("2019-06-01"),
-      readingTime: {minutes: 2, seconds: 15} },
-    { title: "Cypher Basics II",
-      created: datetime("2019-06-02T10:23:32.122+0100"),
-      datePublished: date("2019-06-02"),
-      readingTime: {minutes: 2, seconds: 30} },
-    { title: "Dates, Datetimes, and Durations in Neo4j",
-      created: datetime(),
-      datePublished: date(),
-      readingTime: {minutes: 3, seconds: 30} }
-] AS articleProperties
-
-CREATE (article:Article {title: articleProperties.title})
-SET article.created = articleProperties.created,
-    article.datePublished = articleProperties.datePublished,
-    article.readingTime = duration(articleProperties.readingTime)
-----
-
-In this query:
-
-* the `created` property is a `DateTime` type equal to the datetime at the time the query is executed.
-* the `date` property is a `Date` type equal to the date at the time the query is executed.
-* the `readingTime` is a `Duration` type of 3 minutes 30 seconds.
-
-Maybe we want to make some changes to this article node to update the `datePublished` and `readingTime` properties.
-
-We've decided to publish the article next week rather than today, so we want to make that change.
-If we want to create a new `Date` type using a https://neo4j.com/docs/cypher-manual/current/syntax/temporal/#cypher-temporal-specify-date[supported format^], we could do so using the following query:
-
-[source,cypher]
-----
-MATCH (article:Article {title: "Dates, Datetimes, and Durations in Neo4j"})
-SET article.datePublished = date("2019-09-30")
-----
-
-But what if we want to create a `Date` type https://neo4j.com/developer/kb/neo4j-string-to-date/[based on an unsupported format^]?
-To do this we'll use a function from the https://neo4j.com/developer/neo4j-apoc/[APOC library^] to parse the string.
-
-The following query parses an unsupported data format into a millisecond based timestamp, creates a `Datetime` from that timestamp, and then creates a `Date` from that `Datetime`:
-
-[source,cypher]
-----
-WITH apoc.date.parse("Sun, 29 September 2019", "ms", "EEE, dd MMMM yyyy") AS ms
-MATCH (article:Article {title: "Dates, Datetimes, and Durations in Neo4j"})
-SET article.datePublished = date(datetime({epochmillis: ms}))
-----
-
-We could use this same approach to update the `created` property.
-The only thing we need to change is that we don't need to convert the `Datetime` type to a `Date`:
-
-
-[source,cypher]
-----
-WITH apoc.date.parse("25 September 2019 06:29:39", "ms", "dd MMMM yyyy HH:mm:ss") AS ms
-MATCH (article:Article {title: "Dates, Datetimes, and Durations in Neo4j"})
-SET article.created = datetime({epochmillis: ms})
-----
-
-Perhaps we also decide that the reading time is actually going to be one minute more than what we originally thought.
-We can update the `readingTime` property with the following query:
-
-[source,cypher]
-----
-MATCH (article:Article {title: "Dates, Datetimes, and Durations in Neo4j"})
-SET article.readingTime =  article.readingTime + duration({minutes: 1})
-----
-
-
-[#formatting-values]
-== Formatting values
-
-Now we want to write a query to return our article.
-We can do this by executing the following query:
-
-[source,cypher]
-----
-MATCH (article:Article)
-RETURN article.title AS title,
-       article.created AS created,
-       article.datePublished AS datePublished,
-       article.readingTime AS readingTime
-----
-
-.Results
-[opts="header",cols="3,2,1,1"]
-|===
-| title | created | datePublished | readingTime
-| "Dates, Datetimes, and Durations in Neo4j" | 2019-09-25T06:29:39Z | 2019-09-29    | P0M0DT270S
-|===
-
-If we want to format these values we can use https://neo4j.com/labs/apoc/4.3/overview/apoc.temporal/[temporal functions^] in the APOC library.
-The following query formats each of the temporal types into more friendly formats:
-
-[source,cypher]
-----
-MATCH (article:Article)
-RETURN article.title AS title,
-       apoc.temporal.format(article.created, "dd MMMM yyyy HH:mm") AS created,
-       apoc.temporal.format(article.datePublished,"dd MMMM yyyy") AS datePublished,
-       apoc.temporal.format(article.readingTime, "mm:ss") AS readingTime
-----
-
-.Results
-[opts="header",cols="3,2,2,1"]
-|===
-| title | created | datePublished | readingTime
-| "Dates, Datetimes, and Durations in Neo4j" | "25 September 2019 06:29" | "29 September 2019" | "04:30"
-|===
-
-[#comparing-filtering-values]
-== Comparing and filtering values
-
-What if we want to filter our articles based on these temporal values.
-
-Let's start by finding the articles that were published on 1st June 2019.
-The following query does this:
-
-[source,cypher]
-----
-MATCH (article:Article)
-WHERE article.datePublished = date({year: 2019, month: 6, day: 1})
-RETURN article.title AS title,
-       article.created AS created,
-       article.datePublished AS datePublished,
-       article.readingTime AS readingTime
-----
-
-.Results
-[opts="header",cols="3,2,1,1"]
-|===
-| title | created | datePublished | readingTime
-| "Cypher Basics I" | 2019-06-01T18:40:32.142+01:00 | 2019-06-01    | P0M0DT135S
-|===
-
-What about if we want to find all the articles published in June 2019?
-We might write the following query to do this:
-
-[source,cypher]
-----
-MATCH (article:Article)
-WHERE article.datePublished = date({year: 2019, month: 6})
-RETURN article.title AS title,
-       article.created AS created,
-       article.datePublished AS datePublished,
-       article.readingTime AS readingTime
-----
-
-If we run this query we'll get the following results:
-
-.Results
-[opts="header",cols="3,2,1,1"]
-|===
-| title | created | datePublished | readingTime
-| "Cypher Basics I" | 2019-06-01T18:40:32.142+01:00 | 2019-06-01    | P0M0DT135S
-|===
-
-This doesn't seem right - what about the `Cypher Basics II` article that was published on 2nd June 2019?
-The problem we have here is that `date({year: 2019, month:6})` returns `2019-06-01`, so we're only finding articles published on 1st June 2019.
-
-We need to tweak our query to find articles published between June 1st 2019 and July 1st 2019.
-The following query does this:
-
-[source,cypher]
-----
-MATCH (article:Article)
-WHERE date({year: 2019, month: 7}) > article.datePublished >= date({year: 2019, month: 6})
-RETURN article.title AS title,
-       article.created AS created,
-       article.datePublished AS datePublished,
-       article.readingTime AS readingTime
-----
-
-.Results
-[opts="header",cols="3,2,1,1"]
-|===
-| title | created | datePublished | readingTime
-| "Cypher Basics I"  | 2019-06-01T18:40:32.142+01:00 | 2019-06-01    | P0M0DT135S
-| "Cypher Basics II" | 2019-06-02T10:23:32.122+01:00 | 2019-06-02    | P0M0DT150S
-|===
-
-What about if we want to filter based on the `created` property, which stores `Datetime` values?
-We need to take the same approach when filtering `Datetime` values as we did with `Date` values.
-The following query finds the articles created after July 2019:
-
-[source,cypher]
-----
-MATCH (article:Article)
-WHERE article.created > datetime({year: 2019, month: 7})
-RETURN article.title AS title,
-       article.created AS created,
-       article.datePublished AS datePublished,
-       article.readingTime AS readingTime
-----
-
-.Results
-[opts="header",cols="3,2,1,1"]
-|===
-| title | created | datePublished | readingTime
-| "Dates, Datetimes, and Durations in Neo4j" | 2019-09-25T06:04:39.072Z | 2019-09-25    | P0M0DT210S
-|===
-
-And finally filtering durations.
-We might be interested in finding articles that can be read in 3 minutes or less.
-
-We'll start with the following query:
-
-[source,cypher]
-----
-MATCH (article:Article)
-WHERE article.readingTime <= duration("PT3M")
-RETURN article.title AS title,
-       article.created AS created,
-       article.datePublished AS datePublished,
-       article.readingTime AS readingTime
-----
-
-However, that query results in the following output: _no changes, no records_.
-
-If we want to compare durations, we need to do that comparison by adding those durations to dates. 
-We don’t really care about dates for our query so we’ll just use the current time to work around this issue.
-We can get the current time by calling the datetime() function.
-
-Our updated query reads like this:
-
-[source,cypher]
-----
-MATCH (article:Article)
-WHERE datetime() + article.readingTime <= datetime() + duration("PT3M")
-RETURN article.title AS title,
-       article.created AS created,
-       article.datePublished AS datePublished,
-       article.readingTime AS readingTime
-----
-
-.Results
-[opts="header",cols="3,2,2,1"]
-|===
-| title | created | datePublished | readingTime
-| "Cypher Basics I"  | "01 June 2019 18:40" | "01 June 2019" | "02:15"
-| "Cypher Basics II" | "02 June 2019 10:23" | "02 June 2019" | "02:30"
-|===
-
-
-[#cypher-resources]
-== Resources
-
-This guide has shown how to work more effectively with temporal types using the APOC libary.
-Below are some resources for learning more about using Temporal types in Neo4j:
-
-* link:https://neo4j.com/docs/cypher-manual/current/syntax/temporal[Temporal Datatypes^]
-* link:https://neo4j.com/developer/kb/neo4j-string-to-date/[Knowledge Base: Converting strings to dates^]
-* link:https://neo4j.com/developer/neo4j-apoc/[APOC Library^]
-** link:https://neo4j.com/labs/apoc/4.3/overview/apoc.date/apoc.date.format/[Date parsing^]
-** link:https://neo4j.com/labs/apoc/4.3/overview/apoc.temporal/[Temporal Functions^]
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/filtering-query-results.adoc b/modules/cypher/pages/filtering-query-results.adoc
deleted file mode 100644
index 6b8b5d39..00000000
--- a/modules/cypher/pages/filtering-query-results.adoc
+++ /dev/null
@@ -1,310 +0,0 @@
-= Filtering Query Results
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: cypher
-:tags: cypher, queries, graph-queries, filtering, where, value-ranges, existence, optional-patterns
-:description: Building on the Cypher Basics I and II guides, this guide continues with additional concepts of Cypher for filtering and query criteria. Upon finishing this guide, you should be able to filter results on properties and patterns for ranges and other expanded searches.
-:page-pagination:
-:page-aliases: ROOT:filtering-query-results.adoc
-:page-newsletter: true
-:page-ad-overline-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-overline: Neo4j GraphAcademy
-:page-ad-title: Cypher Fundamentals
-:page-ad-description: Learn Cypher in this free, hands-on course
-:page-ad-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-underline-role: button
-:page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/results/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with xref:ROOT:get-started.adoc[graph database] concepts and the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-This guide is a continuation of the xref:index.adoc[Cypher Syntax] and xref:updating.adoc[Read and Write Operations] sections.
-You should be familiar with those concepts before walking through this guide.
-
-[role=expertise {level}]
-{level}
-
-[#cypher-filtering]
-== Filtering Queries
-
-The Cypher you have written and learned so far has only tested properties with specific values.
-It has operated on the fact that those values must exist or no results will be returned.
-
-However, most of the time, developers are not querying for a narrow value and need more flexibility in retrieving data for ranges, partial values, or other criteria.
-Cypher provides this capability through the common `WHERE` clause.
-We will look at examples of different ways to apply the `WHERE` clause to retrieve various results, but many will be similar to standard uses of the same clause in other programming languages.
-
-All of our examples will continue with the graph example we have been using in the previous guides, but include some more data for some of our later queries.
-Below is an image of the new graph.
-
-We have added more `Person` nodes (blue) who `WORK_FOR` different `Company` nodes (red) and `LIKE` different `Technology` nodes.
-Each person could also have multiple `IS_FRIENDS_WITH` relationships to other people.
-This gives us a network of people, the companies they work for, and the technologies they like.
-
-image::{img}cypher_graph_v2.jpg[role="popup-link"]
-
-[#cypher-where]
-== The WHERE clause
-
-Cypher is designed to be flexible and easy-to-learn, so there is often more than one way to write syntax.
-This is also the case with the `WHERE` clause.
-You can write a query that looks for specific values, just like we have been doing in the last few guides, but you can also use the `WHERE` clause in the same manner.
-Both queries execute with the same performance, so which way you write them is entirely up to your preference and comfort.
-
-Below is a comparison of the syntax using our example from previous guides.
-Both queries will do the same thing and return the same results.
-
-[source, cypher]
-----
-//query using equality check in the MATCH clause
-MATCH (j:Person {name: 'Jennifer'})
-RETURN j;
-
-//query using equality check in the WHERE clause
-MATCH (j:Person)
-WHERE j.name = 'Jennifer'
-RETURN j;
-----
-
-[#filter-negative]
-=== Negating Properties
-
-Sometimes, you may want to return results that do not match a property value.
-In this case, you need to search for where value is not something using `WHERE NOT`.
-
-There are a few types of these comparisons that you can run in Cypher with the standard boolean operators `AND`, `OR`, `XOR`, and `NOT`.
-To show a comparison using `NOT`, we can write the opposite of the query example just above.
-
-[source, cypher]
-----
-//query using inequality check in the WHERE clause
-MATCH (j:Person)
-WHERE NOT j.name = 'Jennifer'
-RETURN j
-----
-
-[#filter-ranges]
-=== Querying Ranges of Values
-
-There are frequently queries where you want to look for data within a certain range.
-Date or number ranges can be used to check for events within a certain timeline, age values, or other uses.
-
-The syntax for this criteria is very similar to SQL and other programming language logic structures for checking ranges of values.
-Let's look at an example below.
-
-[source, cypher]
-----
-MATCH (p:Person)
-WHERE 3 <= p.yearsExp <= 7
-RETURN p
-----
-
-image::{img}cypher_filter_ranges.jpg[role="popup-link"]
-
-[#filter-exists]
-=== Testing if a Property Exists
-
-You may only be interested if a property exists on a node or relationship.
-For instance, you might want to check which customers in your system have Twitter handles, so you can show relevant content.
-Or, you could check if the all of your employees have a start date property to verify which entities might need updated.
-
-Remember: in Neo4j, a property only exists (is stored) if it has a value.
-A null property will not be stored.
-This ensures that only valuable, necessary information is retained for your nodes and relationships.
-
-To write this type of existence check, you simply need to use the `WHERE` clause and the `exists()` method for that property.
-The Cypher code is written in the block below.
-
-[source, cypher]
-----
-//Query1: find all users who have a birthdate property
-MATCH (p:Person)
-WHERE exists(p.birthdate)
-RETURN p.name;
-
-//Query2: find all WORKS_FOR relationships that have a startYear property
-MATCH (p:Person)-[rel:WORKS_FOR]->(c:Company)
-WHERE exists(rel.startYear)
-RETURN p, rel, c;
-----
-
-.Query1 Results:
-image:{img}cypher_filter_exists_nodeProp.jpg[role="popup-link"]
-
-.Query2 Results:
-image:{img}cypher_filter_exists_relProp.jpg[role="popup-link"]
-
-[#filter-strings]
-== Checking Strings - Partial Values, Fuzzy Searches, and More
-
-Some scenarios require query syntax that matches on partial values or broad categories within a string.
-To do this kind of query, you need some flexibility and options for string matching and searches.
-Whether you are looking for a string that starts with, ends with, or includes a certain value, Cypher offers the ability to handle it performantly and easily.
-
-There are a few keywords in Cypher used with the `WHERE` clause to test string property values.
-The `STARTS WITH` keyword allows you check the value of a property that begins with the string you specify.
-With the `CONTAINS` keyword, you can check if a specified string is part of a property value.
-The `ENDS_WITH` keyword checks the end of the property string for the value you specify.
-
-An example of each is in the Cypher block below.
-
-[source, cypher]
-----
-//check if a property starts with 'M'
-MATCH (p:Person)
-WHERE p.name STARTS WITH 'M'
-RETURN p.name;
-
-//check if a property contains 'a'
-MATCH (p:Person)
-WHERE p.name CONTAINS 'a'
-RETURN p.name;
-
-//check if a property ends with 'n'
-MATCH (p:Person)
-WHERE p.name ENDS WITH 'n'
-RETURN p.name;
-----
-
-You can also use regular expressions to test the value of strings.
-For example, you could look for all the `Person` nodes that share a first name or you could find all the classes with a certain department code.
-
-Let's look at an example.
-
-[source, cypher]
-----
-MATCH (p:Person)
-WHERE p.name =~ 'Jo.*'
-RETURN p.name
-----
-
-image::{img}cypher_filter_regex.jpg[role="popup-link"]
-
-Just like in SQL and other languages, you can check if a property value is a value in a list.
-The `IN` keyword allows you to specify an array of values and validate a property's contents against each one in the list.
-
-Here is an example:
-
-[source, cypher]
-----
-MATCH (p:Person)
-WHERE p.yearsExp IN [1, 5, 6]
-RETURN p.name, p.yearsExp
-----
-
-image::{img}cypher_filter_list.jpg[role="popup-link"]
-
-[#filter-patterns]
-== Filtering on Patterns
-
-One thing that makes graph unique is its focus on relationships.
-Just as you can filter queries based on node labels or properties, you can also filter results based on relationships or patterns.
-This allows you to test if a pattern also has a certain relationship or doesn't, or if another pattern exists.
-
-The Cypher code below shows how this is done.
-
-[source, cypher]
-----
-//Query1: find which people are friends of someone who works for Neo4j
-MATCH (p:Person)-[r:IS_FRIENDS_WITH]->(friend:Person)
-WHERE exists((p)-[:WORKS_FOR]->(:Company {name: 'Neo4j'}))
-RETURN p, r, friend;
-
-//Query2: find Jennifer's friends who do not work for a company
-MATCH (p:Person)-[r:IS_FRIENDS_WITH]->(friend:Person)
-WHERE p.name = 'Jennifer'
-AND NOT exists((friend)-[:WORKS_FOR]->(:Company))
-RETURN friend.name;
-----
-
-.Query1 Results:
-image:{img}cypher_filter_exists_ptrn.jpg[role="popup-link"]
-
-.Query2 Results:
-image:{img}cypher_filter_notExists_ptrn.jpg[role="popup-link"]
-
-[#filter-optional]
-=== Optional Patterns
-
-There are cases where you might want to retrieve results from patterns, even if they do not match the entire pattern or all of the criteria.
-This is how an outer join in SQL functions.
-In Cypher, you can use an `OPTIONAL MATCH` pattern to try to match it, but if it doesn't find results, those rows will return `null` for those values.
-
-We can see how this would look in Cypher by querying for people whose name starts with a letter and who may work for a company.
-
-[source, cypher]
-----
-//find all people whose name starts with J and who may work for a company.
-MATCH (p:Person)
-WHERE p.name STARTS WITH 'J'
-OPTIONAL MATCH (p)-[:WORKS_FOR]-(other:Company)
-RETURN p.name, other.name;
-----
-
-image::{img}cypher_filter_optMatch.jpg[role="popup-link"]
-
-Notice that Joe is returned because his name starts with the letter 'J', but his company name is null.
-That is because he does not have a `WORKS_FOR` relationship to a company node.
-Since we used optional match, his `Person` node is still returned from the first match, but the second match is not found, so returns null.
-
-[NOTE]
---
-To see the difference, try running the query without the `OPTIONAL` in front of the second match.
-You can see that Joe's row is no longer returned.
-That is because Cypher reads the statement with an `AND` match, so that the person must match the first criteria (name starts with 'J') and the second criteria (person works for a company).
---
-
-[#filter-paths]
-== More Complex Patterns
-
-We are able to handle many simple graph queries, even at this point, but what happens when we want to extend our patterns past a single relationship?
-What if we wanted to know who else likes graphs besides Jennifer?
-
-We handle this functionality and many others by simply adding on to our first pattern or matching additional patterns.
-Let us look at a couple of examples.
-
-[source, cypher]
-----
-//Query1: find who likes graphs besides Jennifer
-MATCH (j:Person {name: 'Jennifer'})-[r:LIKES]-(graph:Technology {type: 'Graphs'})-[r2:LIKES]-(p:Person)
-RETURN p.name;
-
-//Query2: find who likes graphs besides Jennifer that she is also friends with
-MATCH (j:Person {name: 'Jennifer'})-[:LIKES]->(:Technology {type: 'Graphs'})<-[:LIKES]-(p:Person),
-      (j)-[:IS_FRIENDS_WITH]-(p)
-RETURN p.name;
-----
-
-.Query1 Results:
-image:{img}cypher_filter_extPattern.jpg[role="popup-link"]
-
-.Query2 Results:
-image:{img}cypher_filter_twoPattern.jpg[role="popup-link"]
-
-Notice that on the second query, we used a comma after the first `MATCH` line and added another pattern to match on the next line.
-This allows us to chain together patterns, similar to when we used the `WHERE exists()` syntax above.
-With this structure, though, we can add multiple different patterns and link them together, allowing us to traverse various pieces of the graph with certain patterns.
-
-[#cypher-next-steps]
-=== Next Steps
-
-We have seen how to use the `WHERE` clause to filter property values and how to search properties for partial values or string matches.
-Patterns helped us maneuver through the graph and check data for specific relationships or paths.
-In the next section, we will learn how to control query processing in Cypher.
-
-[#cypher-resources]
-=== Resources
-
-* link:/docs/cypher-manual/current/clauses/where/[Neo4j Cypher Manual: Filtering with WHERE^]
-* link:/docs/cypher-manual/current/clauses/optional-match/[Neo4j Cypher Manual: OPTIONAL MATCH^]
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/filtering-show.adoc b/modules/cypher/pages/filtering-show.adoc
deleted file mode 100644
index 876c5a92..00000000
--- a/modules/cypher/pages/filtering-show.adoc
+++ /dev/null
@@ -1,97 +0,0 @@
-= Filtering SHOW results
-:level: Beginner
-:page-level: Beginner
-:author: Elaine Rosenberg
-:neo4j-version: 4.1
-:category: cypher
-:tags: cypher, queries, show, filter, database, user
-:description: This guide shows how to better utilize results of the `SHOW` command.
-:page-pagination:
-:page-aliases: ROOT:filtering-show.adoc
-:page-newsletter: true
-:page-ad-overline-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-overline: Neo4j GraphAcademy
-:page-ad-title: Cypher Fundamentals
-:page-ad-description: Learn Cypher in this free, hands-on course
-:page-ad-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-underline-role: button
-:page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/results/#cypher-intro-results-filtering
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with xref:ROOT:manage-multiple-databases.adoc[managing databases].
-You should also be familiar with the commands to link:{opsmanual}/authentication-authorization/[Docs: Manage Users].
-
-[role=expertise {level}]
-{level}
-
-
-[#show-commands]
-== SHOW commands in Neo4j 4.0
-
-In Neo4j 4.0, we introduced these commands to help you manage a Neo4j instance and users.
-Note that in 4.0, these commands had to be issued from the system database.
-
-----
-SHOW DATABASES
-SHOW DATABASE [DEFAULT DATABASE] | []
-----
-
-----
-SHOW USERS
-SHOW USER 
-----
-
-----
-SHOW [ALL | POPULATED] ROLES [WITH USERS}
-SHOW ROLE  PRIVILEGES
-----
-
-----
-SHOW PRIVILEGES
-----
-
-[#yield-for-show-commands]
-== Using SHOW commands in Neo4j 4.1
-
-In 4.1, these commands have been enhanced so you can filter results to display.
-You can specify specific columns that you want to return or filter.
-In addition, you need not run these commands using the system database.
-
-For example:
-
-----
-SHOW DATABASES
-YIELD name, currentStatus
-WHERE name CONTAINS 'test' AND currentStatus = 'online';
-----
-
-image::show-databases-example.jpg[Using SHOW DATABASES, link="{imagesdir}/show-databases-example.jpg",role="popup-link"]
-
-Here is another example:
-
-----
-SHOW ROLE reader PRIVILEGES
-YIELD access, resource, segment, action
-WHERE access = 'GRANTED'  AND action = 'match';
-----
-
-image::show-roles-example.jpg[Using SHOW ROLES, link="{imagesdir}/show-roles-example.jpg",role="popup-link"]
-
-
-[#cypher-resources]
-=== Resources
-
-* link:https://neo4j.com/docs/cypher-manual/current/administration/databases/[Docs: Cypher commands to manage databases].
-* link:https://neo4j.com/docs/operations-manual/current/authentication-authorization/[Docs: Manage Users].
-
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/guide-build-a-recommendation-engine.adoc b/modules/cypher/pages/guide-build-a-recommendation-engine.adoc
deleted file mode 100644
index 93c800f6..00000000
--- a/modules/cypher/pages/guide-build-a-recommendation-engine.adoc
+++ /dev/null
@@ -1,199 +0,0 @@
-= Tutorial: Build a Cypher Recommendation Engine
-:level: Beginner
-:page-level: Beginner
-:author: Neo4j
-:category: cypher
-:tags: cypher, queries, recommendations, tutorial, filtering, paths
-:description: Gather insights and generate recommendations with simple cypher queries, by navigating the graph
-:page-pagination:
-:page-aliases: ROOT:guide-build-a-recommendation-engine.adoc
-:page-type: Tutorial
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/#recommendation-engine
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should have a basic understanding of the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-
-[role=expertise {level}]
-{level}
-
-[#cypher-tutorial]
-Graphs are everywhere.
-By following the meaningful relationships between the people and movies, you can determine occurences of actors working together, the frequency of actors working with one another, and the movies they have in common in the graph.
-This is one way we can recommend movies to users, based on what they liked before, and their favorite actors.
-We will step you through everything you need to get started with AuraDB and Cypher, to solve a real-world problem.
-
-:cta-header: First, Create your AuraDB Free Instance
-include::ROOT:page$_includes/aura_cta.adoc[]
-
-[#recommendation-setup]
-== Setting Up
-
-
-When you've created your AuraDB account, click "Create a Database" and select a free database
-
-image::{img}free-database-type.png[role="popup-link"]
-
-Then, fill out the name, and choose a cloud region for your database and click "Create Database".  Make sure "Learn about graphs with a movie dataset" is selected, so you'll start with a dataset.
-
-image::{img}recommendation-engine-free-database.png[role="popup-link"]
-
-AuraDB will prompt you with the password for your new instance while it being set up.  **Make sure to save the password for later steps**.
-
-Once your database is running, open browser as shown below.
-
-image::{img}open-auradb-browser.png[role="popup-link"]
-
-Now you've arrived inside of Neo4j Browser.  Use your username and password (the one you captured above) to log in.  You'll immediately notice a guide on the left-hand side that you can tab through to start out with some experimental queries.  Any of these queries you see can be automatically put into the query execution box and run on the right hand side of the screen by clicking the little "play" button.
-
-image::{img}first-movies-query.png[role="popup-link"]
-
-This first query just shows a few movies in the database to prove there's something there.
-Congratulations, you've got some data in a new database, and we're ready to get started.
-
-The next section will show you how to write some queries to explore the data you just created.
-
-[#recommendation-queries]
-== Basic Queries
-
-Before we start recommending things, we need to find out what is interesting in our data to see what kinds of things we can and want to recommend.
-To start, let us run a query like this to find a single actor like _Tom Hanks_.
-
-[source,cypher]
-----
-MATCH (tom:Person {name: 'Tom Hanks'})
-RETURN tom
-----
-
-image::{img}cytutorial_match_tomhanks2.png[role="popup-link"]
-
-Now that we found an actor we are interested in, we can retrieve all his movies by starting from the `Tom Hanks` node and following the `ACTED_IN` relationships.
-Your results should look like a graph.
-
-[source,cypher]
-----
-MATCH (tom:Person {name: 'Tom Hanks'})-[r:ACTED_IN]->(movie:Movie)
-RETURN tom, r, movie
-----
-
-image::{img}cytutorial_tomhanks_movies2.png[role="popup-link"]
-
-Of course, Tom has colleagues who acted with him in his movies.
-A statement to find Tom's co-actors looks like this:
-
-[source,cypher]
-----
-MATCH (tom:Person {name: 'Tom Hanks'})-[:ACTED_IN]->(:Movie)<-[:ACTED_IN]-(coActor:Person)
-RETURN coActor.name
-----
-
-image::{img}cytutorial_tomhanks_coactors2.png[role="popup-link"]
-
-[#collaborative-filtering]
-== Recommendations with Collaborative Filtering
-
-We can now turn the co-actor query above into a recommendation query by following those relationships another step out to find the "co-co-actors", i.e. the second-degree actors in Tom's network.
-This will show us all the actors Tom may not have worked with yet, and we can specify a criteria to be sure he hasn't directly acted with that person.
-
-[source,cypher]
-----
-MATCH (tom:Person {name: 'Tom Hanks'})-[:ACTED_IN]->(movie1:Movie)<-[:ACTED_IN]-(coActor:Person)-[:ACTED_IN]->(movie2:Movie)<-[:ACTED_IN]-(coCoActor:Person)
-WHERE tom <> coCoActor
-AND NOT (tom)-[:ACTED_IN]->(:Movie)<-[:ACTED_IN]-(coCoActor)
-RETURN coCoActor.name
-----
-
-image::{img}cytutorial_tomhanks_cocoactors2.png[role="popup-link"]
-
-You probably noticed that a few names appear multiple times.
-This is because there are multiple paths to follow from _Tom Hanks_ to these actors.
-
-To see which co-co-actors appear most often in Tom's network, we can take frequency of occurrences into account by counting the number of paths between _Tom Hanks_ and each coCoActor and ordering them by highest to lowest value.
-
-[source,cypher]
-----
-MATCH (tom:Person {name: 'Tom Hanks'})-[:ACTED_IN]->(movie1:Movie)<-[:ACTED_IN]-(coActor:Person)-[:ACTED_IN]->(movie2:Movie)<-[:ACTED_IN]-(coCoActor:Person)
-WHERE tom <> coCoActor
-AND NOT (tom)-[:ACTED_IN]->(:Movie)<-[:ACTED_IN]-(coCoActor)
-RETURN coCoActor.name, count(coCoActor) as frequency
-ORDER BY frequency DESC
-LIMIT 5
-----
-
-image::{img}cytutorial_tomhanks_cocoactors_freq2.png[role="popup-link"]
-
-One of those "co-co-actors" is _Tom Cruise_.
-Now let's see which movies and actors are between the two Toms so we can find out who can introduce them.
-
-=== Exploring the Paths
-
-[source,cypher]
-----
-MATCH (tom:Person {name: 'Tom Hanks'})-[:ACTED_IN]->(movie1:Movie)<-[:ACTED_IN]-(coActor:Person)-[:ACTED_IN]->(movie2:Movie)<-[:ACTED_IN]-(cruise:Person {name: 'Tom Cruise'})
-WHERE NOT (tom)-[:ACTED_IN]->(:Movie)<-[:ACTED_IN]-(cruise)
-RETURN tom, movie1, coActor, movie2, cruise
-----
-
-image::{img}cytutorial_tomhanks_tomcruise2.png[role="popup-link"]
-
-As you can see, this returns multiple paths.
-If you have ever played the https://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_Bacon[six degrees of Kevin Bacon^] game, this concept of seeing how many hops exist between people is exactly what graphs depict.
-You will notice that our results even return a path with _Kevin Bacon_ himself.
-
-With these two simple Cypher statements, we already created two recommendation algorithms - *who to meet/work with next* and *how to meet them*.
-
-[#recommend-others]
-== Other Recommendations
-
-You could apply the same ideas you learned here to many other uses for recommending products and services, finding restaurants or activities you might like, or connecting with other colleagues who share similar interests of skills.
-We will mention a few specifically here with resources you can use to find more information.
-
-=== Restaurant Recommendations
-
-We have a graph of a few friends with their favorite restaurants, cuisines, and locations.
-
-image::{img}restaurant-recommendation.png[]
-
-A practical question to answer here, formulated as a http://neo4j.com/blog/why-the-most-important-part-of-facebook-graph-search-is-graph/[graph search^], is:
-
-----
-What Sushi restaurants are in New York that my friends like?
-----
-
-How could we translate that into the appropriate Cypher statement?
-
-[source,cypher]
-----
-MATCH (person:Person {name: 'Philip'})-[:IS_FRIEND_OF]->(friend)-[:LIKES]->(restaurant:Restaurant)-[:LOCATED_IN]->(loc:Location {location: 'New York'}),
-      (restaurant)-[:SERVES]->(type:Cuisine {type: 'Sushi'})
-RETURN restaurant.name, count(*) AS occurrence
-ORDER BY occurrence DESC
-LIMIT 5
-----
-
-Other factors that can be easily integrated in this query are favorites, allergies, ratings, and distance from my current position.
-
-=== More Recommendation Solutions
-
-* https://medium.com/neo4j/whats-cooking-approaches-for-importing-bbc-goodfood-information-into-neo4j-64a481906172[Recipe and Food Recommendations^]
-* https://sandbox.neo4j.com/?usecase=recommendations&ref=developer-rec-engine[Sandbox: Recommend Movies by Reviews^]
-* link:/graphgist/beer-amp-breweries-graphgist/[GraphGist: Beer and Breweries Recommendations^]
-* link:/graphgist/northwind-recommendation-engine/[GraphGist: Northwind Product Recommendations^]
-
-[#recommendation-resources]
-== Resources
-* https://www.youtube.com/channel/UCvze3hU6OZBkB1vkhH2lH9Q/search?query=recommendation[Neo4j Videos: Building Recommendation Engines^]
-* link:/use-cases/real-time-recommendation-engine/[Recommendation Use Cases^]
-* link:/graphacademy/online-training/online-training/introduction-to-neo4j-40/[Online Training: Learn Cypher with Intro to Neo4j^]
-* http://www.slideshare.net/bachmanm/recommendations-with-neo4j[Michal Bachman Slides: Recommendation Engines with Neo4j^]
-* link:/graphgists/?category=real-time-recommendations[GraphGists: Recommendation Engine Examples^]
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/guide-cypher-basics.adoc b/modules/cypher/pages/guide-cypher-basics.adoc
deleted file mode 100644
index c618b30b..00000000
--- a/modules/cypher/pages/guide-cypher-basics.adoc
+++ /dev/null
@@ -1,363 +0,0 @@
-= Tutorial: Getting Started with Cypher
-:level: Beginner
-:page-level: Beginner
-:author: Neo4j
-:category: cypher
-:tags: cypher, queries, graph-queries, movie-graph, cypher-example
-:description: This tutorial explains the basic concepts of Cypher, Neo4j's query language, including how to create and query graphs.
-:page-aliases: ROOT:guide-cypher-basics.adoc
-:page-newsletter: true
-:page-ad-overline-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-overline: Neo4j GraphAcademy
-:page-ad-title: Cypher Fundamentals
-:page-ad-description: Learn Cypher in this free, hands-on course
-:page-ad-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-underline-role: button
-:page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/#_getting_started_with_cypher
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-You should be able to read and understand Cypher queries after finishing this tutorial.
-
-.Prerequisites
-[abstract]
-You should have familiarized yourself with xref:ROOT:get-started.adoc[Graph Databases] and the xref:ROOT:graph-database.adoc#property-graph[Property Graph Model].
-
-[role=expertise {level}]
-{level}
-
-[#cypher-basics]
-== Pop culture connections
-
-_The Movie Graph_ is a mini graph application containing actors and directors that are related through the movies they've collaborated on.
-
-It is helpful if you run the queries and cypher code to create data as you follow this tutorial.
-
-This tutorial will show you how to:
-
-1. Create: Insert movie data into the graph.
-2. Find: Retrieve individual movies and actors.
-3. Query: Find patterns in the graph.
-4. Solve: Answer some questions about the graph.
-
-[#cypher-movie-create]
-== Create the movie graph
-
-. Create and start a new Neo4j database.
-.. Create a blank sandbox at https://sandbox.neo4j.com or..
-.. Create a new database in Neo4j Desktop:
-... Create a new project.
-... Add a database to the project.
-... Start the database.
-. Open Neo4j Browser.
-. Set the browser settings to allow multi-statements:
-
-image::EnableMultiStatement.png[Enable Multi-statement, link="{imagesdir}/EnableMultiStatement.png",role="popup-link"]
-
-[start=4]
-. Enter `:play movie graph` in the query pane and click the "Play" button on the right. A new window opens below the query pane with the browser guide.
-. Go to page 2 of the browser guide.
-. Click on the Cypher code block which will bring it into the query pane and click the "Play" button.
-
-This is what you should see in Neo4j Browser after loading the movie graph:
-
-image::AfterLoadMovieGraph.png[Loaded Movie Graph, link="{imagesdir}/AfterLoadMovieGraph.png",role="popup-link"]
-
-This is the graph view of some of the data returned.
-
-If you want to see the table view of the data returned, you click the table icon on the left:
-
-image::AfterLoadMovieGraphTable.png[Loaded Movie Graph as Table, link="{imagesdir}/AfterLoadMovieGraphTable.png",role="popup-link"]
-
-How you view the results will also depend on the data returned. If the query returns nodes, then you can view the data as a graph. If the query returns property values, you can only view the data as a table.
-
-If you need help:
-
-`:help cypher`
-
-[NOTE]
-When you run Cypher code in the query pane, it always creates a new pane with the results below the query pane.
-
-[#cypher-movie-find]
-== Find actors and movies
-
-Next, you will learn about queries for finding individual nodes.
-
-1. Look at every query example
-2. Run the query with the play button
-3. Notice the syntax pattern
-4. Try looking for other movies or actors
-
-If you need help with syntax:
-
-`:help MATCH`, `:help WHERE`, and `:help RETURN`
-
-=== Find the person named "Tom Hanks"...
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (tom:Person)
-WHERE tom.name = "Tom Hanks"
-RETURN tom
-----
-
-The graph result should look as follows:
-
-image::findTom.png[Find Tom Hanks, link="{imagesdir}/findTom.png",role="popup-link"]
-
-You can also view the properties of the node with the table view:
-
-image::findTomTable.png[Find Tom Hanks Table, link="{imagesdir}/findTomTable.png",role="popup-link"]
-
-=== Find the movie titled "Cloud Atlas"...
-
-Here we filter the query a different way where we specify the value in the node specification, rather than using a `WHERE` clause.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (cloudAtlas:Movie {title: "Cloud Atlas"})
-RETURN cloudAtlas
-----
-
-Here is the result of this query:
-
-image::findCloudAtlas.png[Find Cloud Atlas, link="{imagesdir}/findCloudAtlas.png",role="popup-link"]
-
-And here is the table view:
-
-image::findCloudAtlasTable.png[Find Cloud Atlas Table, link="{imagesdir}/findCloudAtlasTable.png",role="popup-link"]
-
-=== Find 10 people...
-
-Next we want to find the names of 10 people in the graph. This code finds all _Person_ nodes in the graph but just returns the _name_ property value for 10 of them.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (people:Person)
-RETURN people.name LIMIT 10
-----
-
-Here is the result of this query:
-
-image::findTenPeople.png[Find 10 People, link="{imagesdir}/findTenPeople.png",role="popup-link"]
-
-For this query, property values are returned and you can only view the results as a table.
-
-=== Find movies released in the 1990s...
-
-Here is a query where we specify a range of values for selecting the _Movie_ nodes to retrieve. Then we return the titles of these _Movie_ nodes.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (nineties:Movie)
-WHERE nineties.released > 1990 AND nineties.released < 2000
-RETURN nineties.title
-----
-
-Here is the result of this query:
-
-image::findNinetiesMovies.png[Find 1990's Movies, link="{imagesdir}/findNinetiesMovies.png",role="popup-link"]
-
-
-[#cypher-movie-query]
-== Find patterns in the graph
-
-Thus far, you have queried the graph for nodes. Next, you will gain experience retrieving related nodes.
-
-You will execute Cypher code to find patterns within the graph.
-
-1. Actors are people who acted in movies.
-2. Directors are people who directed a movie.
-3. What other relationships exist?
-
-=== List all Tom Hanks movies...
-
-Here is a query where we want to return the _Person_ node for the actor Tom Hanks and we also want to return all _Movie_ nodes that have the _ACTED_IN_ relationship to Tom Hanks. That is, all movies that Tom Hanks acted in.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (tom:Person {name: "Tom Hanks"})-[:ACTED_IN]->(tomHanksMovies)
-RETURN tom,tomHanksMovies
-----
-
-Here is the result of this query:
-
-image::findTomHanksMovies.png[Find Tom Hanks Movies, link="{imagesdir}/findTomHanksMovies.png",role="popup-link"]
-
-Notice here that we also see the _DIRECTED_ relationships between the Tom Hanks node and the _Movie_ nodes. This is because we have a setting in our Neo4j Browser where result nodes will be connected:
-
-image::ConnectResultNodesSetting.png[Connected Nodes, link="{imagesdir}/ConnectResultNodesSetting.png",role="popup-link"]
-
-And here is the table view:
-
-image::findTomHanksMoviesTable.png[Find Tom Hanks Movies Table, link="{imagesdir}/findTomHanksMoviesTable.png",role="popup-link"]
-
-
-=== Who directed "Cloud Atlas"?
-
-Here is a query where we want to return the nodes that have the _DIRECTED_ relationship to the Cloud Atlas _Movie_ node. It will return the names of the people who directed the movie.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (cloudAtlas:Movie {title: "Cloud Atlas"})<-[:DIRECTED]-(directors)
-RETURN directors.name
-----
-
-Here is the result of this query:
-
-image::DirectorsCloudAtlas.png[Directors of Cloud Atlas, link="{imagesdir}/DirectorsCloudAtlas.png",role="popup-link"]
-
-
-=== Tom Hanks' co-actors...
-
-Next, we want to find all movies that Tom Hanks acted in and for each movie retrieved, also find the people who acted in that movie.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (tom:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors)
-RETURN tom, m, coActors
-----
-
-Here is the result of this query:
-
-image::TomsCoActors.png[CoActors of Tom Hanks, link="{imagesdir}/TomsCoActors.png",role="popup-link"]
-
-And here is the table view:
-
-image::TomsCoActorsTable.png[CoActors of Tom Hanks Table, link="{imagesdir}/TomsCoActorsTable.png",role="popup-link"]
-
-
-=== How people are related to "Cloud Atlas"...
-
-Here is a query where we want to return information about the relationships to and from the Cloud Atlas movie. We find the related nodes and then we return the name of the person, the type of relationship, and the properties for that relationship.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (people:Person)-[relatedTo]-(:Movie {title: "Cloud Atlas"})
-RETURN people.name, type(relatedTo), relatedTo
-----
-
-Here is the result of this query:
-
-image::CloudAtlasRelationships.png[Cloud Atlas Relationships, link="{imagesdir}/CloudAtlasRelationships.png",role="popup-link"]
-
-
-[#cypher-paths]
-== Answer some questions about the graph
-
-You've heard of the classic "Six Degrees of Kevin Bacon"? That is, find all people who are up to 6 hops away from Kevin Bacon in the graph.
-This is simply a shortest path query called the "Bacon Path".
-To perform this type of query, you need to specify:
-
-* Variable length patterns:
-{cyphermanual}/syntax/patterns/#cypher-pattern-varlength[variable length relationships^]
-* Built-in shortestPath() algorithm:
-{cyphermanual}/execution-plans/shortestpath-planning/[shortestPath^]
-
-
-=== Movies and actors up to 3 hops away from Kevin Bacon
-
-In our first query, we want to find all movies and/or people who are up to 3 hops away from Kevin Bacon in the graph.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (bacon:Person {name:"Kevin Bacon"})-[*1..3]-(hollywood)
-RETURN DISTINCT bacon, hollywood
-----
-
-Here is the result of this query:
-
-image::ThreeDegreesKevinBacon.png[3 Hops from Kevin Bacon, link="{imagesdir}/ThreeDegreesKevinBacon.png",role="popup-link"]
-
-
-=== Find the Bacon Path to Meg Ryan
-
-What is the shortest path between Kevin Bacon and Meg Ryan in the graph?
-In this Cypher, we are returning the path that includes nodes and relationships.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH p=shortestPath(
-  (bacon:Person {name:"Kevin Bacon"})-[*]-(meg:Person {name:"Meg Ryan"})
-)
-RETURN p
-----
-
-Before you execute the query, you will see a warning that a relationship of '*' could take a long time to execute. Our movie graph is small, so you can ignore this warning.
-
-Here is the result of this query:
-
-image::KevinBaconToMegRyan.png[Kevin Bacon to Meg Ryan, link="{imagesdir}/KevinBaconToMegRyan.png",role="popup-link"]
-
-
-[#cypher-movie-cleanup]
-== Clean up
-
-When you're done experimenting, you can remove the movie data set.
-
-[NOTE]
-1. Nodes can't be deleted if relationships to them exist.
-2. Delete both nodes and relationships together.
-
-[WARNING]
-This will remove all nodes and relationships in the graph!
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (n)
-DETACH DELETE n
-----
-
-Here is the result of this query:
-
-image::DetachDelete.png[Delete all Nodes, link="{imagesdir}/DetachDelete.png",role="popup-link"]
-
-Notice that although the database information in the left panel shows no nodes or relationships in the graph, the property key names remain.
-
-
-=== Verify that the movie graph data is gone
-
-If you perform this query to retrieve all nodes in the graph and return the count, you should see a value of 0 returned.
-
-Copy and paste this code into the query pane and execute it:
-
-[source, cypher]
-----
-MATCH (n)
-RETURN count(*)
-----
-
-Here is the result of this query:
-
-image::ZeroNodes.png[Zero Nodes, link="{imagesdir}/ZeroNodes.png",role="popup-link"]
-
-*Congratulations!* You have learned how to use Cypher to query a Neo4j database.
-
-include::partial$next-steps.adoc[]
\ No newline at end of file
diff --git a/modules/cypher/pages/guide-sql-to-cypher.adoc b/modules/cypher/pages/guide-sql-to-cypher.adoc
deleted file mode 100644
index 50161c6e..00000000
--- a/modules/cypher/pages/guide-sql-to-cypher.adoc
+++ /dev/null
@@ -1,507 +0,0 @@
-= Comparing SQL with Cypher
-:level: Beginner
-:page-level: Beginner
-:author: Neo4j
-:category: cypher
-:tags: cypher, queries, graph-queries, sql-cypher, northwind-graph
-:description: This guide teaches anyone familiar with SQL how to write the equivalent, efficient Cypher statements. We'll use the well-known Northwind database to explain the concepts and work through the queries from simple to advanced.
-:page-pagination:
-:page-aliases: ROOT:guide-sql-to-cypher.adoc
-:page-newsletter: true
-:page-ad-overline-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-overline: Neo4j GraphAcademy
-:page-ad-title: Cypher Fundamentals
-:page-ad-description: Learn Cypher in this free, hands-on course
-:page-ad-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-underline-role: button
-:page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should have a basic understanding of the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-Having link:/download[downloaded] and installed Neo4j helps you code along with the examples.
-Importing the data is covered in our xref:ROOT:guide-importing-data-and-etl.adoc[data import guide].
-
-[role=expertise {level}]
-{level}
-
-[#cypher-for-sql-dev]
-== A few words about Cypher
-
-Cypher is like SQL a declarative, textual query language, but for graphs.
-
-It consists of clauses, keywords and expressions like predicates and functions, many of which will be familiar (like `WHERE`, `ORDER BY`, `SKIP LIMIT`, `AND`, `p.unitPrice > 10`).
-
-Unlike SQL, Cypher is all about expressing graph patterns.
-We added a special clause `MATCH` for *matching* those patterns in your data.
-These patterns are what you would usually draw on a whiteboard, just converted into text using *ASCII-art symbols*.
-
-We represent the circles of node entities with round parentheses, like this: `(p:Product)`.
-
-And arrows of relationships are drawn as such `+-->+`, you can add relationship-type and other information in square brackets +
-`+-[:ORDERED]->+`.
-
-Bringing both together `+()-->()<--()+` looks almost like our original diagram.
-It gives us a first hint at the expressiveness of graph patterns: `+(cust:Customer)-[:ISSUED]->(o:Order)-[:CONTAINS]->(prod:Product)+`.
-
-// what is going on here?
-Other highlights of Cypher are graph concepts like paths, variable length paths, shortest-path functions;
-the support of many functions, operations and predicates on lists and the ability to chain query parts.
-
-You can use Cypher to update the graph structure and data and even ingest large amounts of CSV data.
-
-With *user defined procedures* you can extend the language with functionality that you need but which is currently not yet available.
-
-The full Cypher language documentation is available in the {cyphermanual}/[Neo4j Cypher Manual^] together with a complete link:/docs/cypher-refcard/current/[Reference Card].
-
-With the http://openCypher.org[openCypher project], Cypher became an open effort for a modern graph query language, that is supported by several database companies.
-The openCypher project also provides syntax diagrams that you're used to from SQL:
-
-++++
-
-++++
-
-[#cypher-sql-data-model]
-== Data Model Transformation
-
-Relational databases store data in tables with fixed structure (schema), each column having a name, type, length, constraints etc.
-References between tables are represented by repeating the primary key of one table as column in another as a foreign key.
-For many-to-many references, JOIN-tables (or link-tables) are needed as artificial construct between the connected tables.
-
-A well normalized relational model can be directly translated into a equivalent graph model.
-Graph models are mostly driven by use-cases, so there will be opportunity for optimization and model evolution afterwards.
-
-A good, well normalized Entity-Relationship diagram often already represents a decent graph model.
-So if you still have the original ER diagram of your database available, try to use it as a guide.
-
-For such a sensible relational model, the transformation is not too hard.
-Rows of entity tables are converted into nodes and foreign-key relationships and JOIN tables into relationships.
-
-It is important to have an good understanding of the graph model before you start to import data, then it just becomes the task of hydrating that model.
-
-[#cypher-sql-data-import]
-== Data Import
-
-Most relational databases allow an easy export of tables to CSV files, e.g. in Postgres `COPY (SELECT * FROM customers) TO '/tmp/customers.csv' WITH CSV header;`.
-Those files can come from single tables, but might also represent a set of joined tables with some duplicate data.
-
-They can be imported by using Cypher's `LOAD CSV` capability, which we explain in detail in these guides:
-
-* xref:ROOT:guide-importing-data-and-etl.adoc["Guide: Data Import",role=guide]
-* http://watch.neo4j.org/video/112447027["Webinar: Data Import",role=video]
-* xref:ROOT:guide-import-csv.adoc["Guide CSV Import",role=guide]
-
-If you are a developer you can also just connect to the relational database with your regular driver, load the data from there with SQL.
-And directly create the graph structure by using a Neo4j driver for your stack to send the equivalent, parametrized Cypher update statements to Neo4j.
-
-[#cypher-sql-patterns]
-== Cypher is all about Patterns
-
-As already mentioned, the essence of your Cypher statements are the patterns you are are interested in.
-
-In a node pattern `(variable:Label)` you can use a variable and one or more labels for the node.
-You can also provide attributes as key-value structure, e.g. (item:Product {name:"Chocolade"}).
-
-For relationship-patterns like `()-[someRel:REL_TYPE]->()` it is the same, only that you might chose an variable like `someRel` and one or more alternative relationship-types.
-
-Like with SQL aliases you can use the variables later to refer to the nodes and relationships they represent, e.g. to access their properties or call a function on them.
-
-Patterns are used both for querying as well as updating the graph structures.
-
-They are usually used in the `MATCH` clause, but can also be treated as expressions or predicates.
-That's especially helpful when making sure that certain patterns shouldn't exist.
-
-[#cypher-sql-northwind]
-== Northwind Example Model
-
-The well known Northwind database represents the data storage of a retail application.
-You'll find customers, products, orders, employees, shippers and categories as well as their interactions.
-
-Please refer to the relational and graph model below when considering the data structures in the following queries.
-
-=== Relational model
-
-image::{img}Northwind_diagram.jpg[]
-
-=== Graph Model
-
-image::{img}northwind_graph_simple.png[width=800]
-
-[#cypher-sql-queries]
-== Querying the Data Step by Step
-
-The intent of this guide is to introduce Cypher by comparing it with the equivalent SQL statements, so that your existing SQL knowledge allows your to understand it immediately.
-
-=== Find all Products
-
-_Select and Return Records_
-
-Easy in SQL, just select everything from the `products` table.
-
-[source, plsql]
-----
-SELECT p.*
-FROM products as p;
-----
-
-//sqltable
-
-Similarly in Cypher, you just *match* a simple pattern: all nodes with the *label* `:Product` and `RETURN` them.
-
-[source, cypher]
-----
-MATCH (p:Product)
-RETURN p;
-----
-
-//table
-
-_Field Access, Ordering and Paging_
-
-*More efficient is to return only a subset of attributes*, like `ProductName` and `UnitPrice`.
-And while we're on it, let's also order by price and only return the 10 most expensive items.
-
-[source, plsql]
-----
-SELECT p.ProductName, p.UnitPrice
-FROM products as p
-ORDER BY p.UnitPrice DESC
-LIMIT 10;
-----
-
-//sqltable
-
-You can copy and paste the changes from SQL to Cypher, it's thankfully unsurprising.
-But remember that labels, relationship-types and property-names are *case sensitive* in Neo4j.
-
-[source, cypher]
-----
-MATCH (p:Product)
-RETURN p.productName, p.unitPrice
-ORDER BY p.unitPrice DESC
-LIMIT 10;
-----
-
-// table
-
-=== Find single Product by Name
-
-_Filter by Equality_
-
-If we only want to look at a single Product, for instance delicious _Chocolade_, we filter in SQL with a `WHERE` clause.
-
-[source, plsql]
-----
-SELECT p.ProductName, p.UnitPrice
-FROM products AS p
-WHERE p.ProductName = 'Chocolade';
-----
-
-//sqltable
-
-Same in Cypher, here the `WHERE` belongs to the `MATCH` statement. Boring.
-
-[source, cypher]
-----
-MATCH (p:Product)
-WHERE p.productName = "Chocolade"
-RETURN p.productName, p.unitPrice;
-----
-
-// table
-
-There is a shortcut in Cypher if you match for a labeled node with a certain attribute.
-
-[source, cypher]
-----
-MATCH (p:Product {productName:"Chocolade"})
-RETURN p.productName, p.unitPrice;
-----
-
-_Indexing_
-
-// table
-
-If you want to match quickly by this node-label and attribute combination, it makes sense to create an index for that pair, if you haven't during the link:/developer/guide-importing-data-and-etl/[import].
-
-[source, cypher]
-----
-CREATE INDEX ON :Product(productName);
-CREATE INDEX ON :Product(unitPrice);
-----
-
-=== Filter Products
-
-_Filter by List/Range_
-
-You can also filter by multiple values.
-
-[source, plsql]
-----
-SELECT p.ProductName, p.UnitPrice
-FROM products as p
-WHERE p.ProductName IN ('Chocolade','Chai');
-----
-
-//sqltable
-
-Similiarly in Cypher which has full collection support, not just the `IN` operator but collection functions, predicates and transformations.
-
-[source, cypher]
-----
-MATCH (p:Product)
-WHERE p.productName IN ['Chocolade','Chai']
-RETURN p.productName, p.unitPrice;
-----
-
-// table
-
-_Filter by Multiple Numeric and Textual Predicates_
-
-Filtering can go further, let's try to find some expensive things starting with "C".
-
-[source, plsql]
-----
-SELECT p.ProductName, p.UnitPrice
-FROM products AS p
-WHERE p.ProductName LIKE 'C%' AND p.UnitPrice > 100;
-----
-
-//sqltable
-
-The `LIKE` operator is replaced by a `STARTS WITH` (there are also `CONTAINS` and `ENDS WITH`) all three of which are index supported.
-
-[source, cypher]
-----
-MATCH (p:Product)
-WHERE p.productName STARTS WITH "C" AND p.unitPrice > 100
-RETURN p.productName, p.unitPrice;
-----
-
-You can also use a regular expression, like `p.productName =~ "^C.*"`.
-
-// table
-
-=== Joining Products with Customers
-
-_Join Records, Distinct Results_
-
-We want to see who bought _Chocolade_.
-Let's join the four tables together, refer to the model (ER-diagram) when you're unsure.
-
-[source, plsql]
-----
-SELECT DISTINCT c.CompanyName
-FROM customers AS c
-JOIN orders AS o ON (c.CustomerID = o.CustomerID)
-JOIN order_details AS od ON (o.OrderID = od.OrderID)
-JOIN products AS p ON (od.ProductID = p.ProductID)
-WHERE p.ProductName = 'Chocolade';
-----
-
-//sqltable
-
-The graph model (have a look) is much simpler, as we don't need join tables, and expressing connections as graph patterns, is easier to read too.
-
-[source, cypher]
-----
-MATCH (p:Product {productName:"Chocolade"})<-[:PRODUCT]-(:Order)<-[:PURCHASED]-(c:Customer)
-RETURN distinct c.companyName;
-----
-
-// table
-
-=== New Customers without Orders yet
-
-_Outer Joins, Aggregation_
-
-If we turn the question around and ask "What have I bought and paid in total?", the JOIN stays the same, only the filter expression changes.
-Except if we have customers without any orders and still want to return them.
-Then we have to use OUTER joins to make sure that results are returned even if there were no matching rows in other tables.
-
-[source, plsql]
-----
-SELECT p.ProductName, sum(od.UnitPrice * od.Quantity) AS Volume
-FROM customers AS c
-LEFT OUTER JOIN orders AS o ON (c.CustomerID = o.CustomerID)
-LEFT OUTER JOIN order_details AS od ON (o.OrderID = od.OrderID)
-LEFT OUTER JOIN products AS p ON (od.ProductID = p.ProductID)
-WHERE c.CompanyName = 'Drachenblut Delikatessen'
-GROUP BY p.ProductName
-ORDER BY Volume DESC;
-----
-
-//sqltable
-
-In our Cypher query, the MATCH between customer and order becomes an OPTIONAL MATCH, which is the equivalent of an OUTER JOIN.
-
-[source, cypher]
-----
-MATCH (c:Customer {companyName:"Drachenblut Delikatessen"})
-OPTIONAL MATCH (p:Product)<-[pu:PRODUCT]-(:Order)<-[:PURCHASED]-(c)
-RETURN p.productName, toInteger(sum(pu.unitPrice * pu.quantity)) AS volume
-ORDER BY volume DESC;
-----
-
-// table
-
-=== Top-Selling Employees
-
-_Aggregation, Grouping_
-
-In the previous query we sneaked in a bit of aggregation.
-By summing up product prices and ordered quantities, we provided an aggregated view per product for this customer.
-
-You can use aggregation functions like `sum, count, avg, max` both in SQL and Cypher.
-In SQL, aggregation is explicit so you have to provide all grouping keys again in the `GROUP BY` clause.
-If we want to see our top-selling employees.
-
-[source, plsql]
-----
-SELECT e.EmployeeID, count(*) AS Count
-FROM Employee AS e
-JOIN Order AS o ON (o.EmployeeID = e.EmployeeID)
-GROUP BY e.EmployeeID
-ORDER BY Count DESC LIMIT 10;
-----
-
-//sqltable
-
-In Cypher grouping for aggregation is implicit.
-As soon as you use the first aggregation function, all non-aggregated columns automatically become grouping keys.
-
-[source, cypher]
-----
-MATCH (:Order)<-[:SOLD]-(e:Employee)
-RETURN e.name, count(*) AS cnt
-ORDER BY cnt DESC LIMIT 10
-----
-
-//table
-
-=== Employee Territories
-
-_Collecting Master-Detail Queries_
-
-In SQL there is a particularly dreaded kind of query - master detail information.
-You have one main entity (master, head, parent) and many dependent ones (detail, position, child).
-Usually you either query it by joining both and returning the master data multiple times (once for each detail), or by only fetching the primary key of the master and then pulling all detail rows via that foreign key.
-
-For instance if we look at the employees per territory, then the territory information is returned for each employee.
-
-[source, plsql]
-----
-SELECT e.LastName, et.Description
-FROM Employee AS e
-JOIN EmployeeTerritory AS et ON (et.EmployeeID = e.EmployeeID)
-JOIN Territory AS t ON (et.TerritoryID = t.TerritoryID);
-----
-
-//sqltable
-
-In Cypher we can either return the structure like in SQL.
-Or we can choose to use the `collect` aggregation function, which aggregates values into a collection (list,array).
-So we only return one row per parent, containing an inlined collection of child values.
-This also works for nested values.
-
-[source, cypher]
-----
-MATCH (t:Territory)<-[:IN_TERRITORY]-(e:Employee)
-RETURN t.description, collect(e.lastName);
-----
-
-//table
-
-=== Product Categories
-
-_Hierarchies and Trees, Variable Length Joins_
-
-If you have to express category-, territory- or organizational hierarchies in SQL then you model it usually with a self-join via a foreign key from child to parent.
-Adding data is not problematic, as are single level queries (get all children for this parent).
-As soon as you get into multi-level queries, the number of joins explodes, especially if your level depth not fixed.
-
-Taking the example of the product categories, we have to decide upfront up to how many levels of categories we want to query.
-We will tackle only three potential levels here (which means 1+2+3 = 6 self-joins of the ProductCategory table).
-
-// TODO check
-
-[source, plsql]
-----
-SELECT p.ProductName
-FROM Product AS p
-JOIN ProductCategory pc ON (p.CategoryID = pc.CategoryID AND pc.CategoryName = "Dairy Products")
-
-JOIN ProductCategory pc1 ON (p.CategoryID = pc1.CategoryID
-JOIN ProductCategory pc2 ON (pc2.ParentID = pc2.CategoryID AND pc2.CategoryName = "Dairy Products")
-
-JOIN ProductCategory pc3 ON (p.CategoryID = pc3.CategoryID
-JOIN ProductCategory pc4 ON (pc3.ParentID = pc4.CategoryID)
-JOIN ProductCategory pc5 ON (pc4.ParentID = pc5.CategoryID AND pc5.CategoryName = "Dairy Products")
-;
-----
-
-//sqltable
-
-Cypher is able to express hierarchies of any depth just with the appropriate relationships.
-Variable levels are represented by variable length paths, which are denoted by a star `*` after the relationship-type and optional limits (`min..max`).
-
-[source, cypher]
-----
-MATCH (p:Product)-[:CATEGORY]->(l:ProductCategory)-[:PARENT*0..]-(:ProductCategory {name:"Dairy Products"})
-RETURN p.name
-----
-
-//table
-
-There is much more to Cypher than shown in this short section.
-Hopefully the comparison with SQL helped you to understand the concepts.
-If you are intrigued by the possibilities and want to try and learn more, just link:/download[install Neo4j] on your machine and use the links to our different Cypher learning resources.
-
-Learn Cypher:
-
-* xref:index.adoc[Cypher Intro Guide]
-* link:/online-course/[Interactive Online Course]
-* {cyphermanual}[Cypher Manual^]
-* link:/docs/cypher-refcard/current/[Reference Card^]
-
-////
-
-MATCH (choc:Product {ProductName:'Chocolade'})<-[:PRODUCT]-(:Order)<-[:SOLD]-(employee),
-      (employee)-[:SOLD]->()-[:PRODUCT]->(other:Product)
-RETURN employee.EmployeeID, other.ProductName, count(*) as count
-ORDER BY count DESC
-LIMIT 5
-
-also introduce order by, limit
-
-==== Find all Products in Category X
-
-discuss graph patterns with relationships
-
-==== Hierarchy
-
-==== Varlength path
-
-==== With
-
-==== Shortest Path
-
-=== Create a new product
-
-=== create an order
-
-////
-
-[#cypher-resources]
-== Resources
-* link:/developer/graph-db-vs-rdbms/["Data Model Transformation",role=guide]
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/index.adoc b/modules/cypher/pages/index.adoc
deleted file mode 100644
index d3ec7a61..00000000
--- a/modules/cypher/pages/index.adoc
+++ /dev/null
@@ -1,123 +0,0 @@
-= Cypher Query Language
-:author: Jennifer Reif
-:category: cypher
-:tags: cypher, queries, graph-queries, query-language, learn-cypher
-:page-aliases: ROOT:cypher-query-language.adoc, cypher-query-language.adoc
-:page-pagination:
-:page-newsletter: true
-:page-ad-overline-link: https://neo4j.com/cloud/aura/?ref=developer-guides
-:page-ad-overline: Neo4j Aura
-:page-ad-title: Follow Along and Learn While Doing
-:page-ad-description: Free forever, no credit card required.
-:page-ad-link: https://neo4j.com/cloud/aura/?ref=developer-guides
-:page-ad-underline-role: button
-:page-ad-underline: Start on AuraDB
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#about-cypher]
-Cypher is Neo4j's graph query language that lets you retrieve data from the graph.
-It is like SQL for graphs, and was inspired by SQL so it lets you focus on what data you want out of the graph (not how to go get it).  It is the easiest graph language to learn by far because of its similarity to other languages, and intuitiveness.
-
-image:{img}sample-cypher.png[role="popup-link"]
-
-// Note $$->$$ is used as a special escape to prevent asciidoc from replacing -> with arrow in unicode
-Cypher is unique because it provides a visual way of matching patterns and relationships.
-Cypher uses an ASCII-art type of syntax where `$$(nodes)-[:ARE_CONNECTED_TO]->(otherNodes)$$` using rounded brackets for circular
-`(nodes)`, and `$$-[:ARROWS]->$$` for relationships.
-When you write a query, you draw a graph pattern through your data.
-
-Neo4j users use Cypher to construct expressive and efficient queries to do any kind of create, read, update, or
-delete (CRUD) on their graph, and Cypher is the primary interface for Neo4j.
-
-[IMPORTANT]
-Like Neo4j itself, Cypher is open source!  link:http://openCypher.org[The openCypher project^] provides all of the specs needed.
-Cypher is backed by a number of companies all of which benefit from cypher.
-
-[#cypher-intro]
-== Introduction to Cypher
-
-Once you have an AuraDB database, you can use the `:play cypher` command inside of Neo4j Browser to get started.
-
-image::{img}play-cypher-command.png[role="popup-link"]
-
-Neo4j's developer pages cover the basics of the language, which you can explore by topic area below, starting with basic material, and building up towards more complex material.
-
-[cols="1,1"]
-|===
-|xref:intro-cypher.adoc[Getting Started with Cypher]
-|Learn what cypher is, and important concepts for graph
-
-|xref:querying.adoc[Querying with Cypher]
-|How to write Cypher code to query the graph
-
-|xref:updating.adoc[Updating with Cypher]
-|How to change data in a graph
-
-|xref:filtering-query-results.adoc[Filtering Cypher Query Results]
-|Trim results by criteria, focusing only on the data you want.
-
-|xref:controlling-query-processing.adoc[Controlling Query Processing]
-|Counting results, grouping data by values, finding minimum, maximums, and aggregations.
-
-|xref:dates-datetimes-durations.adoc[Cypher Dates]
-|Working with temporal information
-
-|xref:subqueries.adoc[Cypher Subqueries]
-|Running queries within queries for more accurate and focused results
-
-|xref:style-guide.adoc[Cypher Style Guide]
-|Recommended style for writing easy to read, easy to maintain Cypher queries
-
-|xref:guide-sql-to-cypher.adoc[From SQL to Cypher]
-|Similarities and Differences between SQL and Cypher
-
-|xref:procedures-functions.adoc[User-Defined Procedures and Functions] +
-link:/labs/apoc/[APOC: Neo4j's Standard Utility Library^]
-|Extending Cypher with powerful additional functionality for special scenarios
-|===
-
-image::{img}cypher_learning.jpg[role="popup-link"]
-
-[#recommendation-engine]
-== Tutorial: Build a Recommendation Engine
-
-With Cypher structure and syntax covered in the sections above, you can dive into building your own recommendation engine to use graph data and Cypher to recommend movies, colleagues, cuisines, and more.
-
-xref:guide-build-a-recommendation-engine.adoc[Tutorial: Build a Recommendation Engine,role=more information] will walk through using queries and filtering that takes advantage of the relationships in a graph in order to lend insight into habits and hidden connections and provide valuable recommendations.
-
-////
-== Cypher Examples
-//include a guide on different examples or a tutorial?
-////
-
-== Community Help
-
-If you have any questions, the Neo4j Community is the place to go to ask.
-Join a group of professionals using graphs to solve all kinds of interesting problems!
-
-link:https://community.neo4j.com/?ref=guides[Join the Neo4j Community,role=button^].
-
-[#cypher-resources]
-== Cypher Resources
-
-Find out where else you can learn Cypher or increase your depth of knowledge from experts and solutions.
-There are a variety of training opportunities, blogs, videos, and more for taking the next steps in your Cypher (and Neo4j) journey!
-
-xref:resources.adoc[Cypher Resources,role=more information]
-
-
-[.discrete.ad]
-== link:https://graphacademy.neo4j.com/?ref=guides[Learn with GraphAcademy^]
-
-image::https://graphacademy.neo4j.com/courses/cypher-fundamentals/badge/[float=left]
-
-[.discrete]
-=== link:https://graphacademy.neo4j.com/courses/cypher-fundamentals/?ref=guides[Cypher Fundamentals^]
-
-This course teaches you the essentials of using Cypher, Neo4j’s powerful query language, in as little time as possible, with videos, quizzes and hands-on exercises.
-
-
-link:https://graphacademy.neo4j.com/courses/cypher-fundamentals/?ref=guides[Learn Cypher with GraphAcademy^,role=button]
diff --git a/modules/cypher/pages/intro-cypher.adoc b/modules/cypher/pages/intro-cypher.adoc
deleted file mode 100644
index 17d23a61..00000000
--- a/modules/cypher/pages/intro-cypher.adoc
+++ /dev/null
@@ -1,241 +0,0 @@
-= Getting Started with Cypher
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: cypher
-:tags: cypher, queries, graph-queries, syntax, patterns, keywords, aliases
-:description: This guide explains the basic concepts of Cypher, Neo4j's graph query language. You should be able to read and understand Cypher queries after finishing this guide.
-:page-aliases: ROOT:cypher-basics-i.adoc
-:page-pagination:
-:page-newsletter: true
-:page-ad-overline-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-overline: Neo4j GraphAcademy
-:page-ad-title: Cypher Fundamentals
-:page-ad-description: Learn Cypher in this free, hands-on course
-:page-ad-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-underline-role: button
-:page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with xref:ROOT:get-started.adoc[graph database] concepts and the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-
-[role=expertise {level}]
-{level}
-
-[#why-cypher]
-== Why Cypher?
-
-We already know that Neo4j's property graph model is composed of nodes and relationships, which may also have properties associated with them.
-However, nodes and relationships are the simple components that build the most valuable and powerful piece of the property graph model - the pattern.
-Patterns are comprised of node and relationship elements and can express simple or complex traversals and paths.
-
-Pattern recognition is fundamental to the way that the brain works.
-Because of this, humans are very good at working with patterns (think of visual diagrams or even memory-matching games).
-Cypher is also heavily based on patterns and is designed to recognize various versions of these patterns in data, making it a simple and logical language for users to learn.
-
-[#cypher-syntax]
-== Cypher Syntax
-
-The video below walks through some background on Cypher, basic syntax, and some intermediate examples.
-The concepts in the video are discussed in the paragraphs below, as well as in upcoming guides.
-
-++++
-
    
-
-
    
-++++
-
-Since Cypher is designed to be human-readable, it's construct is based on English prose and iconography to make syntax visual and easily understood.
-For example, take a look at the simple graph data in the image below.
-How would you represent this data in English?
-_NOTE: the answer is in the paragraph below_
-
-image::{img}cypher_graph_v1.jpg[role="popup-link"]
-
-
-[TIP]
---
-Jennifer likes Graphs. Jennifer is friends with Michael. Jennifer works for Neo4j.
---
-
-Cypher syntax will build upon this English-language structure we just created.
-In the next section, we will see exactly how to write this example in Cypher.
-
-=== Cypher Comments
-
-As you work through this guide, you will see comments in the Cypher code to help explain the syntax or what a query is doing.
-Comments in Cypher are the same as in many programming languages.
-You can add comments by starting a line with `//` and putting text after the slashes.
-Just like in other languages, starting the line with two forward slashes will mean that anything on that line will become a comment.
-
-[TIP]
---
-This is especially helpful to use in Neo4j Browser when saving queries.
-If you add a comment before the query, the comment automatically becomes the name of the saved query!
---
-
-[#cypher-nodes]
-== Representing Nodes in Cypher
-
-Since Cypher uses ASCII-Art for patterns, we need a visual way to represent each component of our pattern above.
-We know that the main components of the property graph model are nodes and relationships.
-Remember that nodes are the data entities in your graph and that you can often identify nodes by finding the nouns or objects in your data model.
-In our example, `Jennifer`, `Michael`, `Graphs`, and `Neo4j` are our nodes.
-
-image::{img}cypher_graph_nodes.jpg[role="popup-link"]
-
-To depict nodes in Cypher, we surround the node with parentheses, e.g. `(node)`.
-Notice how the parentheses look similar to the circles that the visual representation uses for nodes in our data model.
-
-=== Node Variables
-
-If we later want to refer to the node, we can give it a variable like `(p)` for person or `(t)` for thing.
-In real-world queries, we might use longer, more expressive variable names like `(person)` or `(thing)`.
-Just like in programming language variables, you can name your variables what you want and reference them by that same name later in a query.
-
-If the node is not relevant to your return results, you can specify an anonymous node using empty parentheses `()`.
-This means that you will not be able to return this node later in the query.
-
-=== Node Labels
-
-If you remember from the property graph data model, you can also group similar nodes together by assigning a node label.
-Labels are kind of like tags and allow you to specify certain types of entities to look for or create.
-In our example, `Person`, `Technology`, and `Company` are the labels.
-
-You can kind of think of this like telling SQL which table to look for the particular row.
-Just like to tell SQL to query a person's information from a `Person` or `Employee` or `Customer` table, you can also tell Cypher to only check those labels for that information.
-This helps Cypher distinguish between entities and optimize execution for your queries.
-It is always better to use node labels in your queries, where possible.
-
-[NOTE]
---
-If you do not specify a label for Cypher to filter out non-matching node categories, the query will check all of the nodes in the database!
-As you can imagine, this would be cumbersome if you had a very large graph.
---
-
-=== Example: Nodes in Cypher
-
-Using our graph example above, let's see how we could specify our nodes.
-
-[source,cypher]
-----
-()                  //anonymous node (no label or variable) can refer to any node in the database
-(p:Person)          //using variable p and label Person
-(:Technology)       //no variable, label Technology
-(work:Company)      //using variable work and label Company
-----
-
-[#cypher-relationships]
-== Representing Relationships in Cypher
-
-To fully utilize the power of a graph database, we also need to express the relationships between our nodes.
-Relationships are represented in Cypher using an arrow `+-->+` or `+<--+` between two nodes.
-Notice how the syntax looks like the arrows and lines connecting our nodes in the visual representation.
-Additional information, such as how nodes are connected (relationship type) and any properties pertaining to the relationship, can be placed in square brackets inside of the arrow.
-
-In our example, the lines with `LIKES`, `IS_FRIENDS_WITH`, and `WORKS_FOR` between nodes are our relationships.
-
-image::{img}cypher_graph_rels.jpg[role="popup-link"]
-
-Undirected relationships are represented with no arrow and just two dashes `+--+`.
-This means that the relationship can be traversed in either direction.
-While a direction *must* be inserted to the database, it can be matched with an undirected relationship where Cypher ignores any particular direction and retrieves the relationship and connected nodes, no matter what the physical direction is.
-This allows the queries to be flexible and not force the user to know the physical direction of the relationship stored in the database.
-
-[NOTE]
---
-If data is stored with one relationship direction, and a query specifies the wrong direction, Cypher will not return any results.
-In these cases where you may not be sure of direction, it is better to use an undirected relationship and retrieve some results.
-
-[source,cypher]
-----
-//data stored with this direction
-CREATE (p:Person)-[:LIKES]->(t:Technology)
-
-//query relationship backwards will not return results
-MATCH (p:Person)<-[:LIKES]-(t:Technology)
-
-//better to query with undirected relationship unless sure of direction
-MATCH (p:Person)-[:LIKES]-(t:Technology)
-----
---
-
-=== Relationship Types
-
-Relationship types categorize and add meaning to a relationship, similar to how labels group nodes.
-In our property graph data model, relationships show how nodes are connected and related to each other.
-You can usually identify relationships in your data model by looking for actions or verbs.
-
-You can specify any type of relationship you want between nodes, but we recommend good naming conventions using verbs and actions.
-Poor relationship type names make it more difficult to both read and write Cypher (remember, it should sound like English!).
-
-For example, let us look at the relationship types from our example graph.
-
-* `[:LIKES]` - makes sense when we put nodes on either side of the relationship (Jennifer LIKES Graphs)
-* `[:IS_FRIENDS_WITH]` - makes sense when we put nodes with it (Jennifer IS_FRIENDS_WITH Michael)
-* `[:WORKS_FOR]` - makes sense with nodes (Jennifer WORKS_FOR Neo4j)
-
-=== Relationship Variables
-
-Just as we did with nodes, if we want to refer to a relationship later in a query, we can give it a variable like `[r]` or `[rel]`.
-We can also use longer, more expressive variable names like `[likes]` or `[knows]`.
-If you do not need to reference the relationship later, you can specify an anonymous relationship using two dashes `+--+`, `+-->+`, `+<--+`.
-
-As an example, you could use either `+-[rel]->+` or `+-[rel:LIKES]->+` and call the `rel` variable later in your query to reference the relationship and its details.
-
-[NOTE]
---
-If you forget the colon in front of a relationship type like this `+-[LIKES]->+`, it represents a variable (not a relationship type).
-Since no relationship type declared, Cypher will search all types of relationships.
---
-
-[#cypher-properties]
-== Node or Relationship Properties
-
-We have talked about how to write Cypher for nodes, relationships, and labels.
-The last piece of our property graph data model is for properties.
-Remember that properties are name-value pairs that provide additional details to our nodes and relationships.
-
-To represent these in Cypher, we can use curly braces within the parentheses of a node or the brackets of a relationship.
-The name and value of the property then go inside the curly braces.
-Our example graph has both a node property (`name`) and a relationship property (`since`).
-
-* Node property: `(p:Person {name: 'Jennifer'})`
-* Relationship property: `+-[rel:IS_FRIENDS_WITH {since: 2018}]->+`
-
-image::{img}cypher_graph_props.jpg[role="popup-link"]
-
-Properties can have values with a variety of data types.
-To see the full list that Cypher offers, see the manual section on link:/docs/cypher-manual/current/syntax/values/[values and types^].
-
-[#cypher-patterns]
-== Patterns in Cypher
-
-Nodes and relationships make up the building blocks for graph patterns.
-These building blocks can come together to express simple or complex patterns.
-Patterns are the most powerful capability of graphs.
-In Cypher, they can be written as a continuous path or separated into smaller patterns and tied together with commas.
-
-To show a pattern in Cypher, we need to combine the node and relationship syntaxes we have learned so far.
-Let us use our example of `Jennifer likes Graphs`.
-
-In Cypher, this pattern would look like the code below.
-
-[source, cypher]
-----
-(p:Person {name: "Jennifer"})-[rel:LIKES]->(g:Technology {type: "Graphs"})
-----
-
-This bit of Cypher tells the pattern we want, but it does not tell whether we want to find that existing pattern or insert it as a new pattern.
-To tell Cypher what we want it to do with the pattern, we need to add some keywords.
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/procedures-functions.adoc b/modules/cypher/pages/procedures-functions.adoc
deleted file mode 100644
index 603d15db..00000000
--- a/modules/cypher/pages/procedures-functions.adoc
+++ /dev/null
@@ -1,301 +0,0 @@
-= User Defined Procedures and Functions
-:level: Intermediate
-:page-level: Intermediate
-:author: Neo4j
-:category: cypher
-:tags: cypher, queries, extend-cypher, procedures, functions, custom-development
-:description: This guide explains how to use, create and deploy user defined procedures and functions, the extension mechanism of Cypher, Neo4j's query language. It also covers existing, widely used procedure libraries
-:page-pagination:
-:page-aliases: ROOT:procedures-functions.adoc
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/procedures-functions/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should have learned to read and write statements in xref:index.adoc[Cypher] and seen the need for additional capabilities.
-
-[role=expertise {level}]
-{level}
-
-[#cypher-extension]
-== Extending Cypher
-
-Cypher is a quite powerful and expressive language, with first class graph pattern and collection support.
-But sometimes you need to do more than it currently offers, like additional graph algorithms, parallelization or custom conversions.
-
-That's why Neo4j and Cypher can be extended with User Defined Procedures and Functions.
-Neo4j itself provides and utilizes custom procedures.
-Many of the monitoring, introspection and security features exposed by Neo4j-Browser are implemented using procedures.
-
-image::{img}procedures-functions-bolt.jpg[width=600]
-
-[#procedures-functions]
-== What are Procedures and Functions?
-
-* Functions are simple computations / conversions and return a single value
-* Functions can be used in any expression or predicate
-
-* Procedures are more complex operations and generate streams of results.
-* Procedures must be used within the `CALL` clause and `YIELD` their result columns
-* They can generate, fetch or compute data to make it available to later processing steps in your Cypher query
-
-[#cypher-list-extension]
-== Listing & Using Functions and Procedures
-
-There are a {opsmanual}/reference/procedures/[number of built in procedures^], two of which are used to list available functions and procedures.
-
-Run the following statements along to get a hang of the usage and see their results.
-
-[source,cypher]
-----
-CALL dbms.procedures()
-----
-
-Call `dbms.functions()` to list functions.
-
-Each procedure returns one or more columns of data.
-With `yield` these columns can be selected and also aliased and are then available in your Cypher statement.
-
-[source,cypher]
-----
-CALL dbms.procedures()
-YIELD name, signature, description as text
-WHERE name STARTS WITH 'db.'
-RETURN * ORDER BY name ASC
-----
-
-Of course you can also process the result columns with other Cypher clauses.
-Here we group them by package.
-
-[source,cypher]
-----
-CALL dbms.procedures()
-YIELD name, signature, description
-WITH split(name,".") AS parts
-RETURN parts[0..-1] AS package,  count(*) AS count,
-       collect(parts[-1]) AS names
-ORDER BY count DESC
-----
-
-[options=header]
-|===
-|package             |count  |names
-|["dbms","security"] |16     |["activateUser","addRoleToUser","changePassword",....]
-|["apoc","refactor"] |11     |["categorize","cloneNodes","from"....]
-|["apoc","load"]     |9      |["csv","driver","jdbc","jdbcParams","json","jsoe"]
-|["db"]              |9      |["awaitIndex","constraints","indexes","labels",..,"schema"]
-|["dbms"]            |9      |["components","functions","queries","procedures",...]
-|===
-
-As of Neo4j 3.1, all functions available are directly part of the Cypher implementation, so User Defined Functions would only come from installed libraries.
-
-You can take any procedure library and deploy it to your server to make additional procedures and functions available.
-
-Please also have a look at the link:/docs/java-reference/current/extending-neo4j/procedures-and-functions/introduction/[procedure section in the Neo4j Manual^].
-
-[#deploy-extension]
-== Deploying Procedures & Functions
-
-If you built your own procedures or downloaded them from an community project, they are packaged in a jar-file.
-You can copy that file into the `$NEO4J_HOME/plugins` directory of your Neo4j server and restart.
-
-[WARNING]
-*A word of caution.*
-As procedures and functions use the low level Java API they can access all Neo4j internals as well as the file system and machine.
-That's why you should know which procedures you deploy and why.
-Only install procedures from trusted sources.
-If they are open source, check their source-code and best build them yourself.
-
-[IMPORTANT]
-Certain procedures and functions are available for self-managed Neo4j Enterprise Edition and Community Edition.  Custom code
-described in this section is not compatible with link:{aura_signup}[AuraDB]
-
-[#procedure-function-gallery]
-== Procedure and Function Gallery
-
-In our link:/labs/[Neo4j Labs projects], we provide an impressive set of libraries built by our community and staff.
-Check it out to see what's already there.
-Many of your needs will already be covered by those, for example:
-
-* index operations
-* database/api integration
-* graph refactorings
-* import and export
-* spatial index lookup
-* rdf import and export
-* and many more
-
-Here are two cool examples of what you can do:
-
-A procedure to load data from another database:
-
-[source,cypher]
------
-WITH "jdbc:mysql://localhost:3306/northwind?user=root" as url
-CALL apoc.load.jdbc(url,"products") YIELD row
-RETURN row
-ORDER BY row.UnitPrice DESC
-LIMIT 20
------
-
-image::https://raw.githubusercontent.com/neo4j-contrib/neo4j-apoc-procedures/3.2/docs/img/apoc-load-jdbc.jpg[]
-
-Functions to format and parse timestamps of different resolutions:
-
-[source,cypher]
-----
-RETURN apoc.date.format(timestamp()) as time,
-       apoc.date.format(timestamp(),'ms','yyyy-MM-dd') as date,
-       apoc.date.parse('13.01.1975','s','dd.MM.yyyy') as unixtime,
-       apoc.date.parse('2017-01-05 13:03:07') as millis
-----
-
-[options="header,autowidth"]
-|===
-|time                 |date        |unixtime  |millis
-|"2017-01-05 13:06:39"|"2017-01-05"|158803200 |1483621387000
-|===
-
-[#custom-extension]
-== Developing your own Procedures and Functions
-
-// === Writing your first Function
-
-You can find details on writing and testing procedures in the link:/docs/java-reference/current/extending-neo4j/procedures-and-functions/introduction/[Neo4j Manual^].  
-The https://github.com/neo4j-examples/neo4j-procedure-template[example GitHub repository] contains detailed documentation and comments that you can clone directly and use as a starting point.
-
-Here are just some initial tips:
-
-User-defined functions are simpler, so let's start with them:
-
-* `@UserFunction` are annotated, public Java methods in a class
-* their default name is package-name.method-name
-* they return a single value
-* are read only
-
-User defined procedures are similar:
-
-* `@Procedure` annotated, Java methods
-* with an additional `mode` attribute (`READ, WRITE, DBMS`)
-* return a Java 8 `Stream` of simple objects with `public` fields
-* these fields names are turned into result columns available for `YIELD`
-
-These things are valid for both:
-
-* take `@Name` annotated parameters (with optional default values)
-* can use an injected `@Context public GraphDatabaseService`
-* run within transaction of the Cypher statement
-* supported types for parameters and results are: `Long, Double, Boolean, String, Node, Relationship, Path, Object`
-
-////
-User-defined functions are simpler, so let's look at one here:
-
-* `@UserFunction` annotated, named Java Methods
-** default name is `class package + "." +  method-name`
-* take `@Name`'ed parameters (with optional default values)
-* return a single value
-* are read only
-* can use `@Context` injected `GraphDatabaseService` etc
-* run within Transaction of the Cypher Statement
-
-.simple user defined `create.uuid` function in Java
-[source,java]
-----
-@UserFunction("create.uuid")
-@Description("creates an UUID (universally unique id)")
-public String uuid() {
-   return UUID.randomUUID().toString();
-}
-----
-////
-
-////
-cp $GROOVY_HOME/lib/groovy-2.*.jar $NEO4J_HOME/plugins/
-$GROOVY_HOME/groovyc function.groovy && jar cf $NEO4J_HOME/plugins/uuid.jar UDF.class
-
-@Grab(value="org.neo4j:neo4j:3.1.0-BETA1",initClass=false)
-
-class UDF {
-   @UserFunction("create.uuid")
-   @Description("creates an UUID")
-   def String uuid() { UUID.randomUUID().toString() }
-}
-////
-
-////
-.use the function like this
-[source,cypher]
-----
-CREATE (p:Person {id: create.uuid(), name: $name})
-----
-
-=== Testing the Function
-
-The Neo4j testing library `neo4j-harness` enables you to spin up a Neo4j server, provide fixtures for data setup and register your functions and procedures.
-
-You then call and test test the function via the bolt - `neo4j-java-driver`.
-
-[source,java]
-----
-@Rule
-public Neo4jRule neo4j = new Neo4jRule()
-                         .withFunction( UUIDs.class );
-...
-
-try( Driver driver = GraphDatabase.driver( neo4j.boltURI() , config ) {
-    Session session = driver.session();
-    String uuid = session.run("RETURN create.uuid() AS uuid")
-                         .single().get( 0 ).asString();
-    assertThat( uuid,....);
-}
-----
-
-=== Writing a Procedure
-
-User defined procedures are similar:
-
-* `@Procedure` annotated, Java methods
-* with an additional `mode` attribute (`Read, Write, Dbms`)
-* return a `Stream` of value objects (DTO) with `public` fields
-* value object fields are turned into result columns to be `YIELD`ed
-
-.Expose dijkstra algoritm from the Java API to Cypher
-[source,java]
-----
-@Procedure(mode = Write)
-@Description("apoc.algo.dijkstra(startNode, endNode, 'KNOWS', 'distance') YIELD path," +
-       " weight - run dijkstra with relationship property name as cost function")
-public Stream dijkstra(
-       @Name("startNode") Node startNode,
-       @Name("endNode") Node endNode,
-       @Name("type") String type,
-       @Name("costProperty") String costProperty) {
-
-
-   PathFinder algo = GraphAlgoFactory.dijkstra(
-           PathExpanders.forType(RelationshipType.withName(type)),
-           costProperty);
-   Iterable allPaths = algo.findAllPaths(startNode, endNode);
-   return Iterables.asCollection(allPaths).stream()
-           .map(WeightedPathResult::new);
-}
-
-public static class WeightedPathResult {
-   public final Path path;
-   public final double weight;
-   public WeightedPathResult(WeightedPath wp) { this.path = wp; this.weight = wp.weight(); }
-}
-----
-
-Use a build tool (like maven, gradle, ant) to package your code into a jar-file and copy that into `$NEO4J_HOME/plugins`
-Make sure required dependencies are added as well, either to your jar or the plugins directory.
-////
-
-include::partial$help.adoc[]
\ No newline at end of file
diff --git a/modules/cypher/pages/querying.adoc b/modules/cypher/pages/querying.adoc
deleted file mode 100644
index a3eb447f..00000000
--- a/modules/cypher/pages/querying.adoc
+++ /dev/null
@@ -1,181 +0,0 @@
-= Querying with Cypher
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: cypher
-:tags: cypher, queries, graph-queries, syntax, patterns, keywords, aliases
-:description: This guide explains the basic concepts of Cypher, Neo4j's graph query language. You should be able to read and understand Cypher queries after finishing this guide.
-:page-aliases: ROOT:cypher-basics-ii.adoc
-:page-pagination:
-:page-includedriver: true
-:page-newsletter: true
-:page-ad-overline-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-overline: Neo4j GraphAcademy
-:page-ad-title: Cypher Fundamentals
-:page-ad-description: Learn Cypher in this free, hands-on course
-:page-ad-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-underline-role: button
-:page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with xref:ROOT:get-started.adoc[graph database] concepts and the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-
-[role=expertise {level}]
-{level}
-
-
-[#cypher-keywords]
-== Cypher Keywords
-
-Just like with most programming languages, there are a few words in Cypher reserved for specific actions in parts of a query.
-We need to be able to create, read, update, or delete data in Neo4j, and keywords help us accomplish that functionality.
-Let us look more in detail at two common keywords (more will be covered in upcoming guides).
-
-=== MATCH
-
-The `MATCH` keyword in Cypher is what searches for an existing node, relationship, label, property, or pattern in the database.
-If you are familiar with SQL, `MATCH` works pretty much like `SELECT` in SQL.
-
-You can find all node labels in the database, search for a particular node, find all the nodes with a particular relationship, look for patterns of nodes and relationships, and much more using `MATCH`.
-
-=== RETURN
-
-The `RETURN` keyword in Cypher specifies what values or results you might want to return from a Cypher query.
-You can tell Cypher to return nodes, relationships, node and relationship properties, or patterns in your query results.
-`RETURN` is not required when doing write procedures, but is needed for reads.
-
-The node and relationship variables we discussed earlier become important when using `RETURN`.
-In order to bring back nodes, relationships, properties, or patterns, you need to have variables specified in your `MATCH` clause for the data you want to return.
-
-[#cypher-examples]
-== Cypher Examples
-
-Let us look at some examples of the syntax we have learned so far using `MATCH` and `RETURN` keywords.
-Each example will start with an explanation of what we are trying to achieve and have an image below of the results of the query run in Neo4j Browser.
-
-* *Example 1:* Find the labeled `Person` nodes in the graph.
-Note that we must use a variable like `p` for the `Person` node if we want retrieve the node in the `RETURN` clause.
-
-[source, cypher, role=runnable editable graph]
-----
-MATCH (p:Person)
-RETURN p
-LIMIT 1
-----
-
-// image::{img}cypher_example1_labelvar.jpg[role="popup-link"]
-
-
-* *Example 2:* Find `Person` nodes in the graph that have a name of 'Tom Hanks'.
-Remember that we can name our variable anything we want, as long as we reference that same name later.
-
-[source, cypher, role=runnable editable graph]
-----
-MATCH (tom:Person {name: 'Tom Hanks'})
-RETURN tom
-----
-
-// image::{img}cypher_example2_labelprop.jpg[role="popup-link"]
-
-
-* *Example 3:* Find which `Movie`s Tom Hanks has directed.
-
-Explanation: we know we need to find Tom Hanks' `Person` node, and we need to find the `Movie` nodes he is connected to.
-To do that, we need to follow the `DIRECTED` relationship from Tom Hanks' `Person` node to the `Movie` node.
-We have also specified a label of `Movie` so that the query will only look at nodes with that label.
-Since we only care about returning the movie in this query, we need to give that node a variable (`movie`) but do not need to give variables for the `Person` node or `DIRECTED` relationship.
-
-[source, cypher, role=runnable editable graph]
-----
-MATCH (:Person {name: 'Tom Hanks'})-[:DIRECTED]->(movie:Movie)
-RETURN movie
-----
-
-// image::{img}cypher_example3_returnnode.jpg[role="popup-link"]
-
-
-* *Example 4:* Find which `Movie` Tom Hanks has directed, but this time, return only the title of the movie.
-
-Explanation: this query is very similar to Example 3.
-Example 3 returned the entire `Movie` node with all its properties.
-For this example, we still need to find Tom's movies, but now we only care about their titles.
-We will need to access the node's `title` property using the syntax `variable.property` to return the name value.
-
-[source, cypher, role=runnable editable]
-----
-MATCH (:Person {name: 'Tom Hanks'})-[:DIRECTED]->(movie:Movie)
-RETURN movie.title
-----
-
-// image::{img}cypher_example4_returnprop.jpg[role="popup-link"]
-
-[#cypher-aliases]
-== Aliasing Return Values
-
-Not all properties are simple like our `movie.title` example above.
-Some properties have poor names due to property length, multi-word descriptions, developer jargon, and other shortcuts.
-These naming conventions can be difficult to read, especially if they end up on reports and other user-facing interfaces.
-
-.Poorly-named Properties
-[source,cypher,role=runnable]
-----
-//poorly-named property
-MATCH (tom:Person {name:'Tom Hanks'})-[rel:DIRECTED]-(movie:Movie)
-RETURN tom.name, tom.born, movie.title, movie.released
-----
-
-Just like with SQL, you can rename return results by using the `AS` keyword and aliasing the property with a cleaner name.
-We can look at a mocked-up example to list a customer's orders and the number of items in the order.
-
-.Cleaner Results with aliasing
-[source,cypher,role=runnable editable]
-----
-//cleaner printed results with aliasing
-MATCH (tom:Person {name:'Tom Hanks'})-[rel:DIRECTED]-(movie:Movie)
-RETURN tom.name AS name, tom.born AS `Year Born`, movie.title AS title, movie.released AS `Year Released`
-----
-
-// .Results Without Aliases:
-// image:{img}cypher_without_aliases.jpg[role="popup-link"]
-
-// .Results With Aliases:
-// image:{img}cypher_with_aliases.jpg[role="popup-link"]
-
-[NOTE]
---
-You can specify return aliases that have spaces by using the backtick character before and after the alias (movie.released AS `Year Released`).
-If you do not have an alias that contains spaces, then you do not need to use backticks.
---
-
-[.arrange]
-== Code Challenge
-
-Now that you know the basics, use the parts below to build a cypher statement to to find the `title` and year of `release` for every `:Movie` that Tom Hanks has `:DIRECTED`.
-Click the parts to add them in order and once you are done, click **Run Query** to see whether you have got it right.
-You can click any part of the query inside the code block to remove it.
-
-[source,cypher]
-MATCH (p:Person {name: "Tom Hanks"})-[:DIRECTED]->(m:Movie) RETURN m.title, m.released
-
-[#cypher-next-steps]
-== Next Steps
-
-Now that you know how to write nodes, relationships, properties, and patterns in Cypher for reading existing data, you can begin exploring data that exists in a Neo4j database.
-We will look at more `MATCH` capabilities in an upcoming guide, as well as how to write Cypher for create, update, and delete operations with your data.
-
-[#cypher-resources]
-== Resources
-
-* https://neo4j.com/docs/cypher-manual/current/clauses/match/[Neo4j Cypher Manual: MATCH^]
-* https://neo4j.com/docs/cypher-manual/current/clauses/return/[Neo4j Cypher Manual: RETURN^]
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/replanning.adoc b/modules/cypher/pages/replanning.adoc
deleted file mode 100644
index cc4b5f0d..00000000
--- a/modules/cypher/pages/replanning.adoc
+++ /dev/null
@@ -1,109 +0,0 @@
-= Query Runtime and Replanning
-:level: Intermediate
-:page-level: Intermediate
-:author: Elaine Rosenberg
-:neo4j-version: 4.1
-:category: cypher
-:tags: cypher, queries, execution plan, planner, replan
-:description: This guide describes improvements made for query replanning in Neo4j 4.1.
-:page-pagination:
-:page-aliases: ROOT:replanning.adoc
-:page-newsletter: true
-:page-deprecated-title: the Neo4j Cypher Manual
-:page-deprecated-redirect: https://neo4j.com/docs/cypher-manual/current/query-tuning/query-options/
-
-// This page has been deprecated in favour of the Neo4j Cypher Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with how queries work in Neo4j
-[role=expertise {level}]
-{level}
-
-
-[#pipelined-runtime]
-== New: Pipelined runtime
-
-In Neo4j 4.0, Cypher introduced the pipelined runtime to optimize query planning.
-In 4.1, we have added even more READ operators to the pipelined Cypher runtime.
-These new operators include:
-
-[square]
-* NestedPlanExpression
-* OptionalExpandAdd
-* OrderedAggregation
-* PartialSort
-* PartialTop
-* RollUpApply
-* SemiApply
-* AntiSemiApply
-* Skip
-* Union
-* ValueHashJoin
-
-That brings to a total of operators for pipelined runtime to 48 that we have implemented.
-We have just 8 operators that need to be implemented to fully support read in the pipelined runtime.
-
-Pipelined read operators have improved READ performance by 11-50% in 4.1.
-
-If you have extremely small queries (micro-second level), using slotted runtime will still be faster than pipelined.
-
-
-[#query-planning]
-== Query planning options
-
-In 4.0, Cypher had these options:
-
-----
-CYPHER x.y 
-----
-
-Where you could specify, for example 4.0 or 3.5 for the version of Cypher.
-
-----
-CYPHER runtime= 
-----
-
-Where the options for  were:
-
-[square]
-* *interpreted* (slower, used in Community Edition)
-* *slotted*	(more optimized interpreted)
-* *pipelined* (replaced compiled in 3.x; more optimizations)
-
-== New: Query replanning options
-
-In 4.1, Cypher has added a `replan` option to help you control if the query will be recompiled.
-Sometimes recompilation is desired, especially if the statistics that affect query planning have changed.
-However, there are also times when you do not want the plan to change because you are measuring queries that you have previously cached and you do not want the expense of recompilation.
-
-----
-CYPHER replan= 
-----
-
-Where the options for  are:
-
-[square]
-* *force* (to recompile the query, whether it is in the cache or not)
-* *skip* (recompile only if the query is not in the cache)
-
-In general, if you want to force a replan, then you would do something like this:
-
-----
-CYPHER replan=force EXPLAIN 
-----
-
-This will cause the query to be recompiled and placed in the cache with the latest statistics.
-
-
-[#cypher-resources]
-=== Resources
-
-* link:https://neo4j.com/docs/cypher-manual/current/query-tuning/[Docs: Cypher query tuning].
-* link:https://neo4j.com/graphacademy/online-training/cypher-query-tuning-40/.[Online Course: Cypher Query Tuning in Neo4j 4.0].
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/resources.adoc b/modules/cypher/pages/resources.adoc
deleted file mode 100644
index 962bf7a4..00000000
--- a/modules/cypher/pages/resources.adoc
+++ /dev/null
@@ -1,51 +0,0 @@
-= Cypher Resources
-:author: Jennifer Reif
-:category: cypher
-:tags: cypher, queries, graph-queries, resources, documentation, cypher-training, cypher-help
-:page-pagination: previous
-:page-aliases: ROOT:cypher-resources.adoc
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/resources/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-== Cypher Resources
-
-To help you along your path of learning more about Cypher and how to use it, we want to provide you with the resources we used throughout this section, as well as a few additional links for further knowledge and development.
-
-[#cypher-docs]
-== Cypher Basics and Docs
-* https://graphacademy.neo4j.com/courses/cypher-fundamentals/[GraphAcademy - Cypher Fundamentals].  Learn Cypher in 60 minutes.
-* https://neo4j.com/docs/cypher-manual/current/[Documentation: Cypher Manual^]
-* https://neo4j.com/docs/cypher-refcard/current/[Cypher Refcard^]
-* * https://community.neo4j.com/c/neo4j-graph-platform/cypher[Neo4j Community Site: Ask Questions, Get Answers on Cypher^]
-
-[#cypher-sql-dev]
-== Cypher for SQL Developers
-* https://youtu.be/NO3C-CWykkY[Video: SQL to Cypher^]
-* https://neo4j.com/whitepapers/rdbms-developers-graph-databases-ebook/[Free eBook: Graphs for RDBMS Developers^]
-
-[#other-cypher-resources]
-== Other Resources
-* https://medium.com/neo4j/cypher-query-optimisations-fe0539ce2e5c[Medium Blog: Cypher Optimization^]
-* Blog series, Handling Dates and Temporals in Cypher: https://neo4j.com/developer-blog/cypher-sleuthing-dealing-with-dates-part-1/[Part 1^], https://neo4j.com/developer-blog/cypher-sleuthing-dealing-with-dates-part-2/[Part 2^], https://neo4j.com/developer-blog/cypher-sleuthing-dealing-with-dates-part-3/[Part 3^]
-* https://markhneedham.com/blog/tag/cypher/[Blog: Mark Needham on Cypher^]
-* https://maxdemarzi.com/category/cypher/[Blog: Max De Marzi on Cypher^]
-* https://www.airpair.com/neo4j/posts/getting-started-with-neo4j-and-cypher[Tutorial by Eve Freeman: Building an ACL with Cypher^]
-* https://medium.com/neo4j[Neo4j Medium Blog Channel^]
-
-include::partial$next-steps.adoc[]
-
-[.discrete.ad]
-== link:https://graphacademy.neo4j.com/?ref=guides[Learn with GraphAcademy^]
-
-image::https://graphacademy.neo4j.com/courses/cypher-fundamentals/badge/[float=left]
-
-[.discrete]
-=== link:https://graphacademy.neo4j.com/courses/cypher-fundamentals/?ref=guides[Cypher Fundamentals^]
-
-This course teaches you the essentials of using Cypher, Neo4j’s powerful query language, in as little time as possible, with videos, quizzes and hands-on exercises.
-
-
-link:https://graphacademy.neo4j.com/courses/cypher-fundamentals/?ref=guides[Learn Cypher with GraphAcademy^,role=button]
\ No newline at end of file
diff --git a/modules/cypher/pages/style-guide.adoc b/modules/cypher/pages/style-guide.adoc
deleted file mode 100644
index 951e9311..00000000
--- a/modules/cypher/pages/style-guide.adoc
+++ /dev/null
@@ -1,483 +0,0 @@
-= Cypher Style Guide
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: cypher
-:tags: cypher, queries, graph-queries, syntax-style, model-style, write-cypher, syntax
-:description: This guide will show some of the syntax recommendations for building clean, easy-to-read Cypher queries. At the end of this page, you will know how to improve readability of keywords, properties, labels, clauses, and other components.
-:page-pagination:
-:page-aliases: ROOT:cypher-style-guide.adoc
-:page-newsletter: true
-:page-deprecated-title: the Neo4j Cypher Manual
-:page-deprecated-redirect: https://neo4j.com/docs/cypher-manual/current/styleguide/
-
-// This page has been deprecated in favour of the Neo4j Cypher Manual, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with xref:index.adoc[cypher patterns] and xref:filtering-query-results.adoc[filtering queries].
-
-[role=expertise {level}]
-{level}
-
-[#cypher-style]
-== Cypher Styles
-
-There are a few recommended ways to format and organize Cypher queries so that data naming and formatting is consistent and easy-to-read.
-The next few paragraphs will show the recommendations with some examples.
-
-[#data-model-style]
-== Styling in the Data Model
-
-Formatting nodes, relationships, labels, and properties help to make queries cleaner.
-If each of these components has a different style, then it is easy to pick out these items in a query and visually separate each to understand the syntax better.
-Let us look at each recommendation.
-
-=== Node Labels
-
-Node labels are styled in CamelCase where the first letter of each word begins with a capital letter.
-They are also case-sensitive.
-This means that however you create them in the graph must match the queries you plan to run later.
-For instance, if you use incorrect camelcase when you create the label in the graph, then you will need to write your queries with the same incorrect camelcasing.
-
-[source,cypher]
-----
-(:Person)
-(:NetworkAddress)
-(:VeryDescriptiveLabel)
-----
-
-=== Relationship Types
-
-Relationship types are styled with all upper-case and use the underscore between words.
-They are case-sensitive, so queries syntax must match the format in which they were created in the database.
-
-[source,cypher]
-----
-[:FOLLOWS]
-[:ACTED_IN]
-[:IS_IN_LOVE_WITH]
-----
-
-=== Property Keys, Variables, Parameters, Aliases, and Functions
-
-Property keys, variables, parameters, aliases, and functions are styled in camelCase where the first letter of the component begins with a lower-case letter, and the first letter of each following word is a capital letter.
-All of these are also case-sensitive, so capitalization must match either what is in the database (properties), what is already defined in the query (variables, parameters, aliases), or Cypher definitions (functions).
-
-[source,cypher]
-----
-title
-size()
-businessAddress
-firstName
-customerAccountNumber
-allShortestPaths()
-----
-
-=== Clauses
-
-Clauses are styled in all capital letters, placed at the beginning of a new line, and are not case sensitive.
-It is possible to change casing (`mAtCh`), put multiple keywords on a line, or mistype clauses.
-Cypher will still execute the query.
-However, for readability and supportability of queries, we recommend clauses are in all capital letters and placed at the beginning of a new line.
-
-[source,cypher]
-----
-MATCH (n:Person)
-WHERE n.name = 'Bob'
-RETURN n;
-
-WITH "1980-01-01" AS birthdate
-MATCH (person:Person)
-WHERE person.birthdate > birthdate
-RETURN person.name;
-----
-
-=== Keywords
-
-Keywords, similar to clauses, should be styled in all capital letters and are not case-sensitive, but do *not* need to be placed on a separate line.
-This includes words such as `DISTINCT`, `IN`, `STARTS WITH`, `CONTAINS`, `NOT`, `AS`, `AND`, and others.
-
-[source,cypher]
-----
-MATCH (p:Person)-[:VISITED]-(place:City)
-RETURN collect(DISTINCT place.name);
-
-MATCH (a:Airport)
-RETURN a.airportIdentifier AS AirportCode;
-
-MATCH (c:Company)
-WHERE c.name CONTAINS 'Inc.' AND c.startYear IN [1990, 1998, 2007, 2010]
-RETURN c;
-----
-
-[#indent-line-break]
-== Indentation and Line Breaks
-
-As we started with above, separating clauses to new lines helps with readability of queries and breaking logic into visual blocks.
-We can also make queries a bit easier to read by indenting `ON CREATE` or `ON MATCH` and any subqueries.
-Each of these blocks is indented with 2 spaces on a new line.
-
-[source,cypher]
-----
-//indent 2 spaces on lines with ON CREATE or ON MATCH
-MATCH (p:Person {name: 'Alice'})
-MERGE (c:Company {name: 'Wayne Enterprises'})
-MERGE (p)-[rel:WORKS_FOR]-(c)
-  ON CREATE SET rel.startYear = date({year: 2018})
-  ON MATCH SET rel.updated = date()
-RETURN p, rel, c;
-
-//indent 2 spaces with braces for subqueries
-MATCH (p:Person)
-WHERE EXISTS {
-  MATCH (p)-->(c:Company)
-  WHERE c.name = 'Neo4j'
-}
-RETURN p.name;
-----
-
-Notice that in the query with a subquery, we use braces to group the subquery block (similar to methods or functions in other programming languages).
-The beginning curly brace is on the same line as the start of the subquery (`WHERE EXISTS`).
-The two lines of subquery (`MATCH...WHERE`) are indented with 2 spaces, and then the closing curly brace is on the next line with no indent.
-
-If the subquery is only one line, you do not need to put it on its own line or indent it.
-Instead, you can write it like the query below.
-These are the recommended guidelines if you need to use a subquery.
-
-[source,cypher]
-----
-//indent 2 spaces without braces for 1-line subqueries
-MATCH (p:Person)
-WHERE EXISTS { MATCH (p)-->(c:Company) }
-RETURN p.name
-----
-
-[#cypher-metacharacters]
-== Metacharacters
-
-Metacharacters include things like single or double quotes, backticks, and semicolons.
-In Cypher, there are special uses and recommended cases for using these characters.
-They are easily misused, so we will show what to avoid, as well as proper use of them.
-
-=== Single Quotes
-
-It is recommended to use single quotes for literal string values.
-There are exceptions to this rule, however, when single quotes are part of the string.
-If the string has both double and single quotes, use the form that creates the fewest escaped characters. In the case of a tie, preference is still to use single quotes.
-
-.Not as clean:
-[source,cypher]
---
-RETURN 'Cypher\'s a nice language', "Mats\' quote: "statement"'
---
-
-.Preferred Syntax:
-[source,cypher]
---
-RETURN "Cypher's a nice language", 'Mats\' quote: "statement"'
---
-
-=== Backticks
-
-Backticks should be avoided to escape characters and keywords.
-In the examples below, backticks are used to ignore special characters and spaces in properties and labels.
-
-If we follow the recommended styling guidelines on those components that we discussed above with camelcasing and joined words, then we should never see these types of backticks needed.
-Note that there are some cases with certain string values or other unusual syntax where backticks are appropriate.
-
-Cluttered syntax:
-[source,cypher]
-----
-MATCH (`odd-ch@racter$`:`Spaced Label` {`&property`: 42})
-RETURN labels(`odd-ch@racter$`)
-----
-
-Clean, recommended syntax:
-[source,cypher]
-----
-MATCH (node:NonSpacedLabel {property: 42})
-RETURN labels(node)
-----
-
-=== Semicolons
-
-Most of the time, there is no need to use a semicolon at the end of a Cypher query.
-Adding a semicolon to the end of a Cypher query is a redundant character because Cypher executes the block as an entire unit.
-
-The exception to this is when you have a Cypher script or a block with multiple, separate Cypher statements.
-In this case, you would need the semicolon to tell Cypher where the end of one query is and the beginning of the next query.
-This allows Cypher to execute each statement separately.
-
-[NOTE]
---
-If you do not include a semicolon between a block with multiple statements, Cypher will try to execute them as a single statement, causing an error.
---
-
-Unnecessary semicolon:
-[source,cypher]
-----
-MATCH (c:Company {name: 'Neo4j'})
-RETURN c;
-----
-
-Recommended syntax:
-[source,cypher]
-----
-MATCH (c:Company {name: 'Neo4j'})
-RETURN c
-----
-
-Recommended syntax for multi-query block:
-[source,cypher]
-----
-MATCH (c:Company {name: 'Neo4j'})
-RETURN c;
-
-MATCH (p:Person)
-WHERE p.name = 'Jennifer'
-RETURN p;
-
-MATCH (t:Technology)-[:LIKES]-(a:Person {name: 'Jennifer'})
-RETURN t.type;
-----
-
-[#cypher-null-boolean]
-== Null and Boolean Values
-
-The `null` value and boolean literals should be written in lower case in a query.
-
-Recommended syntax:
-[source,cypher]
-----
-//null and boolean values are lower case
-MATCH (p:Person)
-WHERE p.birthdate = null
-  SET missingBirthdate = true
-RETURN p
-----
-
-[#cypher-pattern-style]
-== Dealing with Patterns
-
-There are a few recommended styling practices for different scenarios using patterns.
-We will review those in the lines below.
-
-* *When you have patterns that wrap lines, it is recommended to break after arrows, not before.*
-
-Recommended:
-[source,cypher]
-----
-MATCH (:Person)-->(vehicle:Car)-->(:Company)<--
-      (:Country)
-RETURN count(vehicle)
-----
-
-* *Use anonymous nodes and relationships when the variable will not be used later in the query.*
-
-Recommended:
-[source,cypher]
-----
-MATCH (:Person {name: 'Kate'})-[:LIKES]-(c:Car)
-RETURN c.type
-----
-
-* *Chain patterns together to avoid repeating variables.*
-
-Unnecessary variable repeated:
-[source,cypher]
-----
-MATCH (:Person)-->(vehicle:Car), (vehicle:Car)-->(:Company)
-RETURN count(vehicle)
-----
-
-Recommended:
-[source,cypher]
-----
-MATCH (:Person)-->(vehicle:Car)-->(:Company)
-RETURN count(vehicle)
-----
-
-* *Put named nodes (that use variables) before anonymous nodes and relationships when possible and put anchor node (starting point or central focus) at the beginning of the `MATCH` clause.*
-
-Recommended:
-[source,cypher]
-----
-MATCH (manufacturer:Company)<--(vehicle:Car)<--()
-WHERE manufacturer.foundedYear < 2000
-RETURN vehicle.mileage
-----
-
-* *Prefer outgoing (left to right) pattern relationships to incoming pattern relationships.*
-
-Recommended:
-[source,cypher]
-----
-MATCH (:Person)-->(vehicle:Car)-->(:Company)<--(:Country)
-RETURN vehicle.mileage
-----
-
-[#style-spacing]
-== Spacing
-
-Whether spacing is used and where it is placed can have a big impact on the readability of queries.
-In the next few examples, we will show some comparisons of less effective spacing, as well as the recommmended practice.
-
-* *One space between label or type predicates and property predicates in patterns.*
-
-Ineffective spacing:
-[source,cypher]
-----
-MATCH (p:Person{name: 'Bob'})-[:KNOWS   {since: 2016}]->(other:Person)
-RETURN other.name
-----
-
-Recommended:
-[source,cypher]
-----
-MATCH (p:Person {name: 'Bob'})-[:KNOWS {since: 2016}]->(other:Person)
-RETURN other.name
-----
-
-* *No space in label predicates.*
-
-Ineffective spacing:
-[source,cypher]
-----
-MATCH (person    : Person  :  Owner  )
-RETURN person.name
-----
-
-Recommended:
-[source,cypher]
-----
-MATCH (person:Person:Owner)
-RETURN person.name
-----
-
-* *No space in patterns.*
-
-Ineffective spacing:
-[source,cypher]
-----
-MATCH (:Person) --> (:Vehicle)
-RETURN count(*)
-----
-
-Recommended:
-[source,cypher]
-----
-MATCH (:Person)-->(:Vehicle)
-RETURN count(*)
-----
-
-* *One space on either side of operators.*
-
-Ineffective spacing:
-[source,cypher]
-----
-MATCH (p:Person)-->(other:Person)
-WHERE p.name<>other.name
-RETURN length(p)
-----
-
-Recommended:
-[source,cypher]
-----
-MATCH (p:Person)-->(other:Person)
-WHERE p.name <> other.name
-RETURN length(p)
-----
-
-* *One space after each comma in lists and enumerations.*
-
-Ineffective spacing:
-[source,cypher]
-----
-WITH ['Sally','Mark','Alice'] as list
-MATCH (c:Customer),(e:Employee)
-WHERE c.name IN list
-AND (c)-[:IS_ASSIGNED_TO]-(e)
-RETURN c.name,e.name as customerContact
-----
-
-Recommended:
-[source,cypher]
-----
-WITH ['Sally', 'Mark', 'Alice'] as list
-MATCH (c:Customer), (e:Employee)
-WHERE c.name IN list
-AND (c)-[:IS_ASSIGNED_TO]-(e)
-RETURN c.name, e.name as customerContact
-----
-
-* *No padding space within function call parentheses.*
-
-Ineffective spacing:
-[source,cypher]
-----
-RETURN split( 'test', 'e' )
-----
-
-Recommended:
-[source,cypher]
-----
-RETURN split('test', 'e')
-----
-
-* *Use padding space within simple subquery expressions.*
-
-Ineffective spacing:
-[source,cypher]
-----
-MATCH (a:Person)
-WHERE EXISTS {(a)-->(b:Person)}
-RETURN a.name, collect(b.name) as friends
-----
-
-Recommended:
-[source,cypher]
-----
-MATCH (a:Person)
-WHERE EXISTS { (a)-->(b:Person) }
-RETURN a.name, collect(b.name) as friends
-----
-
-* *Map Literal recommendations -*
-** No space between opening brace and first key or between key and colon
-** One space between colon and value
-** No space between value and comma, but one space between comma and next key
-** No space between last value and closing brace
-
-Ineffective spacing:
-[source,cypher]
-----
-WITH { key1 :'value' ,key2  :  10 } AS map
-RETURN map
-----
-
-Recommended:
-[source,cypher]
-----
-WITH {key1: 'value', key2: 10} AS map
-RETURN map
-----
-
-[#cypher-next-steps]
-== Next Steps
-
-Now that you are familiar with recommended styling and formatting of Cypher syntax, you will be able to write cleaner and more readable queries.
-This is especially helpful for knowledge tranfers to other developers and supportability of the code.
-
-In the next guide, we will show how to ensure good data integrity using indexes and constraints to maintain uniqueness and query performance.
-
-[#cypher-resources]
-== Resources
-
-* link:https://github.com/opencypher/openCypher/blob/master/docs/style-guide.adoc[Neo4j Cypher Style Guide^]
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/subqueries.adoc b/modules/cypher/pages/subqueries.adoc
deleted file mode 100644
index 7fd7cfd7..00000000
--- a/modules/cypher/pages/subqueries.adoc
+++ /dev/null
@@ -1,313 +0,0 @@
-= Subqueries
-:level: Beginner
-:page-level: Beginner
-:author: Mark Needham
-:neo4j-version: 4.0
-:category: cypher
-:tags: cypher, queries, graph-queries, subqueries, compare-queries
-:description: Building on the previous Cypher guides, this guide shows how to write subqueries. Upon finishing this guide, you should be able to write and understand queries using this capability.
-:page-pagination:
-:page-aliases: ROOT:subqueries.adoc
-:page-newsletter: true
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/subqueries/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) downloaded and installed.
-You should be familiar with xref:ROOT:get-started.adoc[graph database] concepts and the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-This guide is a continuation of the concepts discussed in the previous Cypher sections.
-You should be familiar with xref:index.adoc[MATCH], xref:updating.adoc[Create/Update/Delete], and xref:filtering-query-results.adoc[Filtering] concepts before walking through this guide.
-
-[role=expertise {level}]
-{level}
-
-
-[#recap]
-== Recap: Our Example Graph
-
-All of our examples will continue with the graph example we have been using in the previous guides, but include some more data for some of our later queries.
-Below is an image of the new graph.
-
-We have added more `Person` nodes (blue) who `WORK_FOR` different `Company` nodes (red) and `LIKE` different `Technology` (green) nodes.
-
-To recap, each person could also have multiple `IS_FRIENDS_WITH` relationships to other people.
-This gives us a network of people, the companies they work for, and the technologies they like.
-
-image::{img}cypher_graph_v3.png[role="popup-link"]
-
-[#cypher-filtering]
-== An introduction to Subqueries
-
-Neo4j 4.0 introduced support for two different types of subqueries:
-
-* Existential sub queries in a `WHERE` clause
-* Result returning subqueries using the `CALL {}` syntax
-
-In this guide we're going to learn how to write queries that use both these approaches.
-
-[#existential-subqueries]
-== Existential Subqueries
-
-In the filtering on patterns section of the filtering query results guide, we learnt how to filter based on patterns.
-For example, we wrote the following query to find the friends of someone who works for Neo4j:
-
-[source,cypher]
-----
-MATCH (p:Person)-[r:IS_FRIENDS_WITH]->(friend:Person)
-WHERE exists((p)-[:WORKS_FOR]->(:Company {name: 'Neo4j'}))
-RETURN p, r, friend
-----
-
-If we run this query in the Neo4j Browser, the following graph is returned:
-
-image::{img}friends-of-neo4j.png[role="popup-link"]
-
-https://neo4j.com/docs/cypher-manual/current/clauses/where/#existential-subqueries[Existential subqueries^] enable more powerful pattern filtering.
-Instead of using the `exists` function in our `WHERE` clause, we use the `EXISTS {}` clause.
-We can reproduce the previous example with the following query:
-
-[source,cypher]
-----
-MATCH (p:Person)-[r:IS_FRIENDS_WITH]->(friend:Person)
-WHERE EXISTS {
-  MATCH (p)-[:WORKS_FOR]->(:Company {name: 'Neo4j'})
-}
-RETURN p, r, friend
-----
-
-We'll get the same results, which is nice, but so far all we've achieved is the same thing with more code!
-
-Let's next write a subquery that does more powerful filtering than what we can achieve with the `WHERE` clause or `exists` function alone.
-
-Imagine that we want to find the people who:
-
-* work for a company whose name starts with 'Company' and
-* like at least one technology that's liked by 3 or more people
-
-We aren't interested in knowing what those technologies are.
-We might try to answer this question with the following query:
-
-[source,cypher]
-----
-MATCH (person:Person)-[:WORKS_FOR]->(company)
-WHERE company.name STARTS WITH "Company"
-AND (person)-[:LIKES]->(t:Technology)
-AND size((t)<-[:LIKES]-()) >= 3
-RETURN person.name as person, company.name AS company;
-----
-
-If we run this query, we'll see the following output:
-
-
-[source,text]
-----
-Variable `t` not defined (line 4, column 25 (offset: 112))
-"AND (person)-[:LIKES]->(t:Technology)"
-                         ^
-----
-
-We can find people that like a technology, but we can't check that at least 3 people like that technology as well, because the variable `t` isn't in the scope of the `WHERE` clause.
-Let's instead move the two `AND` statements into an `EXISTS {}` block, resulting in the following query:
-
-[source,cypher]
-----
-MATCH (person:Person)-[:WORKS_FOR]->(company)
-WHERE company.name STARTS WITH "Company"
-AND EXISTS {
-  MATCH (person)-[:LIKES]->(t:Technology)
-  WHERE size((t)<-[:LIKES]-()) >= 3
-}
-RETURN person.name as person, company.name AS company;
-----
-
-Now we're able to successfully execute the query, which returns the following results:
-
-[options="header"]
-|===
-| person    | company
-| "Melissa" | "CompanyA"
-| "Diana"   | "CompanyX"
-|===
-
-If we recall the graph visualisation from the start of this guide, Ryan is the only other person who works for a company whose name starts with "Company".
-He's been filtered out in this query because the only `Technology` that he likes is Python, and there aren't 3 people who like Python.
-
-[#result-returning-subqueries]
-== Result returning subqueries
-
-So far we've learnt how to use subqueries to filter out results, but this doesn't fully show case their power.
-We can also use subqueries to return results as well.
-
-Let's say we want to write a query that finds people who like Java or have more than one friend.
-And we want to return the results ordered by date of birth in descending order.
-We can get some of the way there using the `UNION` clause:
-
-[source,cypher]
-----
-MATCH (p:Person)-[:LIKES]->(:Technology {type: "Java"})
-RETURN p.name AS person, p.birthdate AS dob
-ORDER BY dob DESC
-
-UNION
-
-MATCH (p:Person)
-WHERE size((p)-[:IS_FRIENDS_WITH]->()) > 1
-RETURN p.name AS person, p.birthdate AS dob
-ORDER BY dob DESC;
-----
-
-If we run that query, we'll see the following output:
-
-[options="header"]
-|===
-| person     | dob
-| "Jennifer" | 1988-01-01
-| "John"     | 1985-04-04
-| "Joe"      | 1988-08-08
-|===
-
-We've got the correct people, but the `UNION` approach only lets us sort results per `UNION` clause, not for all rows.
-
-We can try another approach, where we execute each of our subqueries separately and collect the people from each part using the `COLLECT` function.
-There are some people who like Java and have more than one friend, so we'll also need to use a function from the APOC Library to remove those duplicates:
-
-[source,cypher]
-----
-// Find people who like Java
-MATCH (p:Person)-[:LIKES]->(:Technology {type: "Java"})
-WITH collect(p) AS peopleWhoLikeJava
-
-// Find people with more than one friend
-MATCH (p:Person)
-WHERE size((p)-[:IS_FRIENDS_WITH]->()) > 1
-WITH collect(p) AS popularPeople, peopleWhoLikeJava
-
-// Filter duplicate people
-WITH apoc.coll.toSet(popularPeople + peopleWhoLikeJava) AS people
-
-// Unpack the collection of people and order by birthdate
-UNWIND people AS p
-RETURN p.name AS person, p.birthdate AS dob
-ORDER BY dob DESC
-----
-
-If we run that query, we'll get the following output:
-
-[options="header"]
-|===
-| person     | dob
-| "Joe"      | 1988-08-08
-| "Jennifer" | 1988-01-01
-| "John"     | 1985-04-04
-|===
-
-This approach works, but it's more difficult to write, and we have to keep passing through parts of state to the next part of the query.
-
-The https://neo4j.com/docs/cypher-manual/current/clauses/call-subquery/index.html[`CALL {}`^] clause gives us the best of both worlds:
-
-* We can use the UNION approach to run the individual queries and remove duplicates
-* We can sort the results afterwards
-
-Our query using the `CALL {}` clause looks like this:
-
-[source,cypher]
-----
-CALL {
-	MATCH (p:Person)-[:LIKES]->(:Technology {type: "Java"})
-	RETURN p
-
-	UNION
-
-	MATCH (p:Person)
-	WHERE size((p)-[:IS_FRIENDS_WITH]->()) > 1
-	RETURN p
-}
-RETURN p.name AS person, p.birthdate AS dob
-ORDER BY dob DESC;
-----
-
-If we run that query, we'll get the following output:
-
-[options="header"]
-|===
-| person     | dob
-| "Joe"      | 1988-08-08
-| "Jennifer" | 1988-01-01
-| "John"     | 1985-04-04
-|===
-
-We could extend our query further to return the technologies that these people like, and the  friends that they have.
-The following query shows how to do this:
-
-[source,cypher]
-----
-CALL {
-	MATCH (p:Person)-[:LIKES]->(:Technology {type: "Java"})
-	RETURN p
-
-	UNION
-
-	MATCH (p:Person)
-	WHERE size((p)-[:IS_FRIENDS_WITH]->()) > 1
-	RETURN p
-}
-WITH p,
-     [(p)-[:LIKES]->(t) | t.type] AS technologies,
-     [(p)-[:IS_FRIENDS_WITH]->(f) | f.name] AS friends
-
-RETURN p.name AS person, p.birthdate AS dob, technologies, friends
-ORDER BY dob DESC;
-----
-
-[options="header"]
-|===
-| person     | dob        | technologies                        | friends
-| "Joe"      | 1988-08-08 | ["Query Languages"]                 | ["Mark", "Diana"]
-| "Jennifer" | 1988-01-01 | ["Graphs", "Java"]                  | ["Sally", "Mark", "John", "Ann", "Melissa"]
-| "John"     | 1985-04-04 | ["Java", "Application Development"] | ["Sally"]
-|===
-
-We can also apply aggregation functions to the results of our subquery.
-The following query returns the youngest and oldest of the people who like Java or have more than one friend
-
-[source,cypher]
-----
-CALL {
-	MATCH (p:Person)-[:LIKES]->(:Technology {type: "Java"})
-	RETURN p
-
-	UNION
-
-	MATCH (p:Person)
-	WHERE size((p)-[:IS_FRIENDS_WITH]->()) > 1
-	RETURN p
-}
-RETURN min(p.birthdate) AS oldest, max(p.birthdate) AS youngest
-----
-
-[options="header"]
-|===
-| oldest     | youngest
-| 1985-04-04 | 1988-08-08
-|===
-
-[#cypher-next-steps]
-=== Next Steps
-
-We have seen how to use the `EXISTS {}` clause to write complex filtering patterns, and the `CALL {}` clause to execute result returning subqueries.
-In the next section, we will learn how to use aggregation in Cypher and how to do more with the return results.
-
-[#cypher-resources]
-=== Resources
-
-* link:/docs/cypher-manual/current/clauses/where/#existential-subqueries[Neo4j Cypher Manual: Using existential subqueries in WHERE^]
-* link:/docs/cypher-manual/current/clauses/call-subquery/[Neo4j Cypher Manual: CALL {} (subquery)^]
-
-include::partial$help.adoc[]
diff --git a/modules/cypher/pages/updating.adoc b/modules/cypher/pages/updating.adoc
deleted file mode 100644
index ecb0ca21..00000000
--- a/modules/cypher/pages/updating.adoc
+++ /dev/null
@@ -1,320 +0,0 @@
-= Updating with Cypher
-:level: Beginner
-:page-level: Beginner
-:author: Jennifer Reif
-:category: cypher
-:tags: cypher, queries, graph-queries, insert-create, update, delete, merge
-:description: Building on the Cypher Basics I guide, this guide covers more introductory concepts of Cypher, Neo4j's graph query language. Upon finishing this guide, you should be able to read and write Cypher queries for standard CRUD operations.
-:page-pagination:
-:page-aliases: ROOT:cypher-basics-iii.adoc
-:page-newsletter: true
-:page-ad-overline-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-overline: Neo4j GraphAcademy
-:page-ad-title: Cypher Fundamentals
-:page-ad-description: Learn Cypher in this free, hands-on course
-:page-ad-link: https://graphacademy.neo4j.com/?ref=guides
-:page-ad-underline-role: button
-:page-ad-underline: Learn more
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/cypher-intro/updating/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should be familiar with xref:ROOT:graph-database.adoc[graph database] concepts and the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-This guide is a continuation of the xref:intro-cypher.adoc[Cypher Syntax] section.
-You will need to read that guide before continuing with the current section.
-
-[role=expertise {level}]
-{level}
-
-[#cypher-crud-operations]
-== Create, Update, and Delete Operations
-
-In the last guide, we learned how to represent nodes, relationships, labels, properties, and patterns in Cypher for read queries.
-This guide will add another level to your knowledge by introducing how to write create, update, and delete operations in Cypher.
-
-While these are the standard CRUD operations, some things function a bit differently in a graph than in other types of databases.
-You will probably recognize some of the similarities and differences as we go along.
-
-=== Inserting Data with Cypher
-
-Adding data in Cypher works very similarly to any other data access language's insert statement.
-Instead of the `INSERT` keyword like in SQL, though, Cypher uses `CREATE`.
-You can use `CREATE` to insert nodes, relationships, and patterns into Neo4j.
-
-Let us look at an example that we used in our last guide.
-To review, we had a `Person` node (Jennifer) who liked graphs, was friends with Michael, and worked at Neo4j.
-
-image::{img}cypher_graph_v1.jpg[role="popup-link"]
-
-What if we wanted to add another of Jennifer's friends to the graph?
-We can add Jennifer's friend Mark using the Cypher statement below.
-
-[source, cypher]
-----
-CREATE (friend:Person {name: 'Mark'})
-RETURN friend
-----
-
-[NOTE]
---
-It is not required to include the `RETURN` clause in the Cypher statement above.
-If you do not want to return any results, simply run this statement instead:
-`CREATE (friend:Person {name: 'Mark'})`
---
-
-image::{img}cypher_graph_createFriend.jpg[role="popup-link"]
-
-Great! Now we added Mark to the database.
-However, Mark is all alone with no relationships because we just created his node and did not specify any connections.
-We know he is friends with Jennifer (same as Michael), so we can add a new `IS_FRIENDS_WITH` relationship between the existing Jennifer and Mark nodes.
-The Cypher to do that would look like this.
-
-[source, cypher]
-----
-MATCH (jennifer:Person {name: 'Jennifer'})
-MATCH (mark:Person {name: 'Mark'})
-CREATE (jennifer)-[rel:IS_FRIENDS_WITH]->(mark)
-----
-
-image::{img}cypher_graph_createFriendRel.jpg[role="popup-link"]
-
-Notice that we run two `MATCH` queries before we create a relationship between the nodes.
-Why is that?
-The reason we do a match for Jennifer's node and a match for Mark's node first is because the `CREATE` keyword does a blind insert and will create the entire pattern, regardless if it already exists in the database.
-
-This means that running the Cypher statement below will insert duplicate Jennifer and Mark nodes.
-To avoid this, our previous query first found the existing nodes, and then created a new relationship between them.
-
-[source, cypher]
-----
-//this query will create duplicate nodes for Mark and Jennifer
-CREATE (j:Person {name: 'Jennifer'})-[rel:IS_FRIENDS_WITH]->(m:Person {name: 'Mark'})
-----
-
-[NOTE]
---
-We will learn a few ways to ensure unique inserts and to maintain data integrity, so do not worry about how to do that just yet.
---
-
-[#cypher-update]
-== Updating Data with Cypher
-
-Maybe you already have a node or relationship in the data, but you want to modify its properties.
-You can do this by matching the pattern you want to find and using the `SET` keyword to add, remove, or update properties.
-
-Using our example thus far, we could update Jennifer's node to add her birthday.
-The next Cypher statement shows how to do this.
-First, we need to find our existing node for Jennifer.
-Next, we use `SET` to create the new property (with syntax `variable.property`) and set its value.
-Finally, we can return Jennifer's node to ensure that the information was updated correctly.
-
-[source, cypher]
-----
-MATCH (p:Person {name: 'Jennifer'})
-SET p.birthdate = date('1980-01-01')
-RETURN p
-----
-
-image::{img}cypher_graph_addProp.jpg[role="popup-link"]
-
-[NOTE]
---
-For more information on using `date()` and other temporal functions, you can visit the https://neo4j.com/docs/developer-manual/3.4/cypher/syntax/temporal/[developer manual^].
---
-
-If we now wanted to change her birthday, we could use the same query above to find Jennifer's node again and put a different date in the `SET` clause.
-
-We could also update Jennifer's `WORKS_FOR` relationship with her company to include the year that she started working there.
-To do this, you can use similar syntax as above for updating nodes.
-
-[source, cypher]
-----
-MATCH (:Person {name: 'Jennifer'})-[rel:WORKS_FOR]-(:Company {name: 'Neo4j'})
-SET rel.startYear = date({year: 2018})
-RETURN rel
-----
-
-image::{img}cypher_graph_addRelProp.jpg[role="popup-link"]
-
-[NOTE]
---
-If we wanted to return a graph view on the above query, we could add variables to the nodes for `p:Person` and `c:Company` and write the return line as `RETURN p, rel, c`.
---
-
-[#cypher-delete]
-== Deleting Data with Cypher
-
-Another operation for us to cover is how to delete data in Cypher.
-For this operation, Cypher uses the `DELETE` keyword for deleting nodes and relationships.
-It is very similar to deleting data in other languages like SQL, with one exception.
-
-Because Neo4j is ACID-compliant, you cannot delete a node if it still has relationships.
-If you could do that, then you might end up with a relationship pointing to nothing and an incomplete graph.
-We will walk through how to delete a disconnected node, a relationship, as well as a node that still has relationships.
-
-=== Delete a Relationship
-
-To delete a relationship, you need to find the start and end nodes for the relationship you want to delete and then use the `DELETE` keyword, as shown in the statement below.
-Let us go ahead and delete the `IS_FRIENDS_WITH` relationship between Jennifer and Mark for now.
-We will add this relationship back in a later exercise.
-
-[source, cypher]
-----
-MATCH (j:Person {name: 'Jennifer'})-[r:IS_FRIENDS_WITH]->(m:Person {name: 'Mark'})
-DELETE r
-----
-
-image::{img}cypher_graph_delRel.jpg[role="popup-link"]
-
-=== Delete a Node
-
-To delete a node that does not have any relationships, you need to find the node you want to delete and then use the `DELETE` keyword, just as we did for the relationship above.
-We can delete Mark's node for now and add him back in a later exercise.
-
-[source, cypher]
-----
-MATCH (m:Person {name: 'Mark'})
-DELETE m
-----
-
-image::{img}cypher_graph_delNode.jpg[role="popup-link"]
-
-=== Delete a Node and Relationship
-
-Instead of running the last two queries to delete the `IS_FRIENDS_WITH` relationship and the `Person` node for Mark, we can actually run a single statement to delete the node and relationship at the same time.
-As we mentioned above, Neo4j is ACID-compliant so it doesn't allow us to delete a node if it still has relationships.
-Using the `DETACH DELETE` syntax tells Cypher to delete any relationships the node has, as well as remove the node itself.
-
-The statement would look like the code below.
-First, we find Mark's node in the database.
-Then, the `DETACH DELETE` line removes any existing relationships Mark has before also deleting his node.
-
-[source, cypher]
-----
-MATCH (m:Person {name: 'Mark'})
-DETACH DELETE m
-----
-
-=== Delete Properties
-
-You can also remove properties, but instead of using the `DELETE` keyword, we can use a couple of other approaches.
-The first option is to use `REMOVE` on the property.
-This tells Neo4j that you want to remove the property from the node entirely and no longer store it.
-
-The second option is to use the `SET` keyword from earlier to set the property value to `null`.
-Unlike other database models, Neo4j does not store null values.
-Instead, it only stores properties and values that are meaningful to your data.
-This means that you can have different types and amounts of properties on various nodes and relationships in your graph.
-
-To show you both options, let us look at the code for each.
-
-[source, cypher]
-----
-//delete property using REMOVE keyword
-MATCH (n:Person {name: 'Jennifer'})
-REMOVE n.birthdate
-
-//delete property with SET to null value
-MATCH (n:Person {name: 'Jennifer'})
-SET n.birthdate = null
-----
-
-image::{img}cypher_graph_delProp.jpg[role="popup-link"]
-
-[#cypher-merge]
-== Avoiding Duplicate Data Using MERGE
-
-We briefly mentioned in an earlier section that there are some ways in Cypher to avoid creating duplicate data.
-One of those ways is by using the `MERGE` keyword.
-`MERGE` does a "select-or-insert" operation that first checks if the data exists in the database.
-If it exists, then Cypher returns it as is or makes any updates you specify on the existing node or relationship.
-If the data does not exist, then Cypher will create it with the information you specify.
-
-=== Using Merge on a Node
-
-To start, let us look at an example of this by adding Mark back to our database using the query below.
-We use `MERGE` to ensure that Cypher checks the database for an existing node for Mark.
-Since we removed Mark's node in the previous examples, Cypher will not find an existing match and will create the node new with the `name` property set to 'Mark'.
-
-If we run the same statement again, Cypher will find an existing node this time that has the name Mark, so it will return the matched node without any changes.
-
-[source, cypher]
-----
-MERGE (mark:Person {name: 'Mark'})
-RETURN mark
-----
-
-image::{img}cypher_graph_mergeFriend.jpg[role="popup-link"]
-
-=== Using Merge on a Relationship
-
-Just like we used `MERGE` to find or create a node in Cypher, we can do the same thing to find or create a relationship.
-Let's re-create the `IS_FRIENDS_WITH` relationship between Mark and Jennifer that we had in a previous example.
-
-[source, cypher]
-----
-MATCH (j:Person {name: 'Jennifer'})
-MATCH (m:Person {name: 'Mark'})
-MERGE (j)-[r:IS_FRIENDS_WITH]->(m)
-RETURN j, r, m
-----
-
-Notice that we used `MATCH` here to find both Mark's node and Jennifer's node before we used `MERGE` to find or create the relationship.
-Why did we not use a single statement?
-`MERGE` looks for an entire pattern that you specify to see whether to return an existing one or create it new.
-If the entire pattern (nodes, relationships, and any specified properties) does not exist, Cypher will create it.
-
-Cypher never produces a partial mix of matching and creating within a pattern.
-To avoid a mix of match and create, you need to match any existing elements of your pattern first before doing a merge on any elements you might want to create, just as we did in the statement above.
-
-image::{img}cypher_graph_mergeFriendRel.jpg[role="popup-link"]
-
-Just for reference, the Cypher statement that will cause duplicates is below.
-Because this pattern (Jennifer IS_FRIENDS_WITH Mark) does not exist in the database, Cypher creates the entire pattern new - both nodes, as well as the relationship between them.
-
-[source, cypher]
-----
-//this statement will create duplicate nodes for Mark and Jennifer
-MERGE (j:Person {name: 'Jennifer'})-[r:IS_FRIENDS_WITH]->(m:Person {name: 'Mark'})
-RETURN j, r, m
-----
-
-=== Handling MERGE Criteria
-
-Perhaps you want to use `MERGE` to ensure you do not create duplicates, but you want to initialize certain properties if the pattern is created and update other properties if it is only matched.
-In this case, you can use `ON CREATE` or `ON MATCH` with the `SET` keyword to handle these situations.
-
-Let us look at an example.
-
-[source, cypher]
-----
-MERGE (m:Person {name: 'Mark'})-[r:IS_FRIENDS_WITH]-(j:Person {name:'Jennifer'})
-  ON CREATE SET r.since = date('2018-03-01')
-  ON MATCH SET r.updated = date()
-RETURN m, r, j
-----
-
-[#cypher-next-steps]
-== Next Steps
-
-Now that you have learned how to write create, read, update, and delete statement in Cypher, you can interact with data to get it into and out of Neo4j in a variety of ways.
-The next guide will show you how to handle filtering in Neo4j to return results with various criteria and to run fuzzy searches using ranges and partial values.
-
-[#cypher-resources]
-== Resources
-
-* link:/docs/cypher-manual/current/clauses/create/[Neo4j Cypher Manual: CREATE^]
-* link:/docs/cypher-manual/current/clauses/set/[Neo4j Cypher Manual: SET^]
-* link:/docs/cypher-manual/current/clauses/remove/[Neo4j Cypher Manual: REMOVE^]
-* link:/docs/cypher-manual/current/clauses/delete/[Neo4j Cypher Manual: DELETE^]
-* link:/docs/cypher-manual/current/clauses/merge/[Neo4j Cypher Manual: MERGE^]
-* link:/docs/cypher-manual/current/clauses/merge/#query-merge-on-create-on-match[Neo4j Cypher Manual: ON CREATE/ON MATCH^]
-
-include::partial$help.adoc[]
\ No newline at end of file
diff --git a/modules/cypher/partials/help.adoc b/modules/cypher/partials/help.adoc
deleted file mode 100644
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--- a/modules/cypher/partials/help.adoc
+++ /dev/null
@@ -1,4 +0,0 @@
-[TIP]
-.Are you struggling?
-If you need help with any of the information contained on this page, you can reach out to other members of our community.
-You can ask questions in the link:https://community.neo4j.com/c/neo4j-graph-platform/cypher/12?ref=guides[Cypher category on the Neo4j Community Site^].
diff --git a/modules/cypher/partials/next-steps.adoc b/modules/cypher/partials/next-steps.adoc
deleted file mode 100644
index dff3acf9..00000000
--- a/modules/cypher/partials/next-steps.adoc
+++ /dev/null
@@ -1,14 +0,0 @@
-== Next steps
-
-If you have any further questions about anything that has been covered in this section, you can ask in the link:https://community.neo4j.com/c/neo4j-graph-platform/cypher/12?ref=guides[Cypher category on the Neo4j Community Site^].
-We pride ourselves on having an open and engaging community and we would love you to be part of it.
-
-You can test your knowledge by taking these Beginner Learning Path courses:
-
-* https://graphacademy.neo4j.com/courses/neo4j-fundamentals/[Neo4j Fundamentals^]
-* https://graphacademy.neo4j.com/courses/cypher-fundamentals/[Cypher Fundamentals^]
-* https://graphacademy.neo4j.com/courses/modeling-fundamentals/[Data Modeling Fundamentals^]
-* https://graphacademy.neo4j.com/courses/importing-data/[Importing CSV Data into Neo4j^]
-* https://graphacademy.neo4j.com/courses/cypher-intermediate-queries/[Cypher Intermediate Queries^]
-
-Then link:https://neo4j.com/graphacademy/neo4j-certification/[Earn your free Neo4j Certification^] provided by the GraphAcademy.
\ No newline at end of file
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diff --git a/modules/genai-ecosystem/nav.adoc b/modules/genai-ecosystem/nav.adoc
new file mode 100644
index 00000000..cf61ed1d
--- /dev/null
+++ b/modules/genai-ecosystem/nav.adoc
@@ -0,0 +1,30 @@
+** xref:index.adoc[GenAI Ecosystem]
+*** Example Projects
+**** xref:llm-graph-builder.adoc[LLM Graph Builder]
+***** xref:llm-graph-builder-features.adoc[Features]
+***** xref:llm-graph-builder-deployment.adoc[Deployment]
+**** xref:rag-demo.adoc[GraphRAG Demo]
+**** xref:neoconverse.adoc[NeoConverse]
+**** xref:genai-stack.adoc[GenAI Stack]
+*** Neo4j GenAI Features
+**** xref:vector-search.adoc[Vector Index and Search]
+**** xref:apoc-genai.adoc[APOC GenAI]
+*** Cloud Examples
+**** xref:aws-demo.adoc[AWS Bedrock]
+**** xref:microsoft-azure-demo.adoc[Microsoft Azure OpenAI]
+**** xref:google-cloud-demo.adoc[Google Cloud Vertex AI]
+*** xref:genai-frameworks.adoc[GenAI Frameworks]
+**** xref:langchain.adoc[LangChain]
+**** xref:langchain-js.adoc[LangChainJS]
+**** xref:llamaindex.adoc[LlamaIndex]
+// **** link:xxx[Documentation]
+**** xref:spring-ai.adoc[SpringAI]
+// **** link:xxx[Documentation]
+**** xref:langchain4j.adoc[LangChain4j]
+// **** link:xxx[Documentation]
+**** xref:haystack.adoc[Haystack]
+// **** link:xxx[Documentation]
+**** xref:semantic-kernel.adoc[Semantic Kernel]
+// **** link:xxx[Documentation]
+**** xref:dspy.adoc[DSPy]
+// **** link:xxx[Documentation]
diff --git a/modules/genai-ecosystem/pages/_graphacademy_llm.adoc b/modules/genai-ecosystem/pages/_graphacademy_llm.adoc
new file mode 100644
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--- /dev/null
+++ b/modules/genai-ecosystem/pages/_graphacademy_llm.adoc
@@ -0,0 +1,5 @@
+:page-ad-overline-link: https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs
+:page-ad-overline: Learn
+:page-ad-title: Free Neo4j LLM courses from GraphAcademy
+:page-ad-description: Learn everything you need to know to build GenAI applications with LangChain and Neo4j
+:page-ad-link: https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs
diff --git a/modules/genai-ecosystem/pages/agent-neo.adoc b/modules/genai-ecosystem/pages/agent-neo.adoc
new file mode 100644
index 00000000..1a99420d
--- /dev/null
+++ b/modules/genai-ecosystem/pages/agent-neo.adoc
@@ -0,0 +1,121 @@
+= Agent Neo a Graph Powered Technical Document Chatbot
+// :toc:
+// :toc-placement: auto
+:imagesdir: https://dev.assets.neo4j.com/wp-content/uploads/2024/
+// :sectnums:
+:page-pagination:
+:page-product: agent-neo
+
+== Introduction
+
+Agent Neo is a Graph Retrieval-Augmentation Generation (Graph-RAG) chat application that leverages the Neo4j Label Property Graph to manage and query data. 
+Developed initially using a high-quality dataset of Neo4j documentation, the application utilizes metadata enrichment and post-processing to enhance RAG response quality. 
+
+
+== How It Works
+
+The workflow for Agent Neo is outlined as follows:
+
+. **Document Upload and Storage**: Documents are uploaded and stored as `Document` nodes within the graph.
+. **Chunk Processing**: Documents are segmented into smaller chunks, then processed and stored as nodes, linked to their respective documents.
+. **Topic Extraction and Clustering**: Topics are extracted using NLP post-processing, encoded, and linked to text chunks to form clusters.
+. **Similarity and Graph Structuring**: Text embeddings are utilized to identify similar text chunks, which are interconnected using "SIMILAR_TO" relationships to further structure the graph via the Leiden algorithm.
+. **Metadata Integration and Feedback Loop**: Session metadata and user feedback are integrated into the graph, enhancing data richness for future queries and analyses.
+
+image::agent-neo-structure.png[Graph Structure Example, width=600, align="center"]
+
+== Key enhancements
+
+* Capturing session and conversation metadata along with their associated questions and responses, linking them back to relevant entries.
+* Extracting topics using NLP techniques, encoding these into the graph, and associating them with corresponding text blocks to create a semantic hierarchy.
+* Clustering topics and encoding similarity features within the graph, involving generating text embeddings for topics, conducting k-nearest neighbors analyses, and creating "SIMILAR_TO" relationships.
+* Setting "SIMILAR_TO" relationships as undirected and aggregating similarity scores to apply the Leiden algorithm, subsequently forming Topic Groups that expand the semantic topic hierarchy.
+* Injecting session UUIDs generated on the frontend, integrating these with LangSmith LLM logging, and writing back LangSmith metadata statistics into the graph.
+* Capturing user response feedback through the UI and incorporating this into the graph to potentially inform response ranking and analyze application satisfaction.
+
+== Why Graph RAG?
+
+Agent Neo utilizes Graph-RAG technology to offer significant advantages:
+
+* *Completeness of Responses*: Combines classical vector similarity searches with graph traversals to capture a broader context around text similarities beyond simple metric comparisons.
+* *Explainability & Observability*: Enhances the transparency and explainability of operations by incorporating vectors within a graph structure, moving away from the "black box" nature of traditional models.
+* *Rapid Iteration and Integration*: Facilitates quick iterations and seamless integration of diverse data sources through Neo4j’s Label Property Graph, supported by in-platform feature engineering capabilities.
+
+image::agent-neo.png[Agent Neo Architecture, width=600, align="center"]
+
+== Installation
+
+=== Prerequisites
+
+* Docker installed on your machine
+* Python 3.8 or higher
+* Node.js 12 or higher
+
+=== Setup Guide
+
+To set up Agent Neo, execute the following steps:
+
+[source,bash]
+----
+# Clone the Agent Neo repository:
+git clone https://github.com/neo4j-labs/agent-neo.git
+cd agent-neo
+
+# Launch the application using Docker Compose
+docker-compose up
+----
+
+Visit `http://localhost:8000` in your web browser to start using Agent Neo. Review the Dockerfile and associated services for production deployment details.
+
+== Relevant Links
+
+[cols="1,4"]
+|===
+| Online | https://chatbot.agent-neo-chat.com/[React Front End^] https://agent-neo.streamlit.app/[Older Streamlit Front End^]
+| icon:user[] Authors | Dan Bukowski, Alex Gilmore, Alex Fournier, Nathan Smith, Morgan Senechal, Roni Das, Eric Monk, Kumar Subbiah Shunmugathai, Michael Hunger
+| icon:comments[] Community Support | https://community.neo4j.com/c/neo4j-graph-platform/genai/214[Neo4j Online Community^]
+| icon:github[] Code Repository | https://github.com/neo4j-labs/agent-neo[GitHub^]
+| icon:github[] Issues | https://github.com/neo4j-labs/agent-neo/issues[GitHub Issues^]
+// | icon:book[] Documentation |
+|===
+
+== Further Resources
+
+* *Graph Data Models for RAG Applications*: https://medium.com/neo4j/graph-data-models-for-rag-applications-d59c29bb55cc
+* *Topic Extraction with Neo4j Graph Data Science for Better Semantic Search*: https://medium.com/@nsmith_piano/topic-extraction-with-neo4j-graph-data-science-for-better-semantic-search-c5b7f56c7715
+
+////
+
+== Contributors and Contact
+Agent Neo is a collaborative effort. If you are interested in contributing or have questions, please contact the core team:
+
+* **Dan Bukowski** (Architect/PM) - `dan.bukowski@neo4j.com`
+* **Alex Gilmore** (Architect) - `alex.gilmore@neo4j.com`
+* **Alex Fournier** (Architect/PM) - `alex.fournier@neo4j.com`
+* **Nathan Smith** (Graph Data Science/Database) - `nathan.smith@neo4j.com`
+* **Morgan Senechal** (Frontend) - `morgan.senechal@neo4j.com`
+* **Roni Das** (Frontend) - `roni.das@neo4j.com`
+* **Eric Monk** (Technical Guidance) - `eric.monk@neo4j.com`
+* **Kumar Subbiah Shunmugathai** (Technical Guidance) - `kumar.subbiah@neo4j.com`
+* **Michael Hunger** (PM) - `michael.hunger@neo4j.com`
+////
+
+== Raising Issues and Feature Requests
+
+To report bugs or suggest features:
+
+* Visit our GitHub Issues page: link:https://github.com/neo4j-labs/agent-neo/issues[GitHub Issues]
+* For immediate support, especially for urgent issues, please use the `urgent` tag when creating a new issue.
+
+== Change Log / Feature Development
+
+=== Version 1.1 - *2024-06-01*
+
+- Integrated Langchain metadata statistics and exposed via Neodash.
+- Implemented rating feedback and semantic topic clustering.
+- Expanded testing protocols.
+- Enhanced GraphReader functionality for combined traversal and vector search.
+- Implementing frontend feature using bloom vis sdk to enhance observability
+
+=== Version 1.0 - *2024-01-01*
+- Initial release: basic document upload, chunk processing, and initial graph structure setup.
diff --git a/modules/genai-ecosystem/pages/apoc-genai.adoc b/modules/genai-ecosystem/pages/apoc-genai.adoc
new file mode 100644
index 00000000..a70f0934
--- /dev/null
+++ b/modules/genai-ecosystem/pages/apoc-genai.adoc
@@ -0,0 +1,101 @@
+= APOC GenAI Procedures
+include::_graphacademy_llm.adoc[]
+:slug: apoc-genai
+:author: 
+:category: genai-ecosystem
+:tags: embeddings, chat, completion, image generation, genai, query generation, schema explanation
+:neo4j-versions: 5.11+
+:page-pagination:
+:page-product: apoc-genai
+
+The APOC Extended procedure library integrates with various AI/ML platforms to provide a set of useful utilities for quickly demonstrate GenAI features.
+The library includes procedures for generating texts, images, and vector embedding.
+It also allows the generation and execution of Cypher queries from natural language as well as schema explanation.
+
+APOC Extended integrates with 
+
+* https://neo4j.com/labs/apoc/5/ml/openai/[(Azure) OpenAI^], 
+* https://neo4j.com/labs/apoc/5/ml/bedrock/[AWS Bedrock^], 
+* https://neo4j.com/labs/apoc/5/ml/vertexai/[Google Vertex AI^], 
+* https://neo4j.com/labs/apoc/5/ml/watsonai/[IBM Watson AI^]
+* and other https://neo4j.com/labs/apoc/5/ml/openai/#openai_compatible_provider[ML platforms that have OpenAI compatible APIs^].
+
+== Installation
+
+APOC extended is available as a plugin for Neo4j. To install APOC, follow the instructions in the APOC Manual.
+In most installation you can download the https://github.com/neo4j-contrib/neo4j-apoc-procedures/releases[appropriate release^] and place the JAR file in the `plugins` directory of your Neo4j installation.
+
+In https://sandbox.neo4j.com[Neo4j Sandbox^], APOC is already installed and you can start using the procedures right away.
+
+NOTE: APOC Extended is not available in the Neo4j Aura Cloud Service.
+
+== Usage
+
+Here is a quick example:
+
+[source,cypher]
+----
+// generate embedding
+CALL apoc.ml.openai.embedding(['Knowledge Graphs work well with LLMs'], $apiKey, {}) yield index, text, embedding;
+
+// generate text
+CALL apoc.ml.openai.completion('What color is the sky? Answer in one word: ', $apiKey, {config}) yield value;
+
+/*
+{ created=1684248202, model="text-davinci-003", id="cmpl-7GqBWwX49yMJljdmnLkWxYettZoOy",
+  usage={completion_tokens=2, prompt_tokens=12, total_tokens=14},
+  choices=[{finish_reason="stop", index=0, text="Blue", logprobs=null}], object="text_completion"}
+*/
+
+CALL apoc.ml.query("What movies did Tom Hanks play in?", {apiKey: $apiKey}) yield value, query
+RETURN *;
+
+
+CALL apoc.ml.schema({apiKey: $apiKey});
+/* 
+The graph database schema represents a system where users can follow other users and review movies. 
+Users (:Person) can either follow other users (:Person) or review movies (:Movie). 
+The relationships allow users to express their preferences and opinions about movies. 
+This schema can be compared to social media platforms where users can follow each other and leave reviews or ratings for movies they have watched. 
+It can also be related to movie recommendation systems where user preferences and reviews play a crucial role in generating personalized recommendations.
+*/
+----
+
+Most of the procedures take besides your input the configuration for API keys or tokens as well as the model name.
+
+The API keys can also be configured in `apoc.conf` to make the functionality available to all your users, make sure to protect they keys with rate and budget limits.
+
+== Functionality Includes
+
+* Generate vector embeddings
+* Text completion
+* Chat completion with multiple messages having roles for system, assistant and user
+* Image generation
+* Cypher query generation from natural language
+* Schema explanation
+
+== Documentation
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | Neo4j Labs contributors, esp. https://github.com/vga91[Giuseppe Villani^]
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Repository | https://github.com/neo4j-contrib/neo4j-apoc-procedures[GitHub]
+| icon:github[] Issues | https://github.com/neo4j-contrib/neo4j-apoc-procedures/issues
+| icon:gift[] Releases | https://github.com/neo4j-contrib/neo4j-apoc-procedures/releases
+| icon:book[] Documentation | https://neo4j.com/labs/apoc/5/ml/
+|===
+
+
+////
+== Videos & Tutorials
+
+++++
+
+++++
+
+== Highlighted Articles
+
+* https://medium.com/neo4j/building-lightweight-rag-applications-using-neo4j-91661cf258bb[Building Lightweight RAG Applications using Neo4j^]
+////
\ No newline at end of file
diff --git a/modules/genai-ecosystem/pages/aws-demo.adoc b/modules/genai-ecosystem/pages/aws-demo.adoc
new file mode 100644
index 00000000..9505bd85
--- /dev/null
+++ b/modules/genai-ecosystem/pages/aws-demo.adoc
@@ -0,0 +1,38 @@
+= AWS Demo
+include::_graphacademy_llm.adoc[]
+:slug: aws-demo
+:author: Ben Lackey
+:category: genai-ecosystem
+:tags: rag, demo, retrieval augmented generation, chatbot, edgar, sec filings, aws, bedrock
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: aws-demo
+
+This is a sample notebook and web application which shows how Amazon Bedrock and Titan can be used with Neo4j. It shows how to leverage generative AI to build and consume a knowledge graph in Neo4j.
+
+The dataset we're using is from the SEC's EDGAR system.
+
+The dataflow in this demo consists of two parts:
+
+* Ingestion - we read the EDGAR files with Bedrock, extracting entities and relationships from them which is then ingested into a Neo4j database deployed from AWS Marketplace.
+* Consumption - A user inputs natural language into a chat UI. Bedrock converts that to Neo4j Cypher which is run against the database. This flow allows non technical users to query the database.
+
+== Installation
+
+The Demo is available on GitHub: https://github.com/neo4j-partners/neo4j-generative-ai-aws
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:github[] Code Repository | https://github.com/neo4j-partners/neo4j-generative-ai-aws[GitHub]
+| APN Blog Post | https://aws.amazon.com/blogs/apn/leveraging-neo4j-and-amazon-bedrock-for-an-explainable-secure-and-connected-generative-ai-solution[Link]
+| Demo Video | https://www.youtube.com/watch?v=nV3-KKEZnD4&list=PLG3nTnYVz3nya8Me9-Xj9vEuLYIOk03ba&index=11&t=14s[Link]
+| Press Release | https://neo4j.com/press-releases/neo4j-aws-bedrock-integration[Link]
+| Slides | https://docs.google.com/presentation/d/1pnJn1GV7tm6Gr-K-0bEB5TlkPRoDbqLTKkKjWdr3eZs/edit?usp=sharing[Link]
+|===
+
+== Videos & Tutorials
+
+++++
+
+++++
diff --git a/modules/genai-ecosystem/pages/dspy.adoc b/modules/genai-ecosystem/pages/dspy.adoc
new file mode 100644
index 00000000..01089207
--- /dev/null
+++ b/modules/genai-ecosystem/pages/dspy.adoc
@@ -0,0 +1,46 @@
+= DSPy Neo4j Integration
+:slug: dspy
+:author: Tomaz Bratanic
+:category: labs
+:tags: dspy, rag, vector search, neo4j
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: dspy
+
+
+DSPy is a framework for algorithmically optimizing LM prompts and weights, especially when LMs are used one or more times within a pipeline.
+
+The Neo4j integration allows for vector search.
+
+Here is an overview of the https://dspy-docs.vercel.app/api/retrieval_model_clients/Neo4jRM[DSPy Integrations^].
+
+== Installation
+
+[source,shell]
+----
+pip install dspy neo4j
+----
+== Functionality Includes
+
+* `Neo4jRM` - is a typical retriever component which can be used to query vector store index and find related Documents.
+
+[source,python]
+----
+from dspy.retrieve.neo4j_rm import Neo4jRM
+import os
+
+os.environ["NEO4J_URI"] = 'bolt://localhost:7687'
+os.environ["NEO4J_USERNAME"] = 'neo4j'
+os.environ["NEO4J_PASSWORD"] = 'password'
+os.environ["OPENAI_API_KEY"] = 'sk-'
+
+retriever_model = Neo4jRM(
+    index_name="vector",
+    text_node_property="text"
+)
+
+results = retriever_model("Explore the significance of quantum computing", k=3)
+
+for passage in results:
+    print("Document:", passage, "\n")
+----
\ No newline at end of file
diff --git a/modules/genai-ecosystem/pages/genai-frameworks.adoc b/modules/genai-ecosystem/pages/genai-frameworks.adoc
new file mode 100644
index 00000000..5b0d3851
--- /dev/null
+++ b/modules/genai-ecosystem/pages/genai-frameworks.adoc
@@ -0,0 +1,43 @@
+= GenAI Frameworks
+include::_graphacademy_llm.adoc[]
+:imagesdir: https://s3.amazonaws.com/dev.assets.neo4j.com/wp-content/uploads
+:slug: genai-frameworks
+:author: Michael Hunger, Tomaz Bratanic, Oskar Hane
+:category: labs
+:tags: llm, genai, generative ai, large language models, integrations, rag, vector search, retrieval augmented generation, llamaindex, langchain, haystack, frameworks
+:neo4j-versions: 5.X
+:page-pagination:
+:page-product: GenAI Frameworks
+
+While current foundation models (language, image, speech, embeddings) are available through APIs and can be used just with a http request or a few lines of code, the devil is as always in the details. It is not just about a single API call but full applications, workflows and architectures.
+
+In the last years a number of really powerful open-source orchestration libraries have been developed, many with a large contributor community and a lot of momentum. 
+Even the large cloud providers and AI companies contributed and are using these libraries as in this fast moving world it is hard to keep up otherwise.
+
+Those libraries cover a number of aspects:
+
+- LLM usage, including Prompt and Output 
+- Embedding generation
+- Vector and database integration
+- RAG workflows
+- Agentic workflows
+- Monitoring, Observability and Deployment
+
+== GenAI Frameworks
+
+Neo4j and our community have contributed integrations to many of these frameworks. You can find overviews of these integrations in the pages of this section, as well as code examples, tutorials and more.
+
+* xref:langchain.adoc[LangChain (Python)]
+* xref:langchain-js.adoc[LangChainJS]
+* xref:llamaindex.adoc[LLamaIndex]
+* xref:spring-ai.adoc[Spring AI]
+* xref:langchain4j.adoc[LangChain4j]
+* xref:haystack.adoc[Haystack]
+* xref:semantic-kernel.adoc[Semantic Kernel]
+* xref:dspy.adoc[DSPy]
+
+== GraphAcademy Courses
+
+If you want to learn how LLMs and Knowledge Graphs combine to improve GenAI applications, check out the https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs[Neo4j & LLM courses on GraphAcademy^].
+
+image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/llm-fundamentals.png[link=https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs]
diff --git a/modules/genai-ecosystem/pages/genai-stack.adoc b/modules/genai-ecosystem/pages/genai-stack.adoc
new file mode 100644
index 00000000..0a3ee842
--- /dev/null
+++ b/modules/genai-ecosystem/pages/genai-stack.adoc
@@ -0,0 +1,91 @@
+= GenAI Stack
+include::_graphacademy_llm.adoc[]
+:slug: genai-stack
+:author: Michael Hunger, Tomaz Bratanic, Oskar Hane, Jim Clar, Harrison Chase, Michael Chiang
+:category: labs
+:tags: docker, langchain, ollama, neo4j, vector index, vector search, chatbot, rag, streamlit, pdf, stackoverflow
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: genai-stack
+
+image::https://dist.neo4j.com/wp-content/uploads/20231005063102/import-embed-data-stack-overflow.png[width=800]
+
+The GenAI Stack is a collaboration between Docker, Neo4j, LangChain and Ollama launched at DockerCon 2023.
+
+== Installation
+
+On MacOS you have to install and start https://ollama.ai/[Ollama] first, then you can use the GenAI Stack.
+
+[source,shell]
+----
+git clone https://github.com/docker/genai-stack
+cd genai-stack
+
+# optionally copy env.example to .env and add your OpenAI/Bedrock/Neo4j credentials for remote models
+docker compose up
+----
+
+== Functionality Includes
+
+* Docker Setup with local or remote LLMs, Neo4j and LangChain demo applications
+
+* Pull Ollama Models and sentence transformer as needed
+* Import Stackoverflow Questions and Answers for a certain tag, e.g. `langchain`
+* Create Knowledge Graph and vector embeddings for questions and answers
+
+image::https://dist.neo4j.com/wp-content/uploads/20231005063228/query-imported-data.png[]
+
+* Streamlit Chat App with vector search and GraphRAG (vector + graph) answer generation
+* Creating "Support Tickets" for unanswered questions taking good questions from StackOverflow and the actual question into account
+* PDF chat with loading PDFs, chunking, vector indexing and search to generate answers
+* Python Backend and Svelte Front-End Chat App with vector search and GraphRAG (vector + graph) answer generation
+
+image::https://dist.neo4j.com/wp-content/uploads/20231005063244/input-answer-sources.png[]
+
+== Documentation
+
+A detailed walkthrough of the GenAI Stack is available at https://neo4j.com/developer-blog/genai-app-how-to-build/.
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | https://github.com/jexp[Michael Hunger^], https://github.com/tomasojo[Tomaz Bratanic^], https://github.com/oskarhane[Oskar Hane^] and many contributors
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Repository | https://github.com/docker/genai-stack[GitHub]
+| icon:github[] Issues | https://github.com/docker/genai-stack/issues
+// | icon:book[] Documentation | https://github.com/docker/genai-stack
+|===
+
+== Videos & Tutorials
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+== Highlighted Articles
+
+* https://neo4j.com/emil/introducing-genai-stack-developers/[Introducing the GenAI Stack - Emil Eifrem^]
+* https://www.docker.com/blog/introducing-a-new-genai-stack/[Introducing a New GenAI Stack - Docker^]
+* https://collabnix.com/getting-started-with-genai-stack-powered-with-docker-langchain-neo4j-and-ollama/[Getting Started with GenAI Stack powered with Docker, LangChain, Neo4j and Ollama - Collabnix^]
+
+* https://medium.com/@yu-joshua/fast-track-to-mastery-neo4j-genai-stack-for-efficient-llm-applications-87acb0db2cef[Fast Track to Mastery: Neo4j GenAI Stack for Efficient LLM Applications^]
\ No newline at end of file
diff --git a/modules/genai-ecosystem/pages/google-cloud-demo.adoc b/modules/genai-ecosystem/pages/google-cloud-demo.adoc
new file mode 100644
index 00000000..b445beab
--- /dev/null
+++ b/modules/genai-ecosystem/pages/google-cloud-demo.adoc
@@ -0,0 +1,49 @@
+= Google Cloud Vertex AI
+include::_graphacademy_llm.adoc[]
+:slug: google-cloud-demo
+:author: Ben Lackey, Michael Hunger
+:category: genai-ecosystem
+:tags: rag, demo, retrieval augmented generation, chatbot, google, vertexai, gemini, langchain, reasoning-engine
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: google-cloud-demo
+//:imagesdir: https://dev.assets.neo4j.com/wp-content/uploads/2024/
+
+== Google Cloud Generative AI Demos
+
+This example consists of two sample applications that show how to use Neo4j with the generative AI capabilities in Google Cloud Vertex AI. We explore how to leverage Google generative AI to build and consume a knowledge graph in Neo4j.
+
+* assetmanager - Parses data from the SEC containing quarterly filings of asset managers. We build a graph containing assets managers and the securities they hold. A chatbot that queries the knowledge graph is included as well.
+* resume - Extracts entities like jobs and skills from a collection of resumes, then builds a graphs showing what talents individuals share. A chatbot that queries the knowledge graph is included as well.
+
+=== Installation
+
+The Demo is available on GitHub: https://github.com/neo4j-partners/neo4j-generative-ai-google-cloud
+
+=== Relevant Links
+
+[cols="1,4"]
+|===
+| icon:github[] Code Repository | https://github.com/neo4j-partners/neo4j-generative-ai-google-cloud[GitHub]
+| Blog Post | https://cloud.google.com/blog/topics/partners/build-intelligent-apps-with-neo4j-and-google-generative-ai[Link]
+| Demo Video | https://www.youtube.com/watch?v=UGWVMfo5Pew[Link]
+| Slides | https://docs.google.com/presentation/d/1vIXaZCWX5fN5m6y50Z7nM6RlTSJR7vErUrfXlWR0BLY/edit?usp=sharing[Link]
+| Press Release | https://neo4j.com/press-releases/neo4j-google-cloud-vertex-ai[Link]
+|===
+
+=== Videos & Tutorials
+
+++++
+
+++++
+
+== Knowledge Graph Generation with Gemini Pro
+
+The xref:llm-graph-builder.adoc[LLM Graph Builder] that extracts entities from unstructured text (PDFs, YouTube Transcripts, Wikipedia) can be configured to use VertexAI both as embedding model and Gemnini as LLM for the extraction.
+PDFs can be also be loaded from Google Cloud buckets.
+
+It uses the underlying llm-graph-transformer library that we contributed to LangChain.
+
+// TODO image
+
+// The Demo is available https://llm-graph-builder-gemini.neo4jlabs.com[online with with Google Gemini on Vertex AI^].
diff --git a/modules/genai-ecosystem/pages/google-cloud-genai-integrations.adoc b/modules/genai-ecosystem/pages/google-cloud-genai-integrations.adoc
new file mode 100644
index 00000000..68869591
--- /dev/null
+++ b/modules/genai-ecosystem/pages/google-cloud-genai-integrations.adoc
@@ -0,0 +1,40 @@
+= Google Cloud GenAI Integrations
+include::_graphacademy_llm.adoc[]
+:slug: google-cloud-genai-integrations
+:author: Ben Lackey, Michael Hunger
+:category: genai-ecosystem
+:tags: rag, demo, retrieval augmented generation, chatbot, google, vertexai, gemini, langchain, reasoning-engine
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: google-cloud-demo
+//:imagesdir: https://dev.assets.neo4j.com/wp-content/uploads/2024/
+
+== Function Calling with Gemini
+
+// TODO
+
+== Quering Neo4j via Vertex AI Extensions
+
+// TODO
+
+== Deploying GenAI Applications and APIs with Vertex AI Reasoning Engine
+
+GenAI developers, familiar with orchestration tools and architectures often face challenges when deploying their work to production. 
+Google's https://cloud.google.com/vertex-ai/generative-ai/docs/reasoning-engine/overview[Vertex AI Reasoning Engine Runtime^](preview) now offers an easy way to deploy, scale, and monitor GenAI applications and APIs without in-depth knowledge of containers or cloud configurations. 
+
+Compatible with various orchestration frameworks, including xref:langchain.adoc[LangChain], this solution allows developers to use the https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk[Vertex AI Python SDK^] for setup, testing, deployment.
+
+It works like this:
+
+- Create a Python class for your GenAI app class to store environment info and static data in the constructor.
+- Initialize your orchestration framework in a `set_up` method at  startup.
+- Process user queries with a `query` method, returning text responses.
+- Deploy your GenAI API with `llm_extension.ReasoningEngine.create`, including class instance and requirements.
+
+Our new integrations with Google Cloud, combined with our extensive LangChain integrations, allow you to seamlessly incorporate Neo4j knowledge graphs into your GenAI stack. 
+You can use LangChain and other orchestration tools to deploy RAG architectures, like GraphRAG, with Reasoning Engine Runtime.
+
+You can see below an example of GraphRAG on a Company News Knowledge Graph using LangChain, Neo4j and Gemini.
+
+image::https://dev.assets.neo4j.com/wp-content/uploads/2024/reasoning-engine-graphrag.png[]
+
diff --git a/modules/genai-ecosystem/pages/haystack.adoc b/modules/genai-ecosystem/pages/haystack.adoc
new file mode 100644
index 00000000..0410e781
--- /dev/null
+++ b/modules/genai-ecosystem/pages/haystack.adoc
@@ -0,0 +1,71 @@
+= Haystack Neo4j Integration
+:slug: haystack
+:author: 
+:category: genai-ecosystem
+:tags: haystack, integration, llm, neo4j, vector index
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: haystack
+
+
+An integration of Neo4j graph database with Haystack v2.0 by deepset. In Neo4j Vector search index is being used for storing document embeddings and dense retrievals.
+
+The library allows using Neo4j as a DocumentStore, and implements the required Protocol methods. You can start working with the implementation by importing it from neo4_haystack package.
+
+== Installation
+
+[source,python]
+----
+# pip install neo4j-haystack
+from neo4j_haystack import Neo4jDocumentStore
+
+document_store = Neo4jDocumentStore(
+    url="bolt://localhost:7687",
+    username="neo4j",
+    password="passw0rd",
+    database="neo4j",
+    embedding_dim=384,
+    embedding_field="embedding",
+    index="document-embeddings", # The name of the Vector Index in Neo4j
+    node_label="Document", # Providing a label to Neo4j nodes which store Documents
+)
+----
+
+== Functionality Includes
+
+* `Neo4jEmbeddingRetriever` - is a typical retriever component which can be used to query vector store index and find related Documents. The component uses Neo4jDocumentStore to query embeddings.
+* `Neo4jDynamicDocumentRetriever` is also a retriever component in a sense that it can be used to query Documents in Neo4j. However it is decoupled from Neo4jDocumentStore and allows to run arbitrary Cypher query to extract documents. Practically it is possible to query Neo4j same way Neo4jDocumentStore does, including vector search.
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | https://github.com/prosto[Sergey Bondarenco^]
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Repository | https://github.com/prosto/neo4j-haystack[GitHub]
+| icon:github[] Issues | https://github.com/prosto/neo4j-haystack/issues
+| icon:book[] Documentation | https://haystack.deepset.ai/integrations/neo4j-document-store
+| icon:book[] Example Notebook | https://github.com/prosto/neo4j-haystack-playground
+|===
+
+
+== Videos & Tutorials
+
+Online Meetup / Livestream with Sergey Bondarenco and Andreas Kollegger
+
+Neo4j & Haystack Part 1: Knowledge Graphs for RAG:
+
+++++
+
+++++
+
+Neo4j & Haystack Part 2: How the Integration Works:
+
+++++
+
+++++
+
+////
+== Highlighted Articles
+
+// link:https://medium.com/neo4j/....[article^]
+////
diff --git a/modules/genai-ecosystem/pages/index.adoc b/modules/genai-ecosystem/pages/index.adoc
new file mode 100644
index 00000000..27f770a2
--- /dev/null
+++ b/modules/genai-ecosystem/pages/index.adoc
@@ -0,0 +1,99 @@
+= GenAI Ecosystem
+include::_graphacademy_llm.adoc[]
+:imagesdir: https://s3.amazonaws.com/dev.assets.neo4j.com/wp-content/uploads
+:slug: genai-ecosystem
+:author: Michael Hunger, Tomaz Bratanic, Oskar Hane
+:category: labs
+:tags: llm, genai, generative ai, large language models, integrations, rag, vector search, retrieval augmented generation, 
+:neo4j-versions: 5.X
+:page-pagination:
+:page-product: GenAI Ecosystem
+
+image::https://dist.neo4j.com/wp-content/uploads/20231030151119/genai-art-diagram-1.svg[width=800]
+
+
+Knowledge graphs bring more accurate responses, rich context, and explainability to each generative AI model interaction. 
+By combining knowledge graphs with native vector search, you get the best of both worlds with Graph RAG (Retrieval Augmented Generation). 
+
+Language models also can be used to extract entities and their relationships from unstructured text to build up and enrich knowledge graphs.
+
+== How to get started
+
+1. For an high level overview, have a look at https://neo4j.com/generativeai/[neo4j.com/genai^]  
+2. Use the xref:llm-graph-builder.adoc[LLM-Knowledge Graph Builder] to turn your own documents into a knowledge graph
+3. If you want to learn more take one of the https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs[GenAI GraphAcademy courses^]
+4. Pick your xref:genai-frameworks.adoc[GenAI framework of choice] and start building your own GenAI applications with Neo4j
+
+== GraphAcademy Courses
+
+If you want to learn how LLMs and Knowledge Graphs combine to improve GenAI applications, check out the https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs[Neo4j & LLM courses on GraphAcademy^].
+
+image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/llm-fundamentals.png[link=https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs]
+
+== GenAI Ecosystem
+
+The Neo4j GenAI Ecosystem is a collection of tools and integrations that make it easy to use LLMs with Neo4j.
+
+=== GraphRAG
+
+GraphRAG combines an ingestion process that extracts entities and relationships from unstructured text and further uses graph algorithms for enrichment and summarization. 
+The retrieval step then uses the knowledge graph in combination with vector search to navigate to more relevant information than just the initial text chunks.
+
+* https://neo4j.com/blog/graphrag-manifesto/[The GraphRAG Manifesto]
+* https://dev.neo4j.com/dlai-kg[DeepLearning AI Knowledge Graph Course^]
+* http://discord.gg/graphrag[GraphRAG Discord^]
+// * https://huggingface.co/graphrag[GraphRAG HuggingFace Paper Collection^]
+* https://dev.neo4j.com/free-kg-book[(free) Knowledge Graph Book^]
+* https://neo4j.com/developer-blog/global-graphrag-neo4j-langchain/[Implementing GraphRAG with Neo4j, GDS and LangChain]
+* https://microsoft.github.io/graphrag/[Microsoft's GraphRAG project]
+
+=== GraphRAG Ecosystem Tools
+
+In Neo4j Labs we built a number of tools, to demonstrate the power of combining graphs with LLMs. All these tools are open source, you can use and contribute to them or learn and build your own.
+
+* xref:llm-graph-builder.adoc[LLM Knowledge Graph Builder]
+* xref:neoconverse.adoc[NeoConverse multi-dataset query, chat, visualization]
+* xref:rag-demo.adoc[GraphRAG (Retrieval Augmented Generation) Demo]
+* xref:genai-stack.adoc[GenAI Stack (with Docker, Ollama, Neo4j, and LangChain)]
+
+=== Cloud Examples
+
+Neo4j has worked with the main cloud providers to create GenAI integrations and examples on their platforms.
+
+* xref:aws-demo.adoc[AWS Demo]
+* xref:microsoft-azure-demo.adoc[Microsoft Azure Demo]
+* xref:google-cloud-demo.adoc[Google Cloud Demo]
+
+=== Neo4j GenAI Features
+
+Neo4j added a number of features to make it easier to build GenAI applications and integrate LLMs with knowledge graphs.
+
+* xref:vector-search.adoc[Vector Index & Search]
+* xref:apoc-genai.adoc[APOC GenAI Procedures]
+
+=== GenAI Frameworks
+
+Neo4j and our community have contributed integrations to many of these frameworks. 
+You can find overviews of these integrations in the pages of this section, as well as code examples, tutorials and more.
+
+* xref:langchain.adoc[LangChain (Python)]
+* xref:langchain-js.adoc[LangChainJS]
+* xref:llamaindex.adoc[LLamaIndex]
+* xref:spring-ai.adoc[Spring AI]
+* xref:langchain4j.adoc[LangChain4j]
+* xref:haystack.adoc[Haystack]
+* xref:semantic-kernel.adoc[Semantic Kernel]
+* xref:dspy.adoc[DSPy]
+
+== Highlighted Articles
+
+* https://neo4j.com/developer-blog/tagged/llm/[Neo4j Developer Blog: Large Language Models^]
+* https://bratanic-tomaz.medium.com/[Tomaz Bratanic's Medium Blog^]
+* https://medium.com/@yu-joshua[Joshua Yu's Medium Blog^]
+
+== Videos & Tutorials
+
+- https://neo4j.com/blog/unifying-llm-knowledge-graph/[Unifying Large Language Models and Knowledge Graphs Use-Case and Integrations]
+
+++++
+++++
diff --git a/modules/genai-ecosystem/pages/langchain-js.adoc b/modules/genai-ecosystem/pages/langchain-js.adoc
new file mode 100644
index 00000000..a968786f
--- /dev/null
+++ b/modules/genai-ecosystem/pages/langchain-js.adoc
@@ -0,0 +1,172 @@
+= LangchainJS
+include::_graphacademy_llm.adoc[]
+:slug: langchain-js
+:author: 
+:category: genai-ecosystem
+:tags: langchain, langchain js, javascript, typescript, neo4j, vector, index
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: langchain-js
+
+image::https://dist.neo4j.com/wp-content/uploads/20230615211357/1AH05dvGA_7db_EMySc9AAw.png[width=800]
+
+https://js.langchain.com/docs/get_started/introduction[LangChain.js^] is a JavaScript/TypeScript implementation of the LangChain library.
+It uses similar concepts, with Prompts, Chains, Transformers, Document Loaders, Agents, and more.
+
+Here is an overview of the https://js.langchain.com/docs/use_cases/graph/quickstart[Graph Integrations^].
+
+The Neo4j Integration makes the xref:vector-search.adoc[Neo4j Vector] index as well as Cypher generation and execution available in the LangChain.js library.
+
+Learn how to build a chatbot in TypeScript using LangChain.js with our new https://graphacademy.neo4j.com/courses/llm-chatbot-typescript/[GraphAcademy course^].
+
+image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/llm-chatbot-typescript.png[width=800,link="https://graphacademy.neo4j.com/courses/llm-chatbot-typescript/"]
+
+== Installation
+
+[source,shell]
+----
+npm install @langchain/openai neo4j-driver @langchain/community
+----
+
+== Functionality Includes
+
+=== Neo4jVector
+
+The Neo4j Vector integration supports a number of operations
+
+* create vector from langchain documents
+* query vector
+* query vector with additional graph retrieval Cypher query
+* construct vector instance from existing graph data
+* hybrid search
+
+// todo include
+[source,javascript]
+----
+import { OpenAIEmbeddings } from "@langchain/openai";
+import { Neo4jVectorStore } from "@langchain/community/vectorstores/neo4j_vector";
+
+// Configuration object for Neo4j connection and other related settings
+const config = {
+  url: "bolt://localhost:7687", // URL for the Neo4j instance
+  username: "neo4j", // Username for Neo4j authentication
+  password: "pleaseletmein", // Password for Neo4j authentication
+  indexName: "vector", // Name of the vector index
+  keywordIndexName: "keyword", // Name of the keyword index if using hybrid search
+  searchType: "vector" as const, // Type of search (e.g., vector, hybrid)
+  nodeLabel: "Chunk", // Label for the nodes in the graph
+  textNodeProperty: "text", // Property of the node containing text
+  embeddingNodeProperty: "embedding", // Property of the node containing embedding
+};
+
+const documents = [
+  { pageContent: "what's this", metadata: { a: 2 } },
+  { pageContent: "Cat drinks milk", metadata: { a: 1 } },
+];
+
+const neo4jVectorIndex = await Neo4jVectorStore.fromDocuments(
+  documents,
+  new OpenAIEmbeddings(),
+  config
+);
+
+const results = await neo4jVectorIndex.similaritySearch("water", 1);
+
+console.log(results);
+
+/*
+  [ Document { pageContent: 'Cat drinks milk', metadata: { a: 1 } } ]
+*/
+
+await neo4jVectorIndex.close();
+----
+
+==== Hybrid Search
+
+Hybrid search combines vector search with fulltext search with re-ranking and de-duplication of the results.
+
+=== Neo4j Graph
+
+The Neo4j Graph integration is a wrapper for the Neo4j Python driver. 
+It allows querying and updating the Neo4j databsae in a simplified manner from LangChain.
+Many integrations allow to use the Neo4j Graph as a source of data for LangChain.
+
+
+[source,javascript]
+----
+import { Neo4jGraph } from "@langchain/community/graphs/neo4j_graph";
+
+const graph = await Neo4jGraph.initialize({ NEO4J_URL, NEO4J_USERNAME, NEO4J_PASSWORD });
+
+QUERY = """
+"MATCH (m:Movie)-[:IN_GENRE]->(:Genre {name:$genre}) 
+RETURN m.title, m.plot 
+ORDER BY m.imdbRating DESC LIMIT 5"
+"""
+
+await graph.query(QUERY, genre="action")
+----
+
+=== CypherQAChain
+
+The CypherQAChain is a LangChain component that allows you to interact with a Neo4j graph database in natural language.
+Using an LLM and the graph schema it translates the user question into a Cypher query, executes it against the graph and uses the returned context information and the original question with a second LLM to generate a natural language response.
+
+[source,javascript]
+----
+import { Neo4jGraph } from "@langchain/community/graphs/neo4j_graph";
+import { OpenAI } from "@langchain/openai";
+import { GraphCypherQAChain } from "langchain/chains/graph_qa/cypher";
+
+const graph = await Neo4jGraph.initialize({ NEO4J_URL, NEO4J_USERNAME, NEO4J_PASSWORD });
+const model = new OpenAI({ temperature: 0 });
+
+// Populate the database with two nodes and a relationship
+await graph.query(`
+  CREATE (a:Actor {name:'Bruce Willis'})
+  -[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})
+`);
+
+await graph.refreshSchema();
+
+const chain = GraphCypherQAChain.fromLLM({ llm: model, graph });
+
+const res = await chain.run("Who acted in Pulp Fiction?");
+// Bruce Willis acted in Pulp Fiction.
+----
+
+=== Knowledge Graph Construction
+
+Creating a Knowledge Graph from unstructured data like PDF documents used to be a complex and time-consuming task that required training and using dedicated, large NLP models.
+
+The https://js.langchain.com/docs/use_cases/graph/construction[Graph Transformers^] are tools that allows you to extract structured data from unstructured documents and transform it into a Knowledge Graph.
+
+== Documentation
+
+* https://js.langchain.com/docs/modules/data_connection/experimental/graph_databases/neo4j[Neo4jGraph^]
+* https://js.langchain.com/docs/modules/chains/additional/cypher_chain[GraphCypherQAChain^]
+* https://js.langchain.com/docs/integrations/vectorstores/neo4jvector[Neo4j Vector^]
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | https://github.com/easwee[Anej Gorkič^], https://github.com/tomasonjo[Tomaz Bratanic^] and https://github.com/oskarhane[Oskar Hane^]
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Data Repository | https://github.com/langchain-ai/langchainjs[GitHub]
+| icon:github[] Issues | https://github.com/langchain-ai/langchainjs/issues
+| icon:book[] Documentation | https://js.langchain.com/docs/use_cases/graph/quickstart
+| icon:book[] Documentation | https://js.langchain.com/docs/modules/data_connection/experimental/graph_databases/neo4j
+|===
+
+== Videos & Tutorials
+
+++++
+
+++++
+
+////
+== Highlighted Articles
+
+link:https://medium.com/neo4j/....[article^]
+
+////
diff --git a/modules/genai-ecosystem/pages/langchain.adoc b/modules/genai-ecosystem/pages/langchain.adoc
new file mode 100644
index 00000000..98b23b7f
--- /dev/null
+++ b/modules/genai-ecosystem/pages/langchain.adoc
@@ -0,0 +1,364 @@
+= LangChain Neo4j Integration
+include::_graphacademy_llm.adoc[]
+:slug: langchain
+:author: Tomaz Bratanic
+:category: labs
+:tags: langchain, rag, vector search, , neo4j
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: langchain
+
+image::https://dist.neo4j.com/wp-content/uploads/20230615211357/1AH05dvGA_7db_EMySc9AAw.png[width=800]
+
+LangChain is a vast library for GenAI orchestration, it supports numerous LLMs, vector stores, document loaders and agents.
+It manages templates, composes components into chains and supports monitoring and observability.
+
+The broad and deep Neo4j integration allows for vector search, cypher generation and database querying and knowledge graph construction.
+
+Here is an overview of the https://python.langchain.com/docs/use_cases/graph/quickstart[Graph Integrations^].
+
+* https://towardsdatascience.com/integrating-neo4j-into-the-langchain-ecosystem-df0e988344d2[Integrating Neo4j into the LangChain Ecosystem^]
+
+NOTE: When upgrading to LangChain 0.1.0+ make sure to read this article: https://medium.com/neo4j/langchain-v0-1-updating-graphacademy-neo4j-llm-courses-689e7c4eff3e[Updating GraphAcademy Neo4j & LLM Courses to Langchain v0.1^].
+
+image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/llm-chatbot-python.png[width=800,link="https://graphacademy.neo4j.com/courses/llm-chatbot-python/"]
+
+== Installation
+
+[source,shell]
+----
+pip install langchain langchain-community
+# pip install langchain-openai tiktoken
+# pip install neo4j
+----
+== Functionality Includes
+
+=== Neo4jVector
+
+The Neo4j Vector integration supports a number of operations
+
+* create vector from langchain documents
+* query vector
+* query vector with additional graph retrieval Cypher query
+* construct vector instance from existing graph data
+* hybrid search
+* metadata filtering
+
+// todo include
+[source,python]
+----
+from langchain.docstore.document import Document
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.document_loaders import TextLoader
+from langchain_community.vectorstores import Neo4jVector
+from langchain_openai import OpenAIEmbeddings
+
+loader = TextLoader("../../modules/state_of_the_union.txt")
+
+documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+docs = text_splitter.split_documents(documents)
+
+embeddings = OpenAIEmbeddings()
+
+# The Neo4jVector Module will connect to Neo4j and create a vector index if needed.
+
+db = Neo4jVector.from_documents(
+    docs, OpenAIEmbeddings(), url=url, username=username, password=password
+)
+
+query = "What did the president say about Ketanji Brown Jackson"
+docs_with_score = db.similarity_search_with_score(query, k=2)
+----
+
+* link:https://neo4j.com/developer-blog/langchain-library-full-support-neo4j-vector-index/[LangChain Library Adds Full Support for Neo4j Vector Index^][]
+* https://neo4j.com/developer-blog/neo4j-langchain-vector-index-implementation/[Neo4j LangChain Vector Index Implementation^]
+* https://blog.langchain.dev/neo4j-x-langchain-new-vector-index/[Neo4j x LangChain: New Vector Index^]
+
+==== Hybrid Search
+
+Hybrid search combines vector search with fulltext search with re-ranking and de-duplication of the results.
+
+// todo include
+[source,python]
+----
+from langchain.docstore.document import Document
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.document_loaders import TextLoader
+from langchain_community.vectorstores import Neo4jVector
+from langchain_openai import OpenAIEmbeddings
+
+loader = TextLoader("../../modules/state_of_the_union.txt")
+
+documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+docs = text_splitter.split_documents(documents)
+
+embeddings = OpenAIEmbeddings()
+
+# The Neo4jVector Module will connect to Neo4j and create a vector index if needed.
+
+db = Neo4jVector.from_documents(
+    docs, OpenAIEmbeddings(), url=url, username=username, password=password,
+    search_type: 'hybrid'
+)
+
+query = "What did the president say about Ketanji Brown Jackson"
+docs_with_score = db.similarity_search_with_score(query, k=2)
+----
+
+==== Metadata filtering
+
+Metadata filtering enhances vector search by allowing searches to be refined based on specific node properties.
+This integrated approach ensures more precise and relevant search results by leveraging both the vector similarities and the contextual attributes of the nodes.
+
+[source,python]
+----
+db = Neo4jVector.from_documents(
+    docs, 
+    OpenAIEmbeddings(), 
+    url=url, username=username, password=password
+)
+
+query = "What did the president say about Ketanji Brown Jackson"
+filter = {"name": {"$eq": "adam"}}
+
+docs = db.similarity_search(query, filter=filter)
+----
+
+=== Neo4j Graph
+
+The Neo4j Graph integration is a wrapper for the Neo4j Python driver. 
+It allows querying and updating the Neo4j database in a simplified manner from LangChain.
+Many integrations allow you to use the Neo4j Graph as a source of data for LangChain.
+
+
+[source,python]
+----
+from langchain_community.graphs import Neo4jGraph
+
+graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USERNAME, password=NEO4J_PASSWORD)
+
+QUERY = """
+"MATCH (m:Movie)-[:IN_GENRE]->(:Genre {name:$genre}) 
+RETURN m.title, m.plot 
+ORDER BY m.imdbRating DESC LIMIT 5"
+"""
+
+graph.query(QUERY, genre="action")
+----
+
+=== CypherQAChain
+
+The CypherQAChain is a LangChain component that allows you to interact with a Neo4j graph database in natural language.
+Using an LLM and the graph schema it translates the user question into a Cypher query, executes it against the graph and uses the returned context information and the original question with a second LLM to generate a natural language response.
+
+// todo include
+[source,python]
+----
+# pip install --upgrade --quiet  langchain
+# pip install --upgrade --quiet  langchain-openai
+
+from langchain.chains import GraphCypherQAChain
+from langchain_community.graphs import Neo4jGraph
+from langchain_openai import ChatOpenAI
+
+graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USERNAME, password=NEO4J_PASSWORD)
+
+# Insert some movie data
+graph.query(
+    """
+MERGE (m:Movie {title:"Top Gun"})
+WITH m
+UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor
+MERGE (a:Actor {name:actor})
+MERGE (a)-[:ACTED_IN]->(m)
+"""
+)
+
+chain = GraphCypherQAChain.from_llm(
+    ChatOpenAI(temperature=0), graph=graph, verbose=True
+)
+
+chain.run("Who played in Top Gun?")
+----
+
+=== Advanced RAG Strategies
+
+Besides the basic RAG strategy, the Neo4j Integration in LangChain supports advanced RAG strategies that allow for more complex retrieval strategies.
+These are also available as LangChain Templates.
+
+* *regular rag* - direct vector search 
+* *parent - child retriever* that links embedded chunks representing specific concepts to parent documents
+* *hypothetical questions* - generate questions from the document chunks and vector index those to have better matching candidates for user questions
+* *summary* - index summaries of the documents not the whole document
+
+* https://blog.langchain.dev/implementing-advanced-retrieval-rag-strategies-with-neo4j/[Implementing Advanced Retrieval RAG Strategies with Neo4j^]
+
+* https://python.langchain.com/docs/templates/neo4j-advanced-rag
+
+[source,shell]
+----
+pip install -U "langchain-cli[serve]"
+
+langchain app new my-app --package neo4j-advanced-rag
+
+# update server.py to add the neo4j-advanced-rag template as an endpoint
+cat < server.py
+from fastapi import FastAPI
+from langserve import add_routes
+
+from neo4j_advanced_rag import chain as neo4j_advanced_chain
+
+app = FastAPI()
+
+# Add this
+add_routes(app, neo4j_advanced_chain, path="/neo4j-advanced-rag")
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+EOF
+
+langchain serve
+----
+
+image::https://lh7-us.googleusercontent.com/jfDNiPa5ccefX6h0HiVzJbqnlgAZgfPda90truHSfbwSs3JkfxZ-xbA9mZE8y2fNf_3n5cgVhbdhN0ryuMoK2JNbMgTe1OLJMA6CQRhWBxzdKRLVurUFDndT7ki4vMh-cdv3SAn040HTpab9XkzGj5Q[]
+
+=== LangChain Templates
+
+https://blog.langchain.dev/langchain-templates/[Langchain Templates^] are a set of preconfigured chains and components that can be used to build GenAI workflows and applications.
+You can test them interactively on the LangChain Playground and run them with https://github.com/langchain-ai/langserve[LangServe^] to run as REST APIs, they also integrate with [LangSmith] for monitoring and observability.
+
+By creating an application from templates, their source code is added to your application and you can modify them to fit your needs.
+
+==== List of Templates
+
+This https://python.langchain.com/docs/templates/neo4j-cypher[Cypher template] allows you to interact with a Neo4j graph database in natural language, using an OpenAI LLM.
+
+It transforms a natural language question into a Cypher query (used to fetch data from Neo4j databases), executes the query, and provides a natural language response based on the query results.
+
+The https://python.langchain.com/docs/templates/neo4j-cypher-ft[Cypher-FT Template^] additionally utilizes a full-text index for efficient mapping of text values to database entries, thereby enhancing the generation of accurate Cypher statements.
+
+The https://python.langchain.com/docs/templates/neo4j-cypher-memory[Cypher Memory Template^] also features a conversational memory module that stores the dialogue history in the Neo4j graph database. The conversation memory is uniquely maintained for each user session, ensuring personalized interactions. 
+
+The https://python.langchain.com/docs/templates/neo4j-generation[Neo4j generation Template^] pairs LLM-based knowledge graph extraction using OpenAI functions, with Neo4j AuraDB, a fully managed cloud graph database.
+
+This https://python.langchain.com/docs/templates/neo4j-vector-memory[Neo4j Vector Memory Template^] allows you to integrate an LLM with a vector-based retrieval system using Neo4j as the vector store. Additionally, it uses the graph capabilities of the Neo4j database to store and retrieve the dialogue history of a specific user's session. Having the dialogue history stored as a graph allows for seamless conversational flows but also gives you the ability to analyze user behavior and text chunk retrieval through graph analytics.
+
+The https://python.langchain.com/docs/templates/neo4j-parent[Parent-Child Retriever Template^] allows you to balance precise embeddings and context retention by splitting documents into smaller chunks and retrieving their original or larger text information.
+
+Using a Neo4j vector index, the package queries child nodes using vector similarity search and retrieves the corresponding parent's text.
+
+The https://python.langchain.com/docs/templates/neo4j-semantic-layer[Neo4j Semantic Layer Template^] is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using OpenAI function calling. The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph databas based on the user's intent.
+
+=== Semantic Layer
+
+A semantic layer on top of a (graph) database doesn't rely on automatic query generation but offers a number of APIs and tools to give the LLM access to the database and it's structures.
+
+Unlike automatically generated queries, these tools are safe to use as they are implemented using correct queries and interactions and only take parameters from the LLM.
+
+Many cloud (llm) providers offer similar integrations either via function calling (OpenAI, Anthropic) or extensions (Google Vertex AI, AWS Bedrock).
+
+Examples for such tools or functions include:
+
+* retrieve entities with certain names
+* retrieve the neighbors of a node
+* retrieve a shortest path between two nodes
+
+* https://towardsdatascience.com/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49[ Enhancing Interaction Between Language Models and Graph Databases via a Semantic Layer^]
+
+
+=== Conversational Memory
+
+Storing the conversation, i.e. the flow of questions and answers of user sessions in a graph allows you to analyze the conversation history and use it to improve the user experience.
+
+You can index embeddings for and link questions and answers back to the retrieved chunks and entities in the graph and use user feedback to re-rank those inputs for future similar questions.  
+
+=== Knowledge Graph Construction
+
+Creating a Knowledge Graph from unstructured data like PDF documents used to be a complex and time-consuming task that required training and using dedicated, large NLP models.
+
+The https://python.langchain.com/docs/use_cases/graph/constructing[Graph Transformers^] are tools that allows you to extract structured data from unstructured documents and transform it into a Knowledge Graph.
+
+NOTE: You can see a practical application, code and demo for extracting knowledge graphs from PDFs, YouTube transcripts, wikpedia articles and more with the xref:llm-graph-builder.adoc[LLM Graph Builder].
+
+image::
+
+* https://python.langchain.com/docs/use_cases/graph/integrations/diffbot_graphtransformer[Diffbot Graph Transformer^]
+* https://python.langchain.com/docs/use_cases/graph/constructing#llm-graph-transformer[LLM Graph Transformer^]
+
+* https://neo4j.com/developer-blog/knowledge-graph-based-chatbot-with-gpt-3-and-neo4j/[Knowledge Graph-based Chatbot with GPT-3 and Neo4j^]
+* https://blog.langchain.dev/constructing-knowledge-graphs-from-text-using-openai-functions/[Constructing Knowledge Graphs from Text using OpenAI Functions^]
+* https://neo4j.com/developer-blog/enhance-rag-knowledge-graph/[Enhancing the Accuracy of RAG Applications With Knowledge Graphs^]
+* https://www.youtube.com/watch?v=ViHV16ly-qs[Video: Going Meta Ep 25 - LLMs for Automated KG Construction^]
+
+== Documentation
+
+* https://python.langchain.com/docs/integrations/providers/neo4j/[Neo4j Integrations^]
+* https://python.langchain.com/docs/use_cases/graph/graph_cypher_qa[Graph Cypher QA Chain^]
+* https://python.langchain.com/docs/integrations/vectorstores/neo4jvector[Neo4j Vector^]
+* https://python.langchain.com/docs/use_cases/graph/constructing[Graph Transformers^]
+
+== Starter Kit
+
+This https://github.com/neo4j-examples/langchain-starter-kit/blob/main/app/vector_chain.py[starter-kit] demonstrates how to run a FastAPI server using LangChain to answer queries on data stored in a Neo4j instance. The single endpoint can be used to retrieve answers using either a https://python.langchain.com/v0.1/docs/integrations/vectorstores/neo4jvector/[Vector index chain],  https://python.langchain.com/v0.1/docs/integrations/graphs/neo4j_cypher/[GraphCypherQA Chain], or a composite answer of both. The https://github.com/neo4j-examples/langchain-starter-kit/tree/langserve[langserve] branch contains an example of the same service, using https://python.langchain.com/v0.1/docs/langserve/[LangServe]
+
+See this https://neo4j.com/developer-blog/langchain-neo4j-starter-kit/[Developer Blog Article] for additional details and instructions on working with the Starter Kit.
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | https://github.com/tomasonjo[Tomaz Bratanic^]
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Data Repository | https://github.com/langchain-ai/langchain[GitHub]
+| icon:github[] Issues | https://github.com/langchain-ai/langchain/issues
+| icon:book[] Documentation | https://python.langchain.com/docs/integrations/providers/neo4j/[Neo4j Integrations^]
+| icon:book[] Documentation | https://python.langchain.com/docs/use_cases/graph/quickstart[Graph Overview Docs^]
+| icon:github[] Starter Kit |https://github.com/neo4j-examples/langchain-starter-kit[LangChain Starter Kit^]
+| icon:code[] Juypter | https://github.com/tomasonjo/blogs/tree/master/llm[Jupyter Notebooks^]
+|===
+
+== Videos & Tutorials
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+++++
+
+* https://docs.google.com/presentation/d/1J4h4oGXORtiz1nhHfSF-UtvtlqkfHByD0v2MOwKXBwY/edit#slide=id.g265aec74fa4_0_353[Slides from the video^]
+
+
+== Highlighted Articles
+
+* https://graphstuff.fm/episodes/genai-solutions-with-langchain-lance-martin-on-llms-agents-evals-and-more[Podcast: GenAI solutions with LangChain: Lance Martin on LLMs, agents, evals, and more!^]
+* https://blog.langchain.dev/using-a-knowledge-graph-to-implement-a-devops-rag-application/[Using a Knowledge Graph to Implement a DevOps RAG Application^]
+* https://neo4j.com/developer-blog/unstructured-knowledge-graph-neo4j-langchain/[Unstructured Knowledge Graph with Neo4j and LangChain^]
+* https://neo4j.com/developer-blog/langchain-cypher-search-tips-tricks/[LangChain Cypher Search Tips & Tricks^]
+* https://medium.com/neo4j/langchain-neo4j-starter-kit-02054212d8ef[LangChain Neo4j Starter Kit]
+* https://medium.com/neo4j/langchain-v0-1-updating-graphacademy-neo4j-llm-courses-689e7c4eff3e[Updating GraphAcademy Neo4j & LLM Courses to Langchain v0.1^]
+* https://neo4j.com/developer-blog/why-vector-search-didnt-work-rag/[Why Vector Search Didn’t Work for Your RAG Solution?^]
+* https://blog.langchain.dev/constructing-knowledge-graphs-from-text-using-openai-functions/[Constructing Knowledge Graphs from Text using OpenAI Functions^]
+* https://blog.langchain.dev/using-a-knowledge-graph-to-implement-a-devops-rag-application/[Using a Knowledge Graph to Implement a DevOps RAG Application^]
+* https://medium.com/neo4j/json-based-agents-with-ollama-langchain-9cf9ab3c84ef[JSON-based Agents with Ollama & LangChain^]
diff --git a/modules/genai-ecosystem/pages/langchain4j.adoc b/modules/genai-ecosystem/pages/langchain4j.adoc
new file mode 100644
index 00000000..5aefaaab
--- /dev/null
+++ b/modules/genai-ecosystem/pages/langchain4j.adoc
@@ -0,0 +1,65 @@
+= Langchain4j
+include::_graphacademy_llm.adoc[]
+:slug: langchain4j
+:author: Giuseppe Villani
+:category: genai-ecosystem
+:tags: langchain, langchain4j, java, neo4j, vector, index
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: langchain4j
+
+image::https://avatars.githubusercontent.com/u/132277850?v=4[width=400]
+
+https://github.com/langchain4j[Langchain4j^] is a Java implementation of the langchain library.
+It uses similar concepts, with Prompts, Chains, Transformers, Document Loaders, Agents, and more.
+
+The Neo4j Integration makes the xref:vector-search.adoc[Neo4j Vector] index available in the Langchain4j library.
+
+// https://github.com/langchain4j/langchain4j/tree/main/langchain4j-neo4j
+
+== Installation
+
+.pom.xml
+[source,xml]
+----
+include::https://github.com/langchain4j/langchain4j-examples/raw/main/neo4j-example/pom.xml[lines=19..23]
+----
+
+== Functionality Includes
+
+* Create vector index
+* Populate nodes and vector index from documents
+* Query vector index
+
+== Documentation
+
+An example is avalable at: https://github.com/langchain4j/langchain4j-examples/tree/main/neo4j-example
+
+[source,java]
+----
+include::https://github.com/langchain4j/langchain4j-examples/raw/main/neo4j-example/src/main/java/Neo4jEmbeddingStoreExample.java[]
+----
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | https://github.com/vga91[Giuseppe Villani from Neo4j Partner Larus^]
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Data Repository | https://github.com/langchain4j/langchain4j/[GitHub]
+| icon:github[] Issues | https://github.com/langchain4j/langchain4j/issues
+// | icon:book[] Documentation |
+|===
+
+////
+
+== Videos & Tutorials
+
+Installation:
+++++
+
+++++
+
+== Highlighted Articles
+
+// link:https://medium.com/neo4j/....[article^]
+////
diff --git a/modules/genai-ecosystem/pages/llamaindex.adoc b/modules/genai-ecosystem/pages/llamaindex.adoc
new file mode 100644
index 00000000..8c97c190
--- /dev/null
+++ b/modules/genai-ecosystem/pages/llamaindex.adoc
@@ -0,0 +1,381 @@
+= LlamaIndex
+:slug: llamaindex
+:author: Michael Hunger, Tomaz Bratanic
+:category: labs
+:tags: llama index, llm, framework, python, vector, cypher generation
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: llamaindex
+
+// image::todo.png[width=800]
+
+LlamaIndex is a popular LLM orchestration framework with a clean architecture and a focus on data structures and models.
+It integrates many LLMs as well as vector stores and other indexes and contains tooling for document loading (loader hub) and advanced RAG patterns.
+
+LlamaIndex provides a lot of detailed examples for GenAI application development in their https://blog.llamaindex.ai/[blogs^] and https://docs.llamaindex.ai[documentation^].
+
+The Neo4j integration covers both the vector store as well as query generation from natural language and knowledge graph construction.
+
+== Functionality Includes
+
+=== Neo4jVector
+
+The Neo4j Vector integration supports a number of operations
+
+* create vector from langchain documents
+* query vector
+* query vector with additional graph retrieval Cypher query
+* construct vector instance from existing graph data
+* hybrid search
+* metadata filtering
+
+[source,python]
+----
+%pip install llama-index-llms-openai
+%pip install llama-index-vector-stores-neo4jvector
+%pip install llama-index-embeddings-openai
+%pip install neo4j
+
+import os
+import openai
+from llama_index.vector_stores.neo4jvector import Neo4jVectorStore
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+
+os.environ["OPENAI_API_KEY"] = "OPENAI_API_KEY"
+openai.api_key = os.environ["OPENAI_API_KEY"]
+username = "neo4j"
+password = "pleaseletmein"
+url = "bolt://localhost:7687"
+embed_dim = 1536
+
+neo4j_vector = Neo4jVectorStore(username, password, url, embed_dim)
+# load documents
+documents = SimpleDirectoryReader("./data/paul_graham").load_data()
+from llama_index.core import StorageContext
+
+storage_context = StorageContext.from_defaults(vector_store=neo4j_vector)
+index = VectorStoreIndex.from_documents(
+    documents, storage_context=storage_context
+)
+
+query_engine = index.as_query_engine()
+response = query_engine.query("What happened at interleaf?")
+----
+
+==== Hybrid search
+
+Hybrid search combines vector search with fulltext search with re-ranking and de-duplication of the results.
+
+[source,python]
+----
+neo4j_vector_hybrid = Neo4jVectorStore(
+    username, password, url, embed_dim, hybrid_search=True
+)
+
+storage_context = StorageContext.from_defaults(
+    vector_store=neo4j_vector_hybrid
+)
+index = VectorStoreIndex.from_documents(
+    documents, storage_context=storage_context
+)
+query_engine = index.as_query_engine()
+response = query_engine.query("What happened at interleaf?")
+----
+
+==== Metadata filtering
+
+Metadata filtering enhances vector search by allowing searches to be refined based on specific node properties.
+This integrated approach ensures more precise and relevant search results by leveraging both the vector similarities and the contextual attributes of the nodes.
+
+[source,python]
+----
+from llama_index.core.vector_stores import (
+    MetadataFilter,
+    MetadataFilters,
+    FilterOperator,
+)
+
+filters = MetadataFilters(
+    filters=[
+        MetadataFilter(
+            key="theme", operator=FilterOperator.EQ, value="Fiction"
+        ),
+    ]
+)
+
+retriever = index.as_retriever(filters=filters)
+retriever.retrieve("What is inception about?")
+----
+
+=== Neo4jPropertyGraphStore
+
+The Neo4j Property Graph Store integration is a wrapper for the Neo4j Python driver. 
+It allows querying and updating the Neo4j database in a simplified manner from LlamaIndex.
+Many integrations allow you to use the Neo4j Property Graph Store as a source of data for LlamaIndex.
+
+==== Property graph index
+
+Knowledge graph index can be used to extract graph representation of information from text and use it to construct a knowledge graph.
+The graph information can then be retrieved in a RAG application for more accurate responses.
+
+[source,python]
+----
+%pip install llama-index llama-index-graph-stores-neo4j
+
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core import PropertyGraphIndex
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.llms.openai import OpenAI
+from llama_index.core.indices.property_graph import SchemaLLMPathExtractor
+
+from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore
+
+documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
+graph_store = Neo4jPropertyGraphStore(
+    username="neo4j",
+    password="password",
+    url="bolt://localhost:7687",
+)
+# Extract graph from documents
+index = PropertyGraphIndex.from_documents(
+    documents,
+    embed_model=OpenAIEmbedding(model_name="text-embedding-3-small"),
+    kg_extractors=[
+        SchemaLLMPathExtractor(
+            llm=OpenAI(model="gpt-3.5-turbo", temperature=0.0)
+        )
+    ],
+    property_graph_store=graph_store,
+    show_progress=True,
+)
+
+# Define retriever
+retriever = index.as_retriever(
+    include_text=False,  # include source text in returned nodes, default True
+)
+results = retriever.retrieve("What happened at Interleaf and Viaweb?")
+for record in results:
+    print(record.text)
+
+# Question answering
+query_engine = index.as_query_engine(include_text=True)
+response = query_engine.query("What happened at Interleaf and Viaweb?")
+print(str(response))
+----
+
+==== Property Graph constructing modules
+
+LlamaIndex features multiple graph construction modules.
+Property graph construction in LlamaIndex works by performing a series of `kg_extractors` on each text chunk, and attaching entities and relations as metadata to each llama-index node.
+You can use as many as you like here, and they will all get applied.
+Learn more about them in the https://docs.llamaindex.ai/en/latest/module_guides/indexing/lpg_index_guide/#construction[documentation^].
+
+Here is an example of graph construction using a predefined schema.
+
+[source,python]
+----
+%pip install llama-index llama-index-graph-stores-neo4j
+
+from typing import Literal
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core import PropertyGraphIndex
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.llms.openai import OpenAI
+from llama_index.core.indices.property_graph import SchemaLLMPathExtractor
+
+from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore
+
+# best practice to use upper-case
+entities = Literal["PERSON", "PLACE", "ORGANIZATION"]
+relations = Literal["HAS", "PART_OF", "WORKED_ON", "WORKED_WITH", "WORKED_AT"]
+
+# define which entities can have which relations
+validation_schema = {
+    "PERSON": ["HAS", "PART_OF", "WORKED_ON", "WORKED_WITH", "WORKED_AT"],
+    "PLACE": ["HAS", "PART_OF", "WORKED_AT"],
+    "ORGANIZATION": ["HAS", "PART_OF", "WORKED_WITH"],
+}
+
+kg_extractor = SchemaLLMPathExtractor(
+    llm=OpenAI(model="gpt-3.5-turbo", temperature=0.0),
+    possible_entities=entities,
+    possible_relations=relations,
+    kg_validation_schema=validation_schema,
+    # if false, allows for values outside of the schema
+    # useful for using the schema as a suggestion
+    strict=True,
+)
+graph_store = Neo4jPropertyGraphStore(
+    username="neo4j",
+    password="password",
+    url="bolt://localhost:7687",
+)
+documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
+index = PropertyGraphIndex.from_documents(
+    documents,
+    kg_extractors=[kg_extractor],
+    embed_model=OpenAIEmbedding(model_name="text-embedding-3-small"),
+    property_graph_store=graph_store,
+    show_progress=True,
+)
+----
+
+==== Property graph querying modules
+
+Labeled property graphs can be queried in several ways to retrieve nodes and paths.
+And in LlamaIndex, you can combine several node retrieval methods at once!
+Learn more about which ones are available in the https://docs.llamaindex.ai/en/latest/module_guides/indexing/lpg_index_guide/#retrieval-and-querying[documentation^].
+
+You can also define a custom graph retriever as shown below.
+
+[source,python]
+----
+from llama_index.core.retrievers import (
+    CustomPGRetriever,
+    VectorContextRetriever,
+    TextToCypherRetriever,
+)
+from llama_index.core.query_engine import RetrieverQueryEngine
+from llama_index.core.graph_stores import PropertyGraphStore
+from llama_index.core.vector_stores.types import VectorStore
+from llama_index.core.embeddings import BaseEmbedding
+from llama_index.core.prompts import PromptTemplate
+from llama_index.core.llms import LLM
+from llama_index.postprocessor.cohere_rerank import CohereRerank
+
+
+from typing import Optional, Any, Union
+
+
+class MyCustomRetriever(CustomPGRetriever):
+    """Custom retriever with cohere reranking."""
+
+    def init(
+        self,
+        ## vector context retriever params
+        embed_model: Optional[BaseEmbedding] = None,
+        vector_store: Optional[VectorStore] = None,
+        similarity_top_k: int = 4,
+        path_depth: int = 1,
+        ## text-to-cypher params
+        llm: Optional[LLM] = None,
+        text_to_cypher_template: Optional[Union[PromptTemplate, str]] = None,
+        ## cohere reranker params
+        cohere_api_key: Optional[str] = None,
+        cohere_top_n: int = 2,
+        **kwargs: Any,
+    ) -> None:
+        """Uses any kwargs passed in from class constructor."""
+
+        self.vector_retriever = VectorContextRetriever(
+            self.graph_store,
+            include_text=self.include_text,
+            embed_model=embed_model,
+            vector_store=vector_store,
+            similarity_top_k=similarity_top_k,
+            path_depth=path_depth,
+        )
+
+        self.cypher_retriever = TextToCypherRetriever(
+            self.graph_store,
+            llm=llm,
+            text_to_cypher_template=text_to_cypher_template
+            ## NOTE: you can attach other parameters here if you'd like
+        )
+
+        self.reranker = CohereRerank(
+            api_key=cohere_api_key, top_n=cohere_top_n
+        )
+
+    def custom_retrieve(self, query_str: str) -> str:
+        """Define custom retriever with reranking.
+
+        Could return `str`, `TextNode`, `NodeWithScore`, or a list of those.
+        """
+        nodes_1 = self.vector_retriever.retrieve(query_str)
+        nodes_2 = self.cypher_retriever.retrieve(query_str)
+        reranked_nodes = self.reranker.postprocess_nodes(
+            nodes_1 + nodes_2, query_str=query_str
+        )
+
+        ## TMP: please change
+        final_text = "\n\n".join(
+            [n.get_content(metadata_mode="llm") for n in reranked_nodes]
+        )
+
+        return final_text
+
+custom_sub_retriever = MyCustomRetriever(
+    index.property_graph_store,
+    include_text=True,
+    vector_store=index.vector_store,
+    cohere_api_key="...",
+)
+
+query_engine = RetrieverQueryEngine.from_args(
+    index.as_retriever(sub_retrievers=[custom_sub_retriever]), llm=llm
+)
+
+response = query_engine.query("Did the author like programming?")
+print(str(response))
+----
+
+== Documentation
+
+* https://docs.llamaindex.ai/en/latest/module_guides/indexing/lpg_index_guide/[Property Graph Index^]
+* https://docs.llamaindex.ai/en/stable/examples/property_graph/property_graph_custom_retriever/[Custom retriever example^]
+* https://github.com/run-llama/llama_parse/blob/main/examples/knowledge_graphs/kg_agent.ipynb[Knowledge graph agent with LlamaParse^]
+* https://docs.llamaindex.ai/en/stable/examples/vector_stores/Neo4jVectorDemo.html[Neo4jVectorDemo^]
+
+* https://llamahub.ai/l/readers/llama-index-readers-graphdb-cypher[Cypher Loader^]
+* https://llamahub.ai/l/graph_stores/llama-index-graph-stores-neo4j[Neo4j Graph Store^]
+* https://llamahub.ai/l/tools/llama-index-tools-neo4j[Neo4j Schema Query Builder^]
+
+=== Neo4j Query Engine Pack
+
+This https://llamahub.ai/l/tools/llama-index-tools-neo4j[Neo4j Query Engine LlamaPack^] creates a Neo4j query engine, and executes its query function. This pack offers the option of creating multiple types of query engines, namely:
+
+* Knowledge graph vector-based entity retrieval (default if no query engine type option is provided)
+* Knowledge graph keyword-based entity retrieval
+* Knowledge graph hybrid entity retrieval
+* Raw vector index retrieval
+* Custom combo query engine (vector similarity + KG entity retrieval)
+* KnowledgeGraphQueryEngine
+* KnowledgeGraphRAGRetriever
+
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | https://github.com/tomasonjo[Tomaz Bratanic^], https://github.com/jexp[Michael Hunger^]
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Repository | https://github.com/run-llama/llama-hub/tree/main/llama_hub/tools/neo4j_db[GitHub Neo4jDB^] https://github.com/run-llama/llama-hub/tree/main/llama_hub/llama_packs/neo4j_query_engine[GitHub Neo4j Llama Pack^]
+| icon:book[] Documentation | https://docs.llamaindex.ai/en/stable/examples/index_structs/knowledge_graph/Neo4jKGIndexDemo.html
+| icon:github[] Starter Kit | https://neo4j.com/labs/genai-ecosystem/llamaindex/[LlamaIndex Starter Kit^]
+| icon:book[] Notebook | https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/neo4j_query_engine/llama_packs_neo4j.ipynb[Llama Pack Notebook^]
+|===
+
+== Videos & Tutorials
+
+
+* https://graphstuff.fm/episodes/llamaindex-and-more-building-llm-tech-with-jerry-liu[GraphStuff.fm Podcast: LlamaIndex and More: Building LLM Tech with Jerry Liu^]
+
+++++
+
+++++
+
+++++
+
+++++
+
+== Highlighted Articles
+
+* https://www.llamaindex.ai/blog/introducing-the-property-graph-index-a-powerful-new-way-to-build-knowledge-graphs-with-llms[Property graph index introduction^]
+
+* https://blog.llamaindex.ai/multimodal-rag-pipeline-with-llamaindex-and-neo4j-a2c542eb0206[Multimodal RAG Pipeline with LlamaIndex and Neo4j^]
+
+* https://blog.llamaindex.ai/enriching-llamaindex-models-from-graphql-and-graph-databases-bcaecec262d7[Enriching LlamaIndex Models from GraphQL and Graph Databases^]
+
+* https://levelup.gitconnected.com/a-simpler-way-to-query-neo4j-knowledge-graphs-99c0a8bbf1d7[A Simpler Way to Query Neo4j Knowledge Graphs^]
+
+* https://medium.com/@yu-joshua/using-llamaparse-for-knowledge-graph-creation-from-documents-3bd1e1849754[Using LlamaParse for Knowledge Graph Creation from Documents^]
diff --git a/modules/genai-ecosystem/pages/llm-graph-builder-deployment.adoc b/modules/genai-ecosystem/pages/llm-graph-builder-deployment.adoc
new file mode 100644
index 00000000..7553f078
--- /dev/null
+++ b/modules/genai-ecosystem/pages/llm-graph-builder-deployment.adoc
@@ -0,0 +1,216 @@
+= Documentation for local deployments
+include::_graphacademy_llm.adoc[]
+:slug: llm-graph-builder
+:author: Michael Hunger, Tomaz Bratanic, Persistent
+:category: genai-ecosystem
+:tags: 
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: llm-graph-builder
+:imagesdir: https://dev.assets.neo4j.com/wp-content/uploads/2024/
+
+== Prerequisites
+
+You will need to have a Neo4j Database V5.18 or later with https://neo4j.com/docs/apoc/current/installation/[APOC installed^] to use this Knowledge Graph Builder.
+You can use any https://neo4j.com/aura/[Neo4j Aura database^] (including the free tier database). Neo4j Aura automatically includes APOC and run on the latest Neo4j version, making it a great choice to get started quickly. 
+
+You can also use the free trial in https://sandbox.neo4j.com[Neo4j Sandbox^], which also includes Graph Data Science.
+If want to use https://neo4j.com/product/developer-tools/#desktop[Neo4j Desktop^] instead, you need to configure your `NEO4J_URI=bolt://host.docker.internal` to allow the Docker container to access the network running on your computer.
+
+== Docker-compose
+
+By default only OpenAI and Diffbot are enabled since Gemini requires extra GCP configurations.
+
+In your root folder, create a .env file with your OPENAI and DIFFBOT keys (if you want to use both):
+
+[source,env]
+----
+OPENAI_API_KEY="your-openai-key"
+DIFFBOT_API_KEY="your-diffbot-key"
+----
+
+if you only want OpenAI:
+
+[source,env]
+----
+LLM_MODELS="gpt-3.5,gpt-4o"
+OPENAI_API_KEY="your-openai-key"
+----
+
+if you only want Diffbot:
+
+[source,env]
+----
+LLM_MODELS="diffbot"
+DIFFBOT_API_KEY="your-diffbot-key"
+----
+
+You can then run Docker Compose to build and start all components:
+
+[source,bash]
+----
+docker-compose up --build
+----
+
+=== Configuring LLM Models
+
+You can configure the following LLM models besides the ones supported out of the box:
+
+* OpenAI GPT 3.5 and 4o (default)
+* VertexAI (Gemini 1.0) (default)
+* VertexAI (Gemini 1.5)
+* Diffbot
+* Bedrock models 
+* Anthropic
+* OpenAI API compatible models like Ollama, Groq, Fireworks
+
+To achieve that you need to set a number of environment variables:
+
+In your `.env` file, add the following lines. You can of course also add other model configurations from these providers or any OpenAI API compatible provider.
+
+[source,env]
+----
+LLM_MODEL_CONFIG_azure_ai_gpt_35="gpt-35,https://.openai.azure.com/,,"
+LLM_MODEL_CONFIG_anthropic_claude_35_sonnet="claude-3-5-sonnet-20240620,"
+LLM_MODEL_CONFIG_fireworks_llama_v3_70b="accounts/fireworks/models/llama-v3-70b-instruct,"
+LLM_MODEL_CONFIG_bedrock_claude_35_sonnet="anthropic.claude-3-sonnet-20240229-v1:0,,"
+LLM_MODEL_CONFIG_ollama_llama3="llama3,http://host.docker.internal:11434"
+LLM_MODEL_CONFIG_fireworks_qwen_72b="accounts/fireworks/models/qwen2-72b-instruct,"
+
+# Optional Frontend config
+LLM_MODELS="diffbot,gpt-3.5,gpt-4o,azure_ai_gpt_35,azure_ai_gpt_4o,groq_llama3_70b,anthropic_claude_35_sonnet,fireworks_llama_v3_70b,bedrock_claude_35_sonnet,ollama_llama3,fireworks_qwen_72b"
+----
+
+In your `docker-compose.yml` you need to pass the variables through:
+
+[source,yaml]
+----
+- LLM_MODEL_CONFIG_anthropic_claude_35_sonnet=${LLM_MODEL_CONFIG_anthropic_claude_35_sonnet-}
+- LLM_MODEL_CONFIG_fireworks_llama_v3_70b=${LLM_MODEL_CONFIG_fireworks_llama_v3_70b-}
+- LLM_MODEL_CONFIG_azure_ai_gpt_4o=${LLM_MODEL_CONFIG_azure_ai_gpt_4o-}
+- LLM_MODEL_CONFIG_azure_ai_gpt_35=${LLM_MODEL_CONFIG_azure_ai_gpt_35-}
+- LLM_MODEL_CONFIG_groq_llama3_70b=${LLM_MODEL_CONFIG_groq_llama3_70b-}
+- LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet=${LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet-}
+- LLM_MODEL_CONFIG_fireworks_qwen_72b=${LLM_MODEL_CONFIG_fireworks_qwen_72b-}
+- LLM_MODEL_CONFIG_ollama_llama3=${LLM_MODEL_CONFIG_ollama_llama3-}
+----
+
+=== Additional configs
+
+By default, the input sources will be: Local files, Youtube, Wikipedia and AWS S3. 
+This is the default config applied if you do not overwrite it in your .env file:
+
+[source,env]
+----
+REACT_APP_SOURCES="local,youtube,wiki,s3"
+----
+
+If however you want the Google GCS integration, add `gcs` and your Google client ID:
+
+[source,env]
+----
+REACT_APP_SOURCES="local,youtube,wiki,s3,gcs"
+GOOGLE_CLIENT_ID="xxxx"
+----
+
+The `REACT_APP_SOURCES` should be a comma-separated list of the sources you want to enable.
+You can of course combine all (local, youtube, wikipedia, s3 and gcs) or remove any you don't want or need.
+
+[#dev-deployment]
+== Development (Separate Frontend and Backend)
+
+Alternatively, you can run the backend and frontend separately:
+
+- For the frontend:
+1. Create the frontend/.env file by copy/pasting the frontend/example.env.
+2. Change values as needed
+3. Run:
+
+[source,bash]
+----
+cd frontend
+yarn
+yarn run dev
+----
+
+- For the backend:
+1. Create the backend/.env file by copy/pasting the backend/example.env.
+2. Change values as needed
+3.  Run:
+
+[source,bash]
+----
+cd backend
+python -m venv envName
+source envName/bin/activate 
+pip install -r requirements.txt
+uvicorn score:app --reload
+----
+
+== ENV
+
+=== Processing Configuration
+
+[options="header",cols="m,a,m,a"]
+|===
+| Env Variable Name | Mandatory/Optional | Default Value | Description
+| IS_EMBEDDING | Optional | true | Flag to enable text embedding for chunks
+| ENTITY_EMBEDDING | Optional | false | Flag to enable entity embedding (id and description)
+| KNN_MIN_SCORE | Optional | 0.94 | Minimum score for KNN algorithm for connecting similar Chunks
+| NUMBER_OF_CHUNKS_TO_COMBINE | Optional | 6 | Number of chunks to combine when extracting entities
+| UPDATE_GRAPH_CHUNKS_PROCESSED | Optional | 20 | Number of chunks processed before writing to the database and updating progress
+| ENV | Optional | DEV | Environment variable for the app
+| TIME_PER_CHUNK | Optional | 4 | Time per chunk for processing
+| CHUNK_SIZE | Optional | 5242880 | Size of each chunk for processing
+|===
+
+=== Front-End Configuration
+
+[options="header",cols="m,a,m,a"]
+|===
+| Env Variable Name | Mandatory/Optional | Default Value | Description
+| BACKEND_API_URL | Optional | http://localhost:8000 | URL for backend API
+| REACT_APP_SOURCES | Optional | local,youtube,wiki,s3 | List of input sources that will be available
+| BLOOM_URL | Optional | https://workspace-preview.neo4j.io/workspace/explore?connectURL={CONNECT_URL}&search=Show+me+a+graph | URL for Bloom visualization
+|===
+
+
+=== GCP Cloud Integration
+
+[options="header",cols="m,a,m,a"]
+|===
+| Env Variable Name | Mandatory/Optional | Default Value | Description
+| GEMINI_ENABLED | Optional | False | Flag to enable Gemini
+| GCP_LOG_METRICS_ENABLED | Optional | False | Flag to enable Google Cloud logs
+| GOOGLE_CLIENT_ID | Optional | | Client ID for Google authentication for GCS upload
+| GCS_FILE_CACHE | Optional | False | If set to True, will save the files to process into GCS. If set to False, will save the files locally
+|===
+
+=== LLM Model Configuration
+
+[options="header",cols="m,a,m,a"]
+|===
+| Env Variable Name | Mandatory/Optional | Default Value | Description
+| LLM_MODELS | Optional | diffbot,gpt-3.5,gpt-4o | Models available for selection on the frontend, used for entities extraction and Q&A Chatbot (other models: `gemini-1.0-pro, gemini-1.5-pro`)
+| OPENAI_API_KEY | Optional | sk-... | API key for OpenAI (if enabled)
+| DIFFBOT_API_KEY | Optional | | API key for Diffbot (if enabled)
+| EMBEDDING_MODEL | Optional | all-MiniLM-L6-v2 | Model for generating the text embedding (all-MiniLM-L6-v2 , openai , vertexai)
+| GROQ_API_KEY | Optional | | API key for Groq
+| GEMINI_ENABLED | Optional | False | Flag to enable Gemini
+| LLM_MODEL_CONFIG_=",,," | Optional | | Configuration for additional LLM models
+|===
+
+
+=== LangChain and Neo4j Configuration
+
+[options="header",cols="m,a,m,a"]
+|===
+| Env Variable Name | Mandatory/Optional | Default Value | Description
+| NEO4J_URI | Optional | neo4j://database:7687 | URI for Neo4j database for the backend to connect to
+| NEO4J_USERNAME | Optional | neo4j | Username for Neo4j database for the backend to connect to
+| NEO4J_PASSWORD | Optional | password | Password for Neo4j database for the backend to connect to
+| LANGCHAIN_API_KEY | Optional | | API key for LangSmith
+| LANGCHAIN_PROJECT | Optional | | Project for LangSmith
+| LANGCHAIN_TRACING_V2 | Optional | false | Flag to enable LangSmith tracing
+| LANGCHAIN_ENDPOINT | Optional | https://api.smith.langchain.com | Endpoint for LangSmith API
+|===
diff --git a/modules/genai-ecosystem/pages/llm-graph-builder-features.adoc b/modules/genai-ecosystem/pages/llm-graph-builder-features.adoc
new file mode 100644
index 00000000..3ab41882
--- /dev/null
+++ b/modules/genai-ecosystem/pages/llm-graph-builder-features.adoc
@@ -0,0 +1,152 @@
+= Features documentation
+include::_graphacademy_llm.adoc[]
+:slug: llm-graph-builder
+:author: Michael Hunger, Tomaz Bratanic, Persistent
+:category: genai-ecosystem
+:tags: 
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: llm-graph-builder
+:imagesdir: https://dev.assets.neo4j.com/wp-content/uploads/2024/
+
+== Sources
+
+=== Local file upload
+
+You can drag & drop files into the first input zone on the left. The application will store the uploaded sources as Document nodes in the graph using LangChain Loaders (PDFLoader and Unstructured Loader).
+
+|===
+| File Type | Supported Extensions
+
+| Microsoft Office | .docx, .pptx, .xls
+| PDF | .pdf
+| Images | .jpeg, .jpg, .png, .svg
+| Text | .html, .txt, .md
+|===
+
+=== Web Links
+
+The second input zone handles web links.
+
+* YouTube transcripts
+* Wikipedia pages
+* Web Pages
+
+The application will parse and store the uploaded YouTube videos (transcript) as a Document nodes in the graph using YouTube parsers.
+
+For Wikipedia links we use the Wikipedia Loader. For example, you can provide `https://en.wikipedia.org/wiki/Neo4j` and it will load the Neo4j Wikipedia page.
+
+For web pages, we use the Unstructured Loader. For example, you can provide articles from `https://theguardian.com/` and it will load the article content.
+
+== Cloud Storage
+
+=== AWS S3
+
+This AWS S3 integration allows you to connect to an S3 bucket and load the files from there. You will need to provide your AWS credentials and the bucket name.
+
+=== Google Cloud Storage
+
+This Google Cloud Storage integration allows you to connect to a GCS bucket and load the files from there. You will have provide your GCS bucket name and optionally a folder an follow an auth flow to give the application access to the bucket.
+
+== LLM Models
+
+The application uses ML models to transform PDFs, web pages, and YouTube video transcripts into a knowledge graph of entities and their relationships. ENV variables can be set to configure/enable/disable specific models.
+
+The following models are configured (but only the first 3 are available in the publicly hosted version)
+
+* OpenAI GPT 3.5 and 4o
+* VertexAI (Gemini 1.0), 
+* Diffbot
+* Bedrock, 
+* Anthropic
+* OpenAI API compatible models like Ollama, Groq, Fireworks
+
+The selected LLM model will both be used for processing the newly uploaded files and for powering the chatbot. Please note that the models have different capabilities, so they will work not equally well especially for extraction.
+
+== Graph Enhancements
+
+=== Graph Schema
+
+image::llm-graph-builder-taxonomy.png[width=600, align=center]
+
+If you want to use a pre-defined or your own graph schema, you can do so in the Graph Enhancements popup. This is also shown the first time you construct a graph and the state of the model configuration is listed below the connection information.
+
+You can either:
+* select a pre-defined schema from the dropdown on top,
+* use your own by entering the node labels and relationships, 
+* fetch the existing schema from an existing Neo4j database (`Use Existing Schema`), 
+* or copy/paste a text or schema description (also works with RDF ontologies or Cypher/GQL schema) and ask the LLM to analyze it and come up with a suggested schema (`Get Schema From Text`).
+
+=== Delete Disconnected Nodes
+
+When extracting entities, it can happen that after the extraction a number of nodes are only connected to text chunks but not to other entities. 
+Which results to disconnected entities in the entity graph.
+
+While they can hold relevant information for question answering they might affect your downstream usage. So in this view you can select which of the entities that are only connected to text chunks should be deleted.
+
+////
+=== Merging Duplicate Entities
+
+While the prompt instructs the LLM to extract unique identifier for entities, across chunks and documents the same entity can end up with different spellings as duplicate in the graph.
+
+Here we use a mixture of entity embedding, edit distance and substring containment to generate a list of potentially duplicate entities that can be merged.
+
+You can select which sets of entities should be merged and exclude certain entities from the merge.
+////
+
+== Visualization
+
+You can visualize the _lexical_, the _entity_ graph or the full _knowledge_ graph of your extracted documents.
+
+=== Graph Visualization
+
+You have 2 options - either per document with the magifying glass icon at the end of the table or for all selected documents with the "Preview Graph" button.
+
+The graph visualization will show for the relevant files in a pop-up and you can filter for the type of graph you wanto to see:
+
+- Lexical Graph - the document and chunk nodes and their relationships
+- Entity Graph - the entity nodes and their relationships
+- Full Graph - all nodes and relationships
+
+=== Explore in Neo4j Bloom
+
+With the button "Explore in Neo4j Bloom" you can open the constructed knowledge graph in Neo4j Workspace for further visual exploration, querying and analysis.
+
+In Bloom/Explore you can run low code pattern based queries (or use the co-pilot) to fetch data from the graph and succesfully expand. If you are running against a GDS enabled instance, you can also run graph algorithms and visualize the results.
+You can also interactively edit and add to the graph.
+
+In Neo4j Data Importer you can additionally import structured data from CSV files and connect it to your extracted knowledge graph.
+
+In Neo4j Query you can write Cypher queries (or use the co-pilot) to pull tabular and graph data from your database.
+
+== Chatbot
+
+=== How it works
+
+When the user asks a question, we use the configured RAG mode to answer it with the data from the graph of extracted documents. That can mean the question is turned into an embedding or a graph query or a more advanced RAG approach.
+
+We also summarize the chat history and use it as an element to enrich the context.
+
+=== Features
+
+- *Select RAG Mode* you can select vector-only or GraphRAG (vector+graph) modes
+- *Chat with selected documents:* Will use the selected documents only for RAG, uses pre-filtering to achieve that
+- *Details:* Will open a Retrieval information pop-up showing details on how the RAG agent collected and used sources (documents), chunks, and entities. Also provides information on the model used and the token consumption.
+- *Clear chat:* Will delete the current session's chat history.
+- *Expand view:* Will open the chatbot interface in a fullscreen mode.
+- *Copy:* Will copy the content of the response to the clipboard.
+- *Text-To-Speech:* Will read out loud the content of the response.
+
+=== GraphRAG
+
+For GraphRAG we use the Neo4j Vector Index (and a fulltext index for hybrid search) with a Retrieval Query to find the most relevant chunks and entities connected to these and then follow the entity relationships up to a depth of 2 hops.
+
+=== Vectory Only RAG
+
+For Vector only RAG we only use the vector and fulltext index (hybrid) search results and don't include additional information from the entity graph.
+
+=== Answer Generation
+
+The various inputs and determined sources (the question, vector results, entities (name + description), relationship pairs, chat history) are all sent to the selected LLM model as context information in a custom prompt, asking to provide and format a response to the question asked based on the elements and context provided. 
+
+Of course, there is more magic to the prompt such as formatting, asking to cite sources, not speculating if the answer is not known, etc. The full prompt and instructions can be found in the https://github.com/neo4j-labs/llm-graph-builder[GitHub repository^].
diff --git a/modules/genai-ecosystem/pages/llm-graph-builder.adoc b/modules/genai-ecosystem/pages/llm-graph-builder.adoc
new file mode 100644
index 00000000..0a4cca36
--- /dev/null
+++ b/modules/genai-ecosystem/pages/llm-graph-builder.adoc
@@ -0,0 +1,122 @@
+= Neo4j LLM Knowledge Graph Builder - Extract Nodes and Relationships from Unstructured Text +
+(PDF, Documents, YouTube, Webpages)
+include::_graphacademy_llm.adoc[]
+:slug: llm-graph-builder
+:author: Michael Hunger, Tomaz Bratanic, Persistent
+:category: genai-ecosystem
+:tags: 
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: llm-graph-builder
+:imagesdir: https://dev.assets.neo4j.com/wp-content/uploads/2024/
+
+// image::llm-graph-builder.png[width=600, align=center]
+image::https://dist.neo4j.com/wp-content/uploads/20240618104511/build-kg-genai-e1718732751482.png[width=800, align=center,link="https://llm-graph-builder.neo4jlabs.com/",window="_blank"]
+
+The Neo4j LLM Knowledge Graph Builder is an https://llm-graph-builder.neo4jlabs.com/[online application^] for turning unstructured text into a knowledge graph, it provides a magical text to graph experience.
+
+It uses ML models (LLM - OpenAI, Gemini, Llama3, Diffbot, Claude, Qwen) to transform PDFs, documents, images, web pages, and YouTube video transcripts.
+The extraction turns them into a lexical graph of documents and chunks (with embeddings) and an entity graph with nodes and their relationships, which are both stored in your Neo4j database.
+You can configure the extraction schema and apply clean-up operations after the extraction.
+
+Afterwards you can use different RAG approaches (GraphRAG, Vector, Text2Cypher) to ask questions of your data and see how the extracted data is used to construct the answers.
+
+[NOTE]
+====
+* best results for files with long-form text in English
+* less well suited for tabular data like Excel or CSV or images/diagrams/slides
+* higher quality data extraction if you configure the graph schema for nodes and relationship types
+====
+
+The front-end is a React Application and the back-end a Python FastAPI application running on Google Cloud Run, but you can deploy it locally using docker compose.
+It uses the https://python.langchain.com/docs/use_cases/graph/constructing[llm-graph-transformer module^] that Neo4j contributed to LangChain and other langchain integrations (e.g. for GraphRAG search).
+
+All Features are documented in detail here: xref::llm-graph-builder-features.adoc[]
+
+Here is a quick demo:
+
+++++
+
+++++
+
+== Step by Step Instructions
+
+1. Open the https://llm-graph-builder.neo4jlabs.com/[LLM-Knowledge Graph Builder^]
+2. Connect to a https://console.neo4j.io[Neo4j (Aura)^] instance
+3. Provide your PDFs, Documents, URLs or S3/GCS buckets
+4. Construct Graph with the selected LLM
+5. Visualize Knowledge Graph in App
+6. Chat with your data using GraphRAG
+7. Open Neo4j Bloom for further visual exploration
+8. Use the constructed knowledge graph in your applications
+
+image::llm-graph-builder-viz.png[width=600, align=center]
+
+[NOTE]
+====
+* Login, or create an account at https://console.neo4j.io
+* Under Instances, create a new AuraDB Free Database
+* Downloads the credentials file
+* Wait until the instance is running
+* Drop the credentials file on the connect dialog for the LLM Graph Builder
+====
+
+== How it works
+
+1. Uploaded Sources are stored as `Document` nodes in the graph
+2. Each document (type) is loaded with the LangChain Loaders
+3. The content is split into Chunks 
+4. Chunks are stored in the graph and connected to the Document and to each other for advanced RAG patterns
+5. Highly similar Chunks are connected with a `SIMILAR` relationship to form a kNN Graph
+6. Embeddings are computed and stored in the Chunks and Vector index
+7. Using the llm-graph-transformer or diffbot-graph-transformer entities and relationships are extracted from the text
+8. Entities and relationships are stored in the graph and connected to the originating Chunks
+
+// TODO architecture diagram
+image::https://dist.neo4j.com/wp-content/uploads/20240618104514/retrieval-information-e1718732797663.png[width=800, align=center]
+
+== Relevant Links
+[cols="1,4"]
+|===
+| Online Application | https://llm-graph-builder.neo4jlabs.com/
+| icon:user[] Authors | Michael Hunger, Tomaz Bratanic, Niels De Jong, Morgan Senechal, Persistent Team
+| icon:comments[] Community Support | https://community.neo4j.com/c/neo4j-graph-platform/genai/214[Neo4j Online Community^]
+| icon:github[] Repository | https://github.com/neo4j-labs/llm-graph-builder[GitHub^]
+| icon:github[] Issues | https://github.com/neo4j-labs/llm-graph-builder/issues[GitHub Issues^]
+| icon:book[] LangChain | https://python.langchain.com/v0.1/docs/use_cases/graph/constructing/[LangChain KG Construction Module^]
+|===
+
+
+== Installation
+
+The https://llm-graph-builder.neo4jlabs.com[LLM Knowledge Graph Builder Application^] is available online.
+
+You can also run it locally, by cloning the https://github.com/neo4j-labs/llm-graph-builder[GitHub repository] and following the instructions in the README.md file.
+
+It is using Docker for packaging front-end and back-end, and you can run `docker-compose up` to start the whole application.
+
+Local deployment and configuration details are available in the xref::llm-graph-builder-deployment.adoc[]
+
+
+== Videos & Tutorials
+
+image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/llm-knowledge-graph-construction.png[width=800,link="https://graphacademy.neo4j.com/courses/llm-knowledge-graph-construction/"]
+
+=== Detailed Walk-Through
+
+++++
+
+++++
+
+=== Livestream LLM-Knowledge Graph Builder
+
+++++
+
+++++
+
+////
+
+== Highlighted Articles
+
+// link:https://medium.com/neo4j/....[article^]
+////
diff --git a/modules/genai-ecosystem/pages/microsoft-azure-demo.adoc b/modules/genai-ecosystem/pages/microsoft-azure-demo.adoc
new file mode 100644
index 00000000..3f623806
--- /dev/null
+++ b/modules/genai-ecosystem/pages/microsoft-azure-demo.adoc
@@ -0,0 +1,33 @@
+= Microsoft Azure Demo
+include::_graphacademy_llm.adoc[]
+:slug: microsoft-azure-demo
+:author: Ben Lackey
+:category: genai-ecosystem
+:tags: rag, demo, retrieval augmented generation, chatbot, edgar, sec filings, azure, openai
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: microsoft-azure-demo
+
+This is a sample notebook and web application which shows how Azure OpenAI can be used with Neo4j. We will explore how to leverage Azure OpenAI LLMs to build and consume a knowledge graph in Neo4j.
+
+This notebook parses data from a public corpus of Medical Case Sheet using Azure OpenAI's gpt-4-32k model. The model is prompted to recognise and extract entities and relationships.
+
+We then use the gpt-4-32k model and prompt it to convert questions in English to Cypher - Neo4j's query language for data retrieval.
+
+== Installation
+
+The Demo is available on GitHub: https://github.com/neo4j-partners/neo4j-generative-ai-azure
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:github[] Code Repository | https://github.com/neo4j-partners/neo4j-generative-ai-azure[GitHub]
+| Demo Video | https://www.youtube.com/watch?v=3PO-erAP6R4&list=PLG3nTnYVz3nya8Me9-Xj9vEuLYIOk03ba[Link]
+| Slides | https://docs.google.com/presentation/d/16KtVfpRoQWoUTY9UAK7fDBm-ZTCG7NrT0VCSa5brmLY/edit?usp=sharing[Link]
+|===
+
+== Videos & Tutorials
+
+++++
+
+++++
diff --git a/modules/genai-ecosystem/pages/neoconverse.adoc b/modules/genai-ecosystem/pages/neoconverse.adoc
new file mode 100644
index 00000000..58ebd265
--- /dev/null
+++ b/modules/genai-ecosystem/pages/neoconverse.adoc
@@ -0,0 +1,164 @@
+= NeoConverse - Graph Database Search with Natural Language
+include::_graphacademy_llm.adoc[]
+:slug: neoconverse
+:author: Kumar SS, Eric Monk
+:category: genai-ecosystem
+:tags: 
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: neoconverse
+:imagesdir: https://dev.assets.neo4j.com/wp-content/uploads/2024/
+
+image::neoconverse-latest.gif[width=800, align=center]
+
+NeoConverse is an application to demonstrate natural language database search with GenAI.
+It makes existing graph databases available to non-technical users, allowing them to issue natural language questions.
+Using the graph database schema, question-statement examples, and fine-tuning, the natural language question is translated by an LLM to a graph query (Cypher). 
+Which is then validated and executed against the database.
+
+The query results are sent with the user question to the LLM to generate a natural language answer.
+Alterantively the LLM can be configured to generate the data an configuration to render the results of the query as a chart for visual representation.
+
+image::neoconverse-chart.png[width=800, align=center]
+
+== Functionality Includes
+
+* Experiment with different cloud providers' GenAl models: Currently supported are GCP and OpenAI, with AWS Bedrock coming soon.
+* Connect your own neo4j databases as agents in NeoConverse and interact with them in plain English:
+  - Configure schema information as context for the large language model (LLM).
+  - Optionally add a few-shot examples to provide in-context learning to the LLM.
+  - Save the conversation in a configured database for future analysis:
+        . Evaluate the LLM responses.
+        . Rank the responses and use them to improve future LLM interactions
+        . Prompt engineering adjustments.
+* Generate chart visualizations from natural language questions.
+* Interact with predefined agents from different domains:
+  - Chat with these agents.
+  - Explore the data model of the predefined agent datasets.
+  - Loaded with sample questions to get you started.
+
+=== Configuring Agents
+
+NeoConverse includes with selection of *predefined conversational agents*, each backed by Neo4J database, 
+To explore and converse with Neo4j database using these agents, user can select any agent from the `Explore Predefined Agent` section.
+Upon selection, users can engage with the chosen agent through the chat interface displayed on the right side of the screen.
+This enables users to interact directly with the database graoh, faciltating an interactive exploration of the knowledge graph.
+
+Users have the flexibility to `*add your own local agents*` that are backed by your organizations neo4j database, and start interacting with your database knowledge graphs. 
+
+Below is the quick visual guide to `ADD`, `DELETE`, `EDIT` agents, and interact with your own database
+
+// image::add-agent[width=800, align=center]
+// image::https://kumarss.blob.core.windows.net/neoconverse-resources/add-agent.gif?sp=r&st=2024-04-10T19:12:09Z&se=2026-04-11T03:12:09Z&spr=https&sv=2022-11-02&sr=b&sig=EYn4U%2B1AVCteCs1Va%2FxSAfDLPl%2FytHBjRZd%2Bji%2Bc058%3D[]
+++++
+
+++++
+
+[WARNING]
+====
+When integrating your own agents, be aware that these agents are stored in the client's browser storage and not on a server. This means that all interactions, including API calls and result processing, happen on the client side and do not reach our servers. Consequently, if the browser’s local storage is cleared for reasons such as browsing data cleanup, selection of "cookies and other site data", or when local storage capacity is exceeded, all custom agents will be permanently lost.
+====
+
+[IMPORTANT]
+====
+We are considering future updates to enhance user experience. These updates may include capabilities for the export/import of agents and the persistence of user-defined agent configurations within the user’s own database. Such features aim to provide a more unified and simplified experience across different users and sessions.
+====
+
+=== Generating Chart Visualizations
+
+NeoConverse empowers users to create chart visualizations directly from the chat interface. By switching between the text input and a chart icon within the chat window, users can seamlessly transition their queries into visual representations. Here's how it works: Users pose their question in plain English, which is then transformed into a Cypher query for execution in the Neo4J database. Following this, the query results are passed to a Large Language Model (LLM), which generates chart metadata based on the data. The LLM intelligently selects the most appropriate chart type for the data at hand and returns the corresponding chart metadata. This metadata is then used to render the chart, enabling users to visualize and interpret the data insights more effectively and in an easily consumable manner.
+
+Looking ahead, NeoConverse is considering the expansion of its visualization capabilities to include graph visualizations. This development will be contingent upon the interest and feedback from NeoConverse users, potentially broadening the scope of interactive data exploration.
+// TODO add a gif for chart generation
+
+== How NeoConverse Works: Overcoming LLM Limitations
+
+Large Language Models (LLMs) are trained on massive public datasets, making them adept at tasks based on that knowledge. However, their usefulness in enterprise settings is limited because:
+
+* Their knowledge is confined to the training data.
+* Updating their knowledge with internal data can be challenging.
+* Information may be outdated.
+
+*NeoConverse leverages Retrieval-Augmented Generation (RAG) to address these issues.* RAG provides LLMs with additional context retrieved from internal data sources hosted in Neo4j graph databases.
+
+Here's the step-by-step process of how NeoConverse works:
+
+1. *User Asks a Question:* The user asks a natural language question about their enterprise data stored in Neo4j.
+2. *Context for LLM:* NeoConverse provides the LLM with context relevant to the question:
+    * Schema information about the data stored in the Neo4j database. 
+    * A few relevant examples from the data to guide the LLM.
+3. *LLM Generates Cypher Query:* Based on the question and context, the LLM generates a Cypher query, the query language for Neo4j.
+4. *Query Refinement and Execution:* NeoConverse cleans, formats, and executes the Cypher query on the Neo4j database.
+5. *Data Retrieval:* Relevant data is retrieved from the Neo4j database based on the executed query.
+6. *LLM Answer Generation:* Finally, the LLM uses the retrieved data and the original question to generate an accurate and relevant answer that's grounded in the user's specific data.
+
+By leveraging internal data, NeoConverse empowers LLMs to provide more accurate and relevant answers to user queries within an enterprise setting.
+
+image::neoconverse-arch.png[width=600, align=center]
+// image::https://kumarss.blob.core.windows.net/neoconverse-resources/neoconverse-arch.png?sp=r&st=2024-04-10T19:08:45Z&se=2026-04-09T03:08:45Z&spr=https&sv=2022-11-02&sr=b&sig=vE8JahnRHRgNO8xEZzQejX0ACMQdQ9kr6FzeIKs0ILw%3D[]
+
+== Available predefined datasets
+* *Enron Email Corpus Dataset*:  The subset of publicly available enron email corpus data has been anonymized and ingested in neo4j database, the ingested dataset contains about 500K email messages, The dataset facilitates an in-depth exploration of email communication both within enron leadership internally and with external parties, providing means to analyse anomolies and email leakages.  In addition to email communcation metadata, this dataset also enriched with entities and watch terms extracted from email subject & body.
+* *Patient Journey Dataset* :  The patient journey dataset encapsulates the healthcare experiences of 1 million patients, detailing 41 million encounters that span doctor visits, diagnoses, treatments, and allergies, among other medical events. With 11 million prescriptions, 26 million observations, 9 million condition-specific observations, 200 unique conditions, and 500K allergy encounters, this rich dataset offers a comprehensive view of individual health narratives. It's a crucial resource for analyzing treatment patterns, patient outcomes, and drug efficacy, providing insights that can enhance healthcare delivery and personalize patient care strategies.
+* *Retail Dataset* : This retail dataset leverages a knowledge graph built from a public Amazon electronics dataset containing product information, reviews, and user purchase and review history. To extract deeper insights, Neo4j GDS graph algorithms have been applied to calculate similarity scores between users and products, considering not only product features and user demographics but also how users interact with the products.
+* *Business Intelligence*: Its a Knowledge graph of Software Applications with supported business processes, deployed instances, software vulnerability reports,
+and data concepts that provides impact analysis for vulnerabilities as well as impact analysis for application changes and data traceability
+// TODO Add details on Business resilience and retail dataset
+
+== Enhancing NeoConverse
+
+NeoConverse relies on Large Language Models (LLMs) cypher generation capabilities, while LLMs have shown improvements in generating Cypher queries, yet inconsistencies persist. NeoConverse is designed to identify these inconsistencies, providing users with effective ways to navigate or bypass them, rather than confronting them with raw errors. This approach ensures a smoother user experience.
+
+In future NeoConverse can be enhanced to mitigate these inconsistencies by:
+
+- *Fine-Tuning LLMs*: Finetune LLMs for cypher generation for more accurate and consistent Cypher queries.
+- *Developing a Semantic Layer*: To deepen LLMs' contextual understanding, improving result accuracy.
+- *Incorporating Domain-Specific Nomenclature*: To increase the relevance and precision of queries.
+- *Leveraging Historical Conversations*: Using past interactions to provide richer context and more relevant examples to LLMs.
+
+These initiatives aim to address current challenges and improve the reliability of LLM-generated Cypher queries in NeoConverse.
+
+
+== Relevant Links
+[cols="1,4"]
+|===
+| Online | https://neoconverse.graphapp.io/
+| icon:user[] Authors | Kumar SS, Eric Monk
+| icon:comments[] Community Support | https://community.neo4j.com/c/neo4j-graph-platform/genai/214[Neo4j Online Community^]
+| icon:github[] Code Repository | https://github.com/neo4j-labs/neoconverse[GitHub^]
+| icon:github[] Issues | https://github.com/neo4j-labs/neoconverse/issues[GitHub Issues^]
+// | icon:book[] Documentation |
+|===
+
+
+== Installation
+
+The Demo is available online, https://neoconverse.graphapp.io
+
+You can also run it locally, by cloning the https://github.com/neo4j-labs/neoconverse[repository] and following the instructions in the README.md file.
+
+== Highlighted Articles
+
+* link:https://medium.com/@kumarss20/from-code-to-conversation-unleashing-the-potential-of-neo4j-with-llm-powered-conversational-0fd243337e9d[From code to conversation^]
+* link:https://medium.com/@kumarss20/text-to-chart-generation-e654720b15a0[Text to chart generation^]
+
+
+// == Videos & Tutorials
+
+// TODO Dan Bukowski workshops and articles
+
+// image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/llm-knowledge-graph-construction.png[width=800,link="https://graphacademy.neo4j.com/courses/llm-knowledge-graph-construction/"]
+
+
+////
+++++
+
+++++
+////
+
+////
+
+== Highlighted Articles
+
+// link:https://medium.com/neo4j/....[article^]
+////
diff --git a/modules/genai-ecosystem/pages/rag-demo.adoc b/modules/genai-ecosystem/pages/rag-demo.adoc
new file mode 100644
index 00000000..2388b42b
--- /dev/null
+++ b/modules/genai-ecosystem/pages/rag-demo.adoc
@@ -0,0 +1,52 @@
+= RAG (Retrieval Augmented Generation) Demo
+include::_graphacademy_llm.adoc[]
+:slug: rag-demo
+:author: Andreas Kolleger, Jennifer Reif, Jason Koo, Alison Cosette
+:category: genai-ecosystem
+:tags: rag, demo, retrieval augmented generation, chatbot, edgar, sec filings
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: rag-demo
+
+image::https://res.cloudinary.com/dk0tizgdn/image/upload/v1707842287/rag-demo-short_vwezew.gif[width=600, align=center]
+
+
+The RAG Demo uses a knowledge graph of the SEC filings with additional company information with the Neo4j vector index and LangChain to provide a retrieval augmented generation (RAG) chatbot. 
+
+The chatbot can answer questions about the SEC filings and provide additional information about the companies.
+
+
+== Installation
+
+The Demo is available online under: https://dev.neo4j.com/rag-demo
+
+////
+== Functionality Includes
+
+== Documentation
+////
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | https://github.com/jmhreif[Jennifer Reif^], https://github.com/akollegger[Andreas Kollegger^] https://github.com/jalakoo/[Jason Koo^], https://github.com/alisoncossette[Alison Cosette^]
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Code Repository | https://github.com/neo4j-examples/rag-demo[GitHub]
+| icon:github[] Data Preparation Repository | https://github.com/neo4j-product-examples/data-prep-sec-edgar/[GitHub]
+| icon:github[] Issues | https://github.com/neo4j-product-examples/data-prep-sec-edgar/issues
+// | icon:book[] Documentation |
+|===
+
+////
+
+== Videos & Tutorials
+
+Installation:
+++++
+
+++++
+
+== Highlighted Articles
+
+// link:https://medium.com/neo4j/....[article^]
+////
diff --git a/modules/genai-ecosystem/pages/semantic-kernel.adoc b/modules/genai-ecosystem/pages/semantic-kernel.adoc
new file mode 100644
index 00000000..0ffeb199
--- /dev/null
+++ b/modules/genai-ecosystem/pages/semantic-kernel.adoc
@@ -0,0 +1,86 @@
+= Semantic Kernel Neo4j Integration (preview)
+:slug: semantic-kernel
+:author: 
+:category: genai-ecosystem
+:tags: semantic-kernel, integration, llm, neo4j, vector index
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: semantic-kernel
+
+
+An integration of Neo4j graph database with Microsoft's https://github.com/microsoft/semantic-kernel[Semantic Kernel^], 
+through Microsoft https://microsoft.github.io/kernel-memory/[Kernel Memory^]. Neo4j's vector search index is used for storing document embeddings and dense retrievals.
+
+The library allows using Neo4j as an IMemoryDb. It provides a simple way to store and retrieve data from Neo4j.
+
+== Installation
+
+Hello world example:
+
+[source,csharp]
+----
+# dotnet add package Neo4j.KernelMemory.Core
+# dotnet add package Neo4j.Driver
+
+using Microsoft.KernelMemory;
+using Neo4j.KernelMemory.MemoryStorage;
+using Neo4j.Driver;
+
+var env = System.Environment.GetEnvironmentVariables();
+
+var neo4jConfig = new Neo4jConfig
+{
+    Uri = env["NEO4J_URI"],
+    Username = env["NEO4J_USERNAME"],
+    Password = env["NEO4J_PASSWORD"]
+};
+
+var kernelMemory = new KernelMemoryBuilder()
+    .WithOpenAIDefaults(env["OPENAI_API_KEY"])
+    .WithNeo4j(neo4jConfig)
+    .Build();
+
+// First, provide some text to the Kernel Memory, which will be indexed and stored in Neo4j
+await kernelMemory.ImportTextAsync("""
+The "Hello, World!" program, often attributed to Brian Kernighan's work in the 1970s, serves as the quintessential introduction to programming languages, demonstrating basic syntax with a simple output function. Originating as a test phrase in Bell Laboratories for the B programming language, it has evolved into a universal starter program for beginners in coding, symbolizing the initiation into software development. Its simplicity makes it an ideal tool for education and system testing, illustrating the minimal requirements to execute a program across various computing environments. As a cultural staple in the tech community, "Hello, World!" represents both a rite of passage for new programmers and the universal joy of creating with code. This tradition showcases the evolution of programming languages and the shared beginnings of developers worldwide.
+""", 
+    documentId: "HelloWorld");
+
+// Now ask a question
+var question = "Who wrote the first Hello World?";
+
+var answer = await kernelMemory.AskAsync(question);
+
+Console.WriteLine($"Question: {question}\n\nAnswer: {answer.Result}");
+
+----
+
+== Functionality Includes
+
+* `Neo4jMemory` - implementation of `IMemoryDb`.
+* `WithNeo4j` - extension to the KernelMemoryBuilder.
+* Integation with Semantic Kernel
+* Develop in serverless, deploy as server
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | https://github.com/akollegger[Andreas Kollegger^]
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Integration | https://github.com/neo4j-labs/Neo4j.KernelMemory.MemoryStorage[GitHub]
+| icon:github[] Issues | https://github.com/neo4j-labs/Neo4j.KernelMemory.MemoryStorage/issues
+| icon:book[] Documentation | https://github.com/neo4j-labs/Neo4j.KernelMemory.MemoryStorage
+| icon:github[] Starter Kit | https://github.com/neo4j-examples/semantic-kernel-starter-kit[Sematic Kernel Starter Kit]
+| icon:github[] Examples | https://github.com/neo4j-labs/Neo4j.KernelMemory.MemoryStorage/tree/main/examples[GithHub]
+|===
+
+
+== Videos & Tutorials
+
+
+
+////
+== Highlighted Articles
+
+// link:https://medium.com/neo4j/....[article^]
+////
diff --git a/modules/genai-ecosystem/pages/spring-ai.adoc b/modules/genai-ecosystem/pages/spring-ai.adoc
new file mode 100644
index 00000000..bab3607f
--- /dev/null
+++ b/modules/genai-ecosystem/pages/spring-ai.adoc
@@ -0,0 +1,80 @@
+= Spring AI
+:slug: spring-ai
+:author: Gerrit Meier, Michael Simons, Jennifer Reif
+:category: labs
+:tags: spring, neo4j, vector index, vector search
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: spring-ai
+
+// image::todo.png[width=800]
+
+The https://docs.spring.io/spring-ai/reference/index.html[Spring AI^] project aims to be something like LangChain for the Java world.
+
+Combining Spring AI with Spring Data Neo4j makes it possible to build on existing domain models, turn them into a graph, and enrich them with vector embeddings.
+
+== Installation
+
+If you are using the https://start.spring.io/[Spring Initializr^], you can add the Spring AI model of your choosing and `Neo4j Vector Database` as dependencies to your project, and it will pull in all the related pieces.
+
+If you add the dependencies manually, Spring AI is not yet a general release library, so you will need to add the dependencies, as well as the snapshot and milestone repositories to your `pom.xml` file, as outlined in the https://docs.spring.io/spring-ai/reference/getting-started.html#repositories[Getting Started documentation^].
+
+The next piece you will need to add is the configuration for the Neo4j Vector Database, which also needs to be accompanied by custom driver and embedding client configuration. Probably the simplest approach is to create Spring Beans for each of these components in the main application class. This is mentioned in the https://docs.spring.io/spring-ai/reference/api/vectordbs/neo4j.html#_auto_configuration[documentation^], but the full code is not provided. An example of how to configure the Neo4j Vector Database is shown below (note that you will need to alter the `vectorStore` bean to match your specific configuration):
+
+[source,java,tabsize=2]
+----
+include::https://github.com/neo4j-examples/spring-ai-starter-kit/raw/main/src/main/java/com/neo4j/springaistarterkit/SpringAiStarterKitApplication.java[lines=21..42,indent=0]
+----
+
+Now that we have the vector store configured, we can use Spring AI for retrieval augmented generation (RAG) via a three-step process.
+
+1. Call the vector similarity search method to retrieve the most similar documents.
+2. Use the similar documents as input to a retrieval query that pulls related entities from the graph.
+3. Provide the similar documents (with their related graph entities) as input to a prompt that the LLM will use to generate a response.
+
+The code snippet below demonstrates how to use Spring AI for RAG with Neo4j Vector Store:
+
+[source,java,tabsize=2]
+----
+include::https://github.com/neo4j-examples/spring-ai-starter-kit/raw/main/src/main/java/com/neo4j/springaistarterkit/DocumentController.java[lines=30..57,indent=0]
+----
+
+== Functionality Includes
+
+* Create vector index
+* Populate nodes and vector index from documents
+* Query vector index
+
+== Documentation
+
+The Neo4j Vector integration documentation is avalable in the https://docs.spring.io/spring-ai/reference/api/vectordbs/neo4j.html[Spring AI Reference Guide^].
+
+== Starter Kit
+
+Getting started in any new technology space can feel intimidating. Neo4j has been working on some pre-packaged solutions for GenAI and Neo4j to hopefully make the process easier by providing starter kit projects with a few key technologies, including Spring AI. You can find the https://github.com/neo4j-examples/spring-ai-starter-kit/[Spring AI starter kit code^] on Github, as well as a [https://neo4j.com/developer-blog/genai-starter-kit-spring-java/[blog post with more information^].
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | https://github.com/meistermeier[Gerrit Meier^], https://github.com/rotnroll666[Michael Simons^], https://github.com/JMHReif[Jennifer Reif^]
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Data Repository | https://github.com/spring-projects/spring-ai[GitHub]
+| icon:github[] Issues | https://github.com/spring-projects/spring-ai/issues
+| icon:book[] Documentation | https://docs.spring.io/spring-ai/reference/api/vectordbs/neo4j.html
+| icon:github[] Starter Kit | https://github.com/neo4j-examples/spring-ai-starter-kit/[Spring AI Starter Kit^]
+|===
+
+////
+
+== Videos & Tutorials
+
+Installation:
+++++
+
+++++
+
+////
+
+== Highlighted Articles
+
+* https://meistermeier.com/2024/02/23/spring-ai-neo4j.html[Spring AI with Neo4j Vector Index (Gerrit Meier)^]
diff --git a/modules/genai-ecosystem/pages/vector-search.adoc b/modules/genai-ecosystem/pages/vector-search.adoc
new file mode 100644
index 00000000..c5007e19
--- /dev/null
+++ b/modules/genai-ecosystem/pages/vector-search.adoc
@@ -0,0 +1,98 @@
+= Neo4j Vector Index and Search
+include::_graphacademy_llm.adoc[]
+:slug: vector-search
+:author: 
+:category: genai-ecosystem
+:tags: vector index, vector search, embeddings, neo4j
+:neo4j-versions: 5.11+
+:page-pagination:
+:page-product: neo4j
+
+image::https://dist.neo4j.com/wp-content/uploads/20230821135317/Grounding-LLM-Responses-with-Implicit-and-Explicit-Search-Through-Neo4js-Knowledge-Graph-2048x1152.png[width=800]
+
+The Neo4j Vector index implements HNSW (Hierarchical Navigatable Small World) for creating layers of k-nearest neighbors to enable efficient and robust approximate nearest neighbor search. 
+The index is designed to work with embeddings, such as those generated by machine learning models, and can be used to find similar nodes in a graph based on their embeddings.
+
+== Functionality Includes
+
+* Create a vector index with a specified number of dimensions and similarity function (euclidean, cosine) for both nodes and relationships
+* Query vector index with embedding and top-k, returning nodes and similarity score
+* Procedures to compute text vector embeddings with (Azure) OpenAI, AWS Bedrock, Google Vertex AI, and other ML platforms 
+* Vector similarity functions to compute cosine angle and euclidean distance between vectors
+
+image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/llm-vectors-unstructured.png[width=800,link="https://graphacademy.neo4j.com/courses/llm-vectors-unstructured/"]
+
+== Usage
+
+[source,cypher]
+----
+// create vector index
+CREATE VECTOR INDEX `abstract-embeddings`
+FOR (n: Abstract) ON (n.embedding)
+OPTIONS {indexConfig: {
+ `vector.dimensions`: 1536,
+ `vector.similarity_function`: 'cosine'
+}};
+
+
+// set embedding as parameter
+MATCH (a:Abstract {id: $id})
+CALL db.create.setNodeVectorProperty(a, 'embedding', $embedding);
+
+
+// use a genai function to compute the embedding
+MATCH (a:Abstract {id: $id})
+WITH a, genai.vector.encode(a.text, "OpenAI", { token: $token }) AS embedding
+CALL db.create.setNodeVectorProperty(n, 'embedding', embedding);
+
+
+// query vector index for similar abstracts
+MATCH (title:Title)<--(:Paper)-->(abstract:Abstract)
+WHERE title.text CONTAINS = 'hierarchical navigable small world graph'
+CALL db.index.vector.queryNodes('abstract-embeddings', 10, abstract.embedding)
+YIELD node AS similarAbstract, score
+
+MATCH (similarAbstract)<--(:Paper)-->(similarTitle:Title)
+RETURN similarTitle.text AS title, score;
+
+
+// use cosine similarity for exact nearest neighbor search
+// pre-filter vector search
+MATCH (venue:Venue)<--(:Paper)-->(abstract:Abstract)
+WHERE venue.name CONTAINS 'NETWORK'
+
+WITH abstract, paper, 
+     vector.similarity.cosine(abstract.embedding, $embedding) AS score
+WHERE score > 0.9
+
+RETURN paper.title AS title, abstract.text, score
+ORDER BY score DESC LIMIT 10;
+----
+
+== Documentation
+
+== Relevant Links
+[cols="1,4"]
+|===
+| icon:user[] Authors | Neo4j Engineering
+| icon:comments[] Community Support | https://community.neo4j.com/[Neo4j Online Community^]
+| icon:github[] Repository | https://github.com/neo4j/neo4j[GitHub]
+| icon:github[] Issues | https://github.com/neo4j/neo4j/issues
+| icon:book[] Documentation | https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/[Vector Index and Search^]
+| icon:book[] Documentation | https://neo4j.com/docs/cypher-manual/current/genai-integrations/[GenAI Embedding Procedures^]
+| icon:book[] Documentation | https://neo4j.com/docs/cypher-manual/current/functions/vector/[Vector Similarity Functions^]
+|===
+
+
+== Videos & Tutorials
+
+++++
+
+++++
+
+== Highlighted Articles
+
+* https://neo4j.com/blog/vector-search-deeper-insights/[​​Neo4j’s Vector Search: Unlocking Deeper Insights for AI-Powered Applications^]
+* https://www.linkedin.com/pulse/vector-indexing-plus-knowledge-graphs-neo4j-jeff-tallman-ayxve/[Vector Indexing plus Knowledge Graphs in Neo4j^]
+* https://medium.com/@therobbrennan/explore-openai-vector-embedding-with-neo4j-6ea2a40693d9[Explore OpenAI Vector Embedding with Neo4j^]
+* https://medium.com/neo4j/youtube-transcripts-knowledge-graphs-for-rag-applications-2cc790543d4b[YouTube Transcripts Into Knowledge Graphs for RAG Applications]
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@@ -1,886 +0,0 @@
-= Graph App Development for Neo4j Desktop
-:level: Advanced
-:page-level: Advanced
-:author: Adam Cowley
-:category: desktop
-:tags: graph-apps, app-development, drivers
-:description: This guide explains how to develop a Graph App for Neo4j Desktop.
-:page-pagination: previous
-:page-aliases: ROOT:graph-app-development.adoc
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-This article assumes that you already have a basic understanding of HTML and CSS.
-It will also discuss some concepts of Single Page Applications.
-
-. Basic knowledge of HTML and JavaScript and an understanding of Single Page Applications (SPAs)
-. Knowledge of the link:/developer/javascript[offical JavaScript Drivers]
-. If you haven't already, http://neo4j.org/download[download and install Neo4j Desktop^]
-
-[.discrete.ad]
-== link:https://graphacademy.neo4j.com/?ref=guides[Learn with GraphAcademy^]
-
-image::https://graphacademy.neo4j.com/courses/app-nodejs/badge/[float=left]
-
-[.discrete]
-=== link:https://graphacademy.neo4j.com/courses/app-nodejs/?ref=guides[Building Neo4j Applications with Node.js^]
-
-In this free course, we walk through the steps to integrate Neo4j into your Node.js projects.
-You will learn about the Neo4j JavaScript Driver, how sessions and transactions work and how to query Neo4j from an existing application.
-
-link:https://graphacademy.neo4j.com/courses/app-nodejs/?ref=guides[Learn more^,role=button]
-
-[#graphapp-dev-intro]
-== Introduction
-
-So, you would like to build a Graph App?  You are in good company, many enterprises and community developers are building Graph Apps for a wide range of use cases.
-These range from utility tools like monitoring tool https://halin.graphapp.io/[Halin^] and educational Graph Apps like Neuler to visulisation apps including link:/blog/graphxr-graph-app-neo4j-desktop/[GraphXR by Kineviz^] and https://medium.com/neo4j/introducing-neomap-a-neo4j-desktop-application-for-spatial-data-3e14aad59db2[NeoMap^].
-
-Graph apps can be built with vanilla HTML and JavaScript, but most commonly we see these built with modern front end frameworks like https://reactjs.org/[React^] or https://vuejs.org/[Vue.js^].
-Whichever framework you choose to build your application, you will interact with the Graph the same way; by running a Cypher Query through the https://github.com/neo4j/neo4j-javascript-driver[Official JavaScript drivers^].
-There is plenty of content over on the link:/developer/javascript/[JavaScript developer guides] so we will not cover this in too much detail, only the nuances around how the driver works with the Neo4j Desktop API.
-
-If you are looking for inspiration, head over to the https://install.graphapp.io/[Graph App Gallery^] to see what other developers have built.
-Otherwise, let's get started.
-
-[#driver-dependencies]
-== Installing Driver Dependencies
-
-The official JavaScript driver is available via npm using the `npm install` or `yarn add` command.
-
-[source,bash]
-----
-npm install --save neo4j-driver
-----
-
-Alternatively, if your graph app will be used in a context where the always has an active internet connection, you can obtain the driver via a CDN.
-
-[source,html]
-----
-
-
-
-
-
-----
-
-[#enable-dev-tools]
-== Enabling Developer Tools
-
-Neo4j Desktop comes with a Development App that can be used while developing and debugging your Graph App.
-To enable Development Mode, click on the Settings image:{img}settings.png[Settings Icon, title="Settings Icon"] icon in the bottom left hand corner of the screen to reveal the Settings pane.
-
-At the bottom of the Settings pane, you will see a *Developer Tools* form.
-Click *Enable Development Mode* and add fill out the Entry Point and Root Path.
-
-image::{img}developer-tools.png[Developer Tools window with Enable development mode checkbox and fields for Entry Point and Root Path, title="Developer Tools form"]
-
-- *Entry Point*: This is the path to your app, either a location of an HTML file (for example `\file:///Users/me/my-graph-app/index.html`), or an HTTP url where your app is being served (for example `http://localhost:8080`).
-- *Root Path*: This is the path to the root of your development project.
- If you are running Background Processes, this should be the location of the files that contain your Java or Node.js processes  - if in doubt, you can just set it to `/`.
-
-[#get-context]
-== Getting the Context
-
-If you require access to the the list of projects and databases that the user has configured in Neo4j Desktop, you can access this via the Context API.
-There are two ways of accessing Neo4j Desktop's context, either through the Injected API or using GraphQL.
-
-Neo4j Desktop provides information about the current desktop environment via the `neo4jDesktopApi` object that is injected into the window.
-This will provide you with a list the Projects and Database that the user has created.
-
-=== Injected API
-
-The _Injected_ API is provided through the `neo4jDesktopApi` object.
-The `getContext()` method returns a Promise which in turn resolves to an object containing a `projects` key.
-Each Project contains a list of Neo4j Databases and a list of files.
-
-[source,javascript]
-----
-if ( window.neo4jDesktopApi ) {
-  neo4jDesktopApi.getContext()
-    .then(context => {
-      // Access to the list of projects will be through context.projects
-    })
-}
-----
-
-If the context should change for any reason, for example if the active database changes or has been stopped, an event will be fired.
-You can listen for these changes using the `onContextUpdate` method on the API which provides an Event object which includes the type of event (for example), the new context and the previous context.
-
-[source,javascript]
-----
-let online = true
-let context = await neo4jDesktopApi.getContext()
-
-neo4jDesktopApi.onContextUpdate((event, newContext, oldContext) => {
-  // If the database has stopped, go into an 'Offline' mode
-  if ( event.type === "APPLICATION_OFFLINE" ) {
-    online = false
-  }
-
-  // Keep the context
-  context = newContext
-})
-----
-
-====
-[NOTE]
-The Injected API will be removed in Neo4j Desktop version 2.0, however we don't have a date for this version yet.
-For now, you are safe to use it.
-====
-
-The following events will trigger a context update via the API:
-
-.Table Context Event Types
-|===
-| Type | Description | Additional information on event
-| APPLICATION_ONLINE | Neo4j Desktop is now in Online mode |
-| APPLICATION_OFFLINE | Neo4j Desktop is now in Offline mode.  Offline mode can be enabled in the Settings pane under *Offline Mode* |
-| PROJECT_CREATED | A new project has been created |
-
-**id**: the UUID of the project +
-**name**: the name of the project
-
-| PROJECT_REMOVED | A project has been removed | **id**: the uuid of the project
-
-| PROJECT_RENAMED | A project has been renamed |
-
-**id**: the uuid of the updated project +
-**name**: the updated name of the project
-
-| GRAPH_ACTIVE | A graph has been made active |
-
-**id**: the UUID of the graph that has become active
-
-| GRAPH_INACTIVE | The previous active graph has been made inactive |
-
-**id**: the UUID of the graph that has become active
-
-| DBMS_CREATED | A new database has been created within the project with the UUID `projectId` |
-
-**id**: the UUID of the new database +
-**projectId**: the project in which this new database sits +
-**name**: the name of the database +
-**description**: the description given to the database +
-**status**: the status of the database +
-**version**: the version of Neo4j for this database +
-**edition**: the edition of Neo4j: community or enterprise +
-
-| DBMS_STARTED | A database has been started | **id**: the UUID for the database
-| DBMS_STOPPED | The active database has been stopped | **id**: the UUID for the database
-
-| DBMS_RENAMED | A database has been renamed |
-**id**: the UUID for the database +
-**name**: the new name for the database
-
-| DBMS_REMOVED | A database has been deleted | **id**: the UUID for the deleted database
-
-| DBMS_UPDATED | A database has been updated |
-
-**id**: the UUID for the updated database
-**database**: an object containing the updates made for the database
-
-| DBMS_UPGRADED | A database has been upgraded to a newer version of Neo4j |
-
-**id**: the UUID for the upgraded database
-**version**: the upgraded version - for example `4.0.2`
-
-| DBMS_SETTINGS_SAVED | The settings for a database have been changed | **id**: the UUID for the updated database
-| REMOTE_CONNECTION_CREATED | A new remote connection has been added to a project | **id**: the UUID for the new remote connection
-| REMOTE_CONNECTION_REMOVED | A remote connection has been removed from a project | **id**: the UUID for the removed remote connection
-| REMOTE_CONNECTION_ACTIVATED | A remote connection has been activated  | **id**: the UUID for the activated remote connection
-| REMOTE_CONNECTION_DEACTIVATED | A remote connection has been deactivated  | **id**: the UUID for the deactivated remote connection
-|===
-
-=== GraphQL API
-
-The GraphQL API contains the same information as the injected API, but instead is accessed via a GraphQL library.
-For this example we will use link:https://www.apollographql.com/docs/react/get-started/[Apollo Boost^].
-The Apollo Boost package is available via npm or yarn
-
-[source,bash]
-----
-npm install apollo-boost graphql # or yarn add apollo-boost
-----
-
-The information required to access the GraphQL API are provided as part of the URL.
-
-- *neo4jDesktopApiUrl*: The URL of the GraphQL service
-- *neo4jDesktopGraphAppClientId*: A token generated by Neo4j Desktop to verify any requests made by the Graph App
-
-[source,javascript]
-----
-const url = new URL(window.location.href)
-const apiEndpoint = url.searchParams.get("neo4jDesktopApiUrl")
-const clientId = url.searchParams.get("neo4jDesktopGraphAppClientId")
-
-import ApolloClient from "apollo-boost"
-
-const client = new ApolloClient({
-  uri: apiEndpoint,
-  headers: {
-    clientId: clientId
-  }
-});
-----
-
-You can then use the Apollo Client to query the GraphQL API endpoint.
-For example, the following code will give you a list of all projects and their databases.
-
-[source,javascript]
-----
-import gql from 'graphql-tag'
-
-const GET_DATABASES = gql`
-query {
-  workspace {
-  	projects {
-      name
-      graphs {
-        name
-        status
-        connection {
-          info {
-            version
-            edition
-          }
-          principals {
-            protocols {
-              bolt {
-                tlsLevel
-                url
-                username
-                password
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-}
-`
-
-client.query({ query: GET_DATABASES })
-  .then(({ data }) => {
-    // Access the list of projects through data.workspace.projects
-  })
-----
-
-=== Creating a Driver Instance
-
-The next step is to create an instance of the https://github.com/neo4j/neo4j-javascript-driver[JavaScript driver^].
-This will be the point of interaction with a Neo4j Database.
-Now that we have the credentials from the previous step, we can run a series of filter and reduce functions to produce a list of graphs that a driver instance can be created for.
-There will be a maximum of one *Active* graph in Desktop (with the status `ACTIVE`), but you may also have remote graphs that could be displayed.
-To find any active graphs, you could run a reduce and filter on the current context.
-
-====
-[NOTE]
-It is recommended that you use the latest version of the Driver, which is currently **4.0.2**.  You can find full installation instructions on the link:/developer/javascript/[JavaScript language guide].
-====
-
-[source,javascript]
-----
-const graphs = context.projects
-  .map(project => ({
-    graphs: project.graphs.filter(graph => graph.status === "ACTIVE" || graph.connection.type === "REMOTE")
-  }))
-  .reduce((acc, { graphs }) => acc.concat(graphs), [])
-
-const { url, username, password } = graphs[0].connection.configuration.protocols.bolt
-----
-
-Once you have the correct credentials, you can create an instance of the Driver and run the session.
-
-[source,javascript]
-----
-const driver = new neo4j.driver(url, neo4j.auth.basic(username, password))
-
-const session = driver.session()
-
-session.run('MATCH (n) RETURN n LIMIT 20')
-  .then(res => {
-    // Handle the Results
-  })
-----
-
-[#graphapp-prod-ready]
-== Getting ready for Production
-
-There are a few steps to follow in order to get your Graph App ready for Production.
-
-=== package.json
-
-If you use npm or yarn, you will be familiar with the `package.json` file.
-This file sits in the root of your project and holds various metadata including the name of your project and any third-party dependencies.
-Adding a `neo4jDesktop` setting  to your package.json will allow you to tie your Graph App to a particular version of the Neo4j Desktop API or {#permissions}[request certain permissions].
-The `name` and `version` of the project are read from package.json and used when deciding whether to install a new Graph App or update an existing install.
-
-[source,bash]
-----
-{
-  "name": "my-graph-app",
-  "version": "1.0.0",
-  "description": "(desktop)-[:LOVES]->(apps)",
-  "homepage": "http://neo4j.com",
-  "neo4jDesktop": {
-    "apiVersion": "^1.4.0",
-    "permissions": [ "allGraphs" ]
-  }
-}
-----
-
-In this example, we are tying the Graph App to the Neo4j Desktop API version 1.4.0 or higher and requesting permission to access all Graphs created in Neo4j Desktop.
-
-====
-[NOTE]
-The current Neo4j Desktop API version is `1.4.0`.
-====
-
-=== manifest.json
-
-The `manifest.json` file is read during the installation process to gather additional information to Neo4j Desktop about your Graph App.
-In a packaged installation of a Graph App (either by manually creating a .tar.gz file or via `npm pack`), this file should be added to the `/dist` folder before packaging.
-For Graph Apps hosted on the internet, the manifest.json file should be served in the same directory as your `index.html` file.
-
-[source,bash]
-----
-{
-  "name": "my-graph-app",
-  "description": "(desktop)-[:LOVES]->(apps)",
-  "icons": [
-    {
-      "src": "./my-image.png",
-      "type": "png"
-    },
-    {
-      "src": "./my-vector-image.svg",
-      "type": "svg"
-    },
-    {
-      "src": "data:image/svg+xml;base64,[data]",
-      "type": "data"
-    }
-  ],
-  "homepage": "http://neo4j.com"
-}
-----
-
-The following image demonstrates how the values from manifest.json are used by Neo4j Desktop.
-
-image:{img}manifest.png[Manifest Mapping, title="How items from manifest.json are used in Neo4j Desktop"]
-
-.Table manifest.json Contents
-|===
-| key | description | example
-| name | The name of your application as used in the UI.
-If this doesn't exist, Neo4j Desktop will either use the name from package.json for packaged installs or the `` tag for hoated installs | My Graph App
-| shortName | A shorter name for your Graph App for use in the UI where space is short | MyApp
-| icons | An array of icons to be used in the UI.
-This can either be a relative path to an image or an inline data URI. | ```"icons": [
-    {
-      "src": "./my-image.png",
-      "type": "png"
-    }```
-
-| permissions | If your Graph App requires additional permissions, for example viewing all graphs, you can list them here | ```"permissions": ["backgroundProcess", "allGraphs", "activeGraph"]```
-|===
-
-Any values provided in `manifest.json` will override a value provided in `package.json`.
-For example, if `package.json` lists version `1.0.0` but `manifest.json` specifies `1.2.3`, the value `1.2.3` will be used.
-
-==== Release Notes
-
-If a `release-notes.md` file exists, the contents of the file will be displayed in Neo4j Desktop when the user is prompted to update the Graph App.
-This file should be in the same directory as the `package.json` file.
-
-[#deploy-graphapp]
-== Deploying your Graph App
-
-=== File Structure
-
-At the minimum, your project should consist of a `dist/` directory containing an index.html file plus any other JavaScript and CSS files that are required to run the Graph App.
-The root directory should also include a `package.json` file and optionally a set of latest release notes in `release-notes.md`.
-
-[source,bash]
-----
-dist/
-  app.js
-  index.html
-  manifest.json
-package.json
-release-notes.md
-----
-
-=== Deployment via .tar file
-
-The most common option for deploying a Graph App is to create a `.tar` file.
-This can be created by running the `npm pack` command.
-
-If you have created a project with a command line tool (for example `create-react-app` or `@vue/cli`) then the build scripts should be configured for you already.
-If not, you can create a build script in `package.json` to move the appropriate files to the `dist/` folder.
-
-Once the files are in the dist folder, you can run the npm pack to package the graph app into a `.tar` file.
-
-[source,bash]
-----
-npm pack
-----
-
-Any files or directories that you do not want to include in the `.tar` file can be listed in a `.npmignore` file.
-By default, the file will be named using the name and version properties from `package.json`.
-The resulting `.tar` file can be installed either by pasting a URL or dragging the tar file into the Install form at the bottom of the Graph Apps pane in Desktop.
-
-=== Deployment via npm
-
-Any https://docs.npmjs.com/cli/publish[published npm package^] can be installed by copying and pasting the npm registry URL.
-For example, the *Neo4j Cloud Tool* Graph App can be installed via Neo4j's npm registry with the URL https://neo.jfrog.io/neo/api/npm/npm/neo4j-cloud-ui.
-Neo4j Desktop will periodically check for updates to npm packages and install them automatically.
-
-More information on the `npm package` command is available on https://docs.npmjs.com/cli/publish[docs.npmjs.com].
-
-=== Online Deployments
-
-A good example of an Online Deployment is https://halin.graphapp.io[Halin^].
-You can install the hosted version of Halin by entering https://halin.graphapp.io into the *Install* form at the bottom of the Graph Apps pane and clicking the Install button.
-The hosted version of Halin hosts a http://halin.graphapp.io/manifest.json[manifest.json^] in the website's root directory.
-
-[#graphapp-other]
-== Additional Topics
-
-//=== Deep Links
-
-// neo4j://remote/add?url=foo&username=bar&name=baz
-
-=== Deep Links to your app
-
-You can provide a deep link to your app using the `neo4j-desktop://` scheme and the name of your app from package.json.
-For example, if the name of the app is `my-graph-app` the link would be the following:
-
-[source,bash]
-----
-neo4j-desktop://graphapps/my-graph-app?key=value
-----
-
-You can pass parameters through to the graph app to help set the initial state of the app.
-For example, in the URL above, the `?key=value` will append a `key` property with a value of `value` to the graph app's url.
-
-The `neo4jDesktopApi` has an `onArgumentsChange` function that allows you to listen to changes in the applications arguments, for example when a new deep link has been clicked.
-On load, and for each subsequent change of parameters, the callback function is called with two arguments; the original string and an object containing the decoded keys and values.
-
-[source,javascript]
-----
-neo4jDesktopApi.onArgumentsChange((queryString, object) => {
-  console.log(object.key) // "value"
-})
-----
-
-=== Deep links to Neo4j Browser
-
-Your graph app can also link to Neo4j Browser using the  `neo4j-desktop://` scheme and the Graph App name `neo4j-browser`.
-Additionally, you can specify a command and argument to automatically run as it loads.  For example, if you wanted to run the `:play movies` command to open the Movies link:/developer/guide-create-neo4j-browser-guide/[Browser Guide], you could use the following link:
-
-[source,bash]
-----
-neo4j-desktop://graphapps/neo4j-browser?cmd=play&arg=movies
-----
-
-You can also start Neo4j Browser with a pre-populated cypher query by setting `cmd` in the query string to edit and the `arg` to the Cypher query in a URL encoded form.
-
-[source,bash]
-----
-neo4j-desktop://graphapps/neo4j-browser?cmd=edit&arg=MATCH%20%28n%29%20RETURN%20count%28n%29%20AS%20count
-----
-
-=== Linking to Bloom
-
-You can link to Bloom by using the  `neo4j-desktop://` scheme, and the Graph App name `neo4j-bloom`.  You can also add a `search` parameter as a URL encoded string to auto-fill the search bar when bloom opens.
-
-[source,bash]
-----
-neo4j-desktop://graphapps/neo4j-bloom?search=URL%20Encoded%20String
-----
-
-=== Plugin Dependencies
-
-You can specify any plugins that your Graph App depends on within `manifest.json` file.
-Any plugin with a valid coordinate from https://search.maven.org/[Maven Central^] will be will be automatically installed to all local databases within the current active project in Neo4j Desktop.
-
-For example, if your Graph App requires link:/developer/neo4j-apoc/[APOC] then your `manifest.json` file may look something like this:
-
-[source,bash]
-----
-{
-  "name": "my-graph-app",
-  "pluginDependencies": [
-    "org.neo4j.procedure/apoc"
-  ]
-}
-----
-
-In order to specify your own plugins here, they must be https://maven.apache.org/repository/guide-central-repository-upload.html[published to Maven Central^].
-Once published, the coordinates of the Maven Artifact can be added to the array.
-
-=== Permissions
-
-If a Graph App requires the use of a privileged API (for example executing Java or Node.js), these will need to be specified either in the `neo4jDesktop` section of package.json or in `manifest.json`.
-Permissions can be defined as an array:
-
-[source,bash]
-----
-{
-  "name": "my-graph-app",
-  "permissions": ["backgroundProcess", "allGraphs", "activeGraph"]
-}
-----
-
-Or alternatively, a map-like object can be provided with a short description of how the permission will be used.
-
-[source,bash]
-----
-{
-  "name": "my-graph-app",
-  "permissions": [
-    "activeGraph",
-    {
-      "backgroundProcess": "Allow background processes to see output of demo Java class",
-      "allGraphs": "Another usage description here"
-    }
-  ]
-}
-----
-
-==== Permissions Available
-
-The following permissions are currently available:
-
-.Table Permissions Available
-|===
-| Permission |	Description
-| activationKeys | Provides access to activation keys registered to all Graph Apps
-| activeGraph |	Provides access to the active Graph data.
-This is a default permission granted on app install.
-| allGraphs	| Provides access to all the configured Graphs.
-| backgroundProcess |	Provides access to `executeJava` and `executeNode` API.
-|===
-
-==== Checking for Permission
-
-When your graph app is installed, the user will have the option to grant or deny a permission and these permissions can also be revoked at any time from the Graph Apps pane.
-Therefore, it is good practice to to check that the permission has been granted.
-To do so, you can call the `checkPermission` method on the injected API.
-
-[source,javascript]
-----
-window.neo4jDesktopApi.checkPermission("backgroundProcess")
-  .then(granted => {
-    if ( granted === true ) {
-      // Permission has been granted
-    }
-  });
-----
-
-==== Requesting Permission
-
-If your graph app doesn't already have the permission it needs, then it can be requested by calling the `requestPermission` method on the injected API.
-In order to request a permission, it must be listed in the graph app's `manifest.json` file.
-
-The following example features the longform version of the permission declaration, describing how the `backgroundProcess` permission will be used within the app.
-
-[source,javascript]
-----
-{
-  "name": "my-graph-app",
-  "permissions": {
-    "backgroundProcess": "Allow this Graph App to create a CSV file on your hard drive"
-  }
-}
-----
-
-The graph app can then request the permission.
-The user will be issued with a prompt which will allow them to Allow or Deny the permission to the Graph App.
-
-[source,javascript]
-----
-window.neo4jDesktopApi.requestPermission("backgroundProcess")
-  .then(granted => {
-    if (granted) {
-      // Permission has been granted
-    } else {
-      // The user has rejected the permission
-    }
-  });
-----
-
-// [#background-processes]
-// === Background Processes
-
-// There may be occasions where a Graph App may need to run a Background Process.
-// For example, the https://install.graphapp.io[Neo4j Cloud Tools^] app built by link:/labs[Neo4j Labs^] uses Java commands to run a backup of a local database and upload to the internet before running an install command on an link:/aura[AuraDB^] instance.
-// Background processes can be written in either Java or Node.js.
-
-// In order to run a background process, the appropriate `.jar` or `.js` file(s) must be
-
-// When testing your background processes in Development Mode, you must set the *Development App Root Path* to the dist or public folder where your `.jar` or `.js` files reside.
-// To run these scripts in production, these must be placed within the [TODO: `dist/` or root] folder.
-
-// ==== Java Processes
-
-// After building your project, the resulting `.jar` should be placed in the [TODO: `dist/` or root] folder.
-//The `executeJava` method can then be called to execute the jar file.
-
-// For example, say we have developed our own admin program that requires access to the filesystem.
-// The command accepts one argument, either `backup` or `restore` and expects two options to specify the username and password.
-// We've already built the project and placed a jar file called `admin.jar` in our [TODO: `dist/` or root].
-
-// The `executeJava` accepts an object as it's only parameter.
-
-// .Table executeJava Parameter Object
-// |===
-// | Name | Description | Example
-// | arguments | An array of arguments to pass to the jar execution | `['foo', 'bar']`
-// | options | An array of options to pass to the jar execution | `['-DmyProperty=value', '-Xdebug']`
-// | classpath | An array of paths to files that contain java classes or packages | `['/Users/adam/']`
-// | class | The class which should be executed within the classpath | `Main`
-// | jar | The path of the jar file that should be executed relative to the [TODO: `dist/` or root] directory | `./test.jar`
-// |===
-
-// To run our admin.jar file with the argument of backup and username and password options, we would first request te permission and if granted, call the `executeJava()` method.
-
-// [source,javascript]
-// ----
-// const parameters = {
-//   jar: './admin.jar',
-//   arguments: ['backup'],
-//   options: ['--username adam', '--password letmein'],
-// }
-
-// window.neo4jDesktopApi.requestPermission('backgroundProcess')
-//   .then(granted => {
-//       if (granted) {
-//           return window.neo4jDesktopApi.executeJava(parameters)
-//       } else {
-//           return Promise.reject('Execute permission denied.');
-//       }
-//   })
-// ----
-
-// To add directories relative to the current graph app into the class path, you can use the value stored in `neo4jDesktopApi.graphApp.rootPath`.
-
-// [source,javascript]
-// ----
-// const parameters = {
-//   classpath: [ `${neo4jDesktopApi.graphApp.rootPath}/dist/admin.jar` ],
-//   class: 'Main'
-// }
-// ----
-
-// https://github.com/neo4j-apps/graph-app-starter/blob/master/examples/basic-java-executor/index.html[A basic example can be viewed here].
-
-// ==== Node.js Processes
-
-// To run a node script, the appropriate `.js` file should be placed in the [TODO: `dist/` or root] folder.
-
-// For example, say we have developed a Node.js script that calls a URL to get the latest version of Neo4j Desktop and then uses `fs` to write the response to the filesystem in the Graph App's root folder to be read the next time the Graph App opens.  This file will be stored at `[TODO: dist/ or root]/getVersion.js`.
-
-// [source,javascript]
-// ----
-// const fs = require('fs')
-// const https = require('https')
-
-// https.get('https://dist.neo4j.org/neo4j-desktop/win/latest.yml', res => {
-//     let yml = '';
-
-//     // Write Data to YML
-//     res.on('data', chunk => yml += chunk)
-
-//     // When finished, create the file
-//     res.on('end', () => fs.writeFileSync('latest.yml', yml))
-// })
-// ----
-
-// The `executeNode()` method accepts three arguments, the path to the script that will be executed, an array of arguments and an optional object of options.
-// The options parameter should consist of `cwd`, the current working directory for the script to be executed in and `env`, an object of environment variables that can be accessed within the script via `process.env`.
-
-// [source,javascript]
-// ----
-// const filePath = './getVersion.js'
-// const args = []
-// const options = {
-//   cwd: neo4jDesktopApi.graphApp.rootPath, // Current working directory
-//   env: {}, // TODO: WTF are Env Options??
-// }
-
-// window.neo4jDesktopApi.requestPermission('backgroundProcess')
-//   .then(granted => {
-//       if (granted) {
-//           return window.neo4jDesktopApi.executeNode(filePath, args, options)
-//       } else {
-//           return Promise.reject('Execute permission denied.');
-//       }
-//   })
-// ----
-
-// ==== Checking the status of your process
-
-// Both the  `executeJava()` and `executeNode()` methods return a promise which resolve to a Process instance.
-// This can be used to check the status, attach event listeners or kill the process.
-
-// [source,javascript]
-// ----
-// const process = window.neo4jDesktopApi.executeJava(parameters)
-
-// // Attach a listener to stout
-// process.addOutListener(data => console.log('Output from background process:', data))
-
-// // Attach a listener for stderr
-// process.addErrListener(error => console.error('Error in background process:', error))
-
-// // Define code to execute when the process finishes
-// process.onExit(status => console.log('Process exited with status:', status))
-// ----
-
-// The status of a process can be checked at any point using the `.status()` method.
-// This returns a promise that will resolve to either `RUNNING`, `STOPPED`  or `KILLED`.
-
-// [source,javascript]
-// ----
-// const status = await process.status()
-// ----
-
-// A list of process ID's can be retrieved by calling the `getProcessTreeIds()` method.
-// This returns a promise that will resolve to an array of numbers.
-// [source,javascript]
-// ----
-// const processIds = await process.getProcessIds()
-// ----
-
-// If for any reason you would like to stop the process tree gracefully, then you can call the `.stop()` method.
-// This will kill all of the process ID's within the process tree.
-
-// [source,javascript]
-// ----
-// const isKilled = await process.stop()
-// ----
-
-=== Activation Keys
-
-Activation Keys can be used to unlock functionality within your Graph App.
-An Activation Key is a JET token, similar to a https://www.jwt.io[JWT^] token but with specific fields that are used to grant access to protected resources and premium functionality.
-All users are required to enter an Activation Key when they first download desktop.
-
-All keys are currently issued by Neo4j and are tied to the name from your `package.json` file.
-They hold the following keys:
-
-.Table Activation Key Fields
-|===
-| Key | Description
-| featureName | The name of the application.  This will match the name in your `package.json` file.
-| expirationDate | The expiration date for this activation key in ISO format (ISO 8601).
-| activationVersion | The version of the activation key.
-| featureVersion | A semver string denoting the version range that this key is valid for.
-| registrant  | The name of the current user that this key belongs to.
-| organization |  The company that the current user works for.
-| email | The email address of the current user.
-| scope | A string delimited list of features that this activation key grants access for.
-|===
-
-Activation Keys are held as part of the context.
-If you have requested the `activationKeys` setting then it may be appropriate to filter the activation keys by their feature name.
-
-[source,javascript]
-----
-const context = await neo4jDesktopApi.getContext()
-const activationKeys = context.activationKeys
-  .filter(key => key.featureName == "my-graph-app")
-----
-
-If you are interested in using Activation Keys to unlock features in your app, please https://community.neo4j.com/c/neo4j-graph-platform/graph-apps/95[get in touch^] and we will see what we can do.
-
-// [TODO: what happens when keys expire? can they be manually expired?]
-
-=== Files
-
-Neo4j Desktop allows you to drag and drop files into a project for later use.
-For example, you could create a link:/developer/neo4j-browser/#using-neo4j-browser[Browser Guide] to explain your project to your coworkers or create set of link:/developer/cypher-query-language/[Cypher scripts] to seed a new database within the project or to hold commonly run queries.
-The Neo4j Desktop UI displays a link to these files so they can be quickly opened in Neo4j Browser.
-
-You can also access these from your Graph App.
-For example, a visualisation app may take a set of cypher queries and display them in a link:/developer/tools-graph-visualization/[forced graph layout].
-Each file can be accessed via HTTP through Neo4j Desktop's API and therefore can be loaded through node's `http` module or a third party package like axios.
-
-.Table File
-|===
-| Key | type | description
-| contentType | string | The mime type of the file
-| filename | string | The name of the file
-| path | string | The name of the file
-| size | string | The name of the file
-| url | string | The URL to call to load the file
-|===
-
-To get all cypher files from every, you could run a `.map` and `.reduce` on Neo4j Desktop's context:
-
-[source,javascript]
-----
-const axios = require('axios')
-
-const context = await neo4jDesktopApi.getContext()
-const cypherFiles = context.projects.map(project =>
-    project.files.filter(file => file.filename.endsWith('.cypher'))
-  )
-  .reduce((files, projectFiles) => files.concat(projectFiles), [])
-
-axios.get(cypherFiles[0].url)
-  .then(response => console.log(response.data)) // MATCH (n) ...
-----
-
-[#framework-support]
-== Framework Support
-
-We do not recommend any specific front-end frameworks for developing apps.
-However, community members have built packages that will speed up your workflow.
-
-=== React
-
-The link:https://github.com/adam-cowley/use-neo4j[`use-neo4j`^] library provides hooks for running Cypher queries against a Neo4j Database.
-The `Neo4jProvider` component will detect the Neo4j Desktop API and show a list of Projects and Graphs configured in Neo4j Desktop.
-The library falls back to a generic login form which allows the user to enter their own credentials.
-
-[source,jsx]
-----
-ReactDOM.render(
-  <React.StrictMode>
-    <Neo4jProvider>
-      <App />
-    </Neo4jProvider>
-  </React.StrictMode>,
-  document.getElementById('root')
-);
-
-----
-
-==== Starter Kit
-
-You can fork or clone the link:https://github.com/adam-cowley/graphapp-starter-react[Graph App Starter kit for React^] to generate a basic Graph App.  The example project uses Semantic UI for styling and `use-neo4j` to interact with Neo4j.
-
-
-=== Vue.js
-
-The `vue-neo4j` plugin provides a wrapper for the JavaScript driver in all Vue.js components via `this.$neo4j` object.
-There is also a set of helper functions for developing Graph Apps.
-For more information, check out the Github repository for using https://github.com/adam-cowley/vue-neo4j[Vue.js with Neo4j^].
-
-[#neo4j-community]
-== Community Forum
-
-If you have any questions, comments, or would like to show off your own graph app, then there is a dedicated https://community.neo4j.com/c/neo4j-graph-platform/graph-apps/95[Graph Apps^] category on the https://community.neo4j.com/[Neo4j Community site^].
diff --git a/modules/graph-apps/pages/featured.adoc b/modules/graph-apps/pages/featured.adoc
deleted file mode 100644
index 29271a5a..00000000
--- a/modules/graph-apps/pages/featured.adoc
+++ /dev/null
@@ -1,119 +0,0 @@
-= Featured Graph Apps
-:page-pagination:
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-This page contains a list of featured Graph Apps built by link:/labs[Neo4j Labs^], link:/partners[Partners^] and the Neo4j Community. A comprehensive list of published graph apps for Neo4j is provided on our https://install.graphapp.io/[Graph App Gallery^].
-
-
-// == Developer Tools
-
-// * Neo4j Browser
-// * Bloom
-
-== Educational
-
-
-=== Graph Gallery
-
-[.graph-app]
-image:graph-gallery-logo.png[Graph Gallery Logo,width=48,role=rounded graph-app-logo]
-image:graph-gallery-screenshot.jpg[Graph Gallery Screenshot]
-
-If you are new to Graph Databases then the Graph Gallery is a good place to look for inspiration.
-The Graph Gallery features a curation of over a hundred example graphs categorised by use case and industry.
-Each entry includes a **Play as Browser Guide** button, allowing you to walk through the example step-by-step in xref:ROOT:neo4j-browser.adoc[Neo4j Browser].
-
-link:neo4j-desktop://graphapps/install?url=https://neo.jfrog.io/neo/api/npm/npm/graph-gallery[Install Graph Gallery in Neo4j Desktop,role=button]
-
-=== Graph Data Science Playground
-
-image:https://install.graphapp.io/images/neuler.png[Graph Data Science Playground Logo,width=48,float=right,role=rounded]
-The **Graph Data Science Playground** allows you to explore the Graph Algorithms available in the xref:graph-data-science:index.adoc[Neo4j Graph Data Science] library.
-The UI allows you to tweak configuration and preview the effects of each on your data set without writing any code.
-
-link:neo4j-desktop://graphapps/install?url=https://neo.jfrog.io/neo/api/npm/npm/neuler[Install Graph Data Science Playground in Neo4j Desktop,role=button]
-
-=== GraphQL Architect
-
-image:graphql-architect-logo.png[GraphQL Architect Logo,width=48,float=right,role=rounded]
-link:https://medium.com/neo4j/introducing-graphql-architect-19b0f2035e21[GraphQL Architect^] is a graph app for Neo4j Desktop that enables developers to build, query, and deploy GraphQL APIs backed by the Neo4j graph database, all from within Neo4j Desktop.
-
-link:neo4j-desktop://graphapps/install?url=https://registry.npmjs.org/@grandstack/graphql-architect[Install GraphQL Architect in Neo4j Desktop,role=button]
-
-
-=== Neosemantics
-
-image:https://install.graphapp.io/images/neosemantics-icon.png[Neosemantics Logo,width=48,float=right,role=rounded]
-The **Neosemantics Graph App** is a Graph App that will teach you to use the link:/labs/neosemantics[Neosemantics RDF toolkit for Neo4j] .
-Neosemantics integrates RDF and Linked Data with Neo4j.
-The Graph App guides you through the process of preparing your graph to import RDF data and allows you to create mappings from RDF schemas and preview the results before importing the data.
-
-link:neo4j-desktop://graphapps/install?url=https://registry.npmjs.org/@graphapps/neosemantics[Install the Neosemantics Graph App in Neo4j Desktop,role=button]
-
-
-== Montoring
-
-// tag::featured[]
-===  Halin
-
-[.graph-app]
-image:halin-logo.png[Halin Logo,role=rounded graph-app-logo]
-image:halin-screenshot.png[Halin Screenshot]
-
-Halin is a Graph App for monitoring Neo4j.  It shows you how your system is functioning and using system resources like CPU and Memory, and helps you diagnose common configuration and performance problems.
-
-// video::halin.mov[]
-
-link:neo4j-desktop://graphapps/install?url=https://neo.jfrog.io/neo/api/npm/npm/halin[Install Halin in Neo4j Desktop,role=button]
-// end::featured[]
-
-=== Query Log Analyser
-
-image:https://install.graphapp.io/images/qloganalyzer_icon.png[Query Log Analyser Logo,width=48,float=right,role=rounded]
-The **Query Log Analyser** is a tool that helps make sense of the link:https://neo4j.com/docs/operations-manual/current/monitoring/logging/query-logging/[query log file^] produced by a Neo4j Enterprise server.
-The Graph App scans through the log file, highlighting any long running queries and provides insights on how to improve them.
-
-link:neo4j-desktop://graphapps/install?url=https://neo.jfrog.io/neo/api/npm/npm/query-log-analyzer[Install Query Log Analyser in Neo4j Desktop, role=button]
-
-
-== Developer Tools
-
-=== Neo4j-ETL Tool
-
-image:https://install.graphapp.io/images/etl.png[ETL-Tool Logo,width=48,float=right,role=rounded]
-The link:/labs/etl-tool[Neo4j-ETL Tool^] provides a User Interface for importing data from relational databases into Neo4j.
-The link:/labs/etl-tool[Neo4j-ETL Tool^] makes this easy by inferring a graph model from the relational meta model that you can then adapt to fit your needs.
-Given that transformation, this tool also handles the actual import for you.
-
-link:neo4j-desktop://graphapps/install?url=https://neo.jfrog.io/neo/api/npm/npm/neo4j-etl-ui[Install ETL-Tool in Neo4j Desktop,role=button]
-
-=== Neo4j Cloud Tool
-
-image:https://install.graphapp.io/images/cloud-icon.png[Neo4j Cloud Tool Logo,width=48,float=right,role=rounded]
-The Neo4j Cloud Tool provides the functionality required to load your local Neo4j Instances into a xref:ROOT:aura-cloud-dbaas.adoc[Neo4j AuraDB] instance.  Neo4j AuraDB is a fully-managed cloud database service.
-
-link:neo4j-desktop://graphapps/install?url=https://neo.jfrog.io/neo/api/npm/npm/neo4j-cloud-ui[Install Neo4j Cloud Tool in Neo4j Desktop, role=button]
-
-// * Neo4j Db Analyzer
-
-
-== Visualisation
-
-=== NeoMap
-
-image:https://install.graphapp.io/images/neomap.png[NeoMap Logo,width=48,float=right,role=rounded]
-NeoMap is a React based Graph App built by Community Member link:https://twitter.com/st3llasia[Estelle Scifo^].
-NeoMap allows you to build up a map visualisation consisting of layers.
-Data points can be rendered on a map as markers, polylines, heatmaps or clusters.
-
-link:neo4j-desktop://graphapps/install?url=https://registry.npmjs.org/neomap[Install NeoMap in Neo4j Desktop,role=button]
-
-// * GraphXR
-// * yWorks Neo4j Explorer
-
-== Build Your Own
-
-If you are interested in building your own graph app, you can follow our xref:building-a-graph-app.adoc[Building Graph Apps Guide].
diff --git a/modules/graph-apps/pages/index.adoc b/modules/graph-apps/pages/index.adoc
deleted file mode 100644
index 37dcbd04..00000000
--- a/modules/graph-apps/pages/index.adoc
+++ /dev/null
@@ -1,130 +0,0 @@
-= Graph Applications Overview
-:level: Beginner
-:page-level: Beginner
-:author: Adam Cowley
-:category: desktop
-:tags: graph-apps, app-development, integrations, community, graphql, drivers, deployment
-:description: This guide provides an introduction to Graph Apps for Neo4j Desktop.
-:page-pagination: next
-:page-deprecated-title: the Getting Started Manual
-:page-deprecated-redirect: https://neo4j.com/docs/getting-started/current/
-
-// This page has been deprecated in favour of the Getting Started Guide, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-It helps if you have read the section on link:/developer/graph-database/[graph databases], as well as have downloaded and are familiar with link:/developer/neo4j-desktop/[Neo4j Desktop].
-
-[#what-are-graphapps]
-== What are Graph Apps?
-
-A Graph app is a Single Page Application (SPA) built with HTML and JavaScript which interact with Neo4j databases through link:/desktop/[Neo4j Desktop^].
-They can be developed by anyone - community members, partners, enterprises, and more - and are a convenient way of trying out ideas or building useful tools with Neo4j databases.
-A developer could take an existing SPA and package it into a graph app or start from scratch with a new idea.
-
-[#available-graphapps]
-== Available Graph Apps
-
-A full list of published graph apps for Neo4j is provided on our https://install.graphapp.io/[Graph App Gallery^].
-You can also open the Graph App Gallery within Neo4j Desktop by navigating to the **Graph Apps Pane** using the four square icon on the left hand menu.
-
-
-[.discrete.ad]
-== link:https://graphacademy.neo4j.com/?ref=guides[Learn with GraphAcademy^]
-
-image::https://graphacademy.neo4j.com/courses/app-typescript/badge/[float=left]
-
-[.discrete]
-=== link:https://graphacademy.neo4j.com/courses/app-typescript/?ref=guides[Building Neo4j Applications with TypeScript^]
-
-In this free course, we walk through the steps to integrate Neo4j into your Node.js projects.
-You will learn about the Neo4j JavaScript Driver, how sessions and transactions work and how to query Neo4j in a TypeScript application.
-
-link:https://graphacademy.neo4j.com/courses/app-typescript/?ref=guides[Learn more^,role=button]
-
-
-
-=== Installation
-
-Each Graph App in the https://install.graphapp.io/[Graph App Gallery^] has it's own installation link.
-Provided you have link:/desktop/[Neo4j Desktop^] installed, the Graph App Gallery will open a deep-link into Neo4j Desktop instructing it to download and install the Graph App.
-
-
-==== Installation from Package Managers & NPM
-
-You can instruct Neo4j Desktop to install a Graph App by using a deep link with the `neo4j-desktop://` scheme.  The URL to the Graph App should be passed as a URL parameter.  The full link is: `neo4j-desktop://graphapps/install?url=[URL to NPM style repository Remote Graph App]`.
-
-For example, to install GraphQL Architect, you could use the following URL:
-
-[source]
-neo4j-desktop://graphapps/install?url=https://registry.npmjs.org/@grandstack/graphql-architect
-
-
-==== Installing a Remote Graph App
-
-Any website can be installed as a Graph App, these are referred to as **Remote Graph Apps**.
-An example is link:/labs/halin[Halin^], which can either be installed as a standalone Graph App or as a Remote Graph App which is hosted remotely at link:https://halin.graphapp.io[halin.graphapp.io].
-
-To install a Remote Graph App, enter the URL of the Graph App into the **File or URL** input box at the bottom of the Graph Apps Pane.
-Once installed you should receive a confirmation message.
-
-// TODO: Video?
-
-
-
-== Launching a Graph App
-
-image:graph-apps-pane.png[Open the Graph Apps Pane using the Graph Apps icon, float=right]
-
-=== From the Graph App Pane
-
-All available Graph Apps are listed in the Graph Apps Pane located on the left hand menu in Neo4j Desktop.
-The Graph Apps are listed in alphabetical order.
-You can launch a Graph App by clicking the icon
-
-
-++++
-<div style="clear:both"></div>
-++++
-
-=== From the Action Bar
-image::launch-graph-app-action-bar.png[Launching a Graph app from the Action Bar]
-
-Graph Apps can also be launched from the Action Bar.
-As you type the name of the Graph App you wish to open, the list of suggestions will filter.
-You can use the arrow keys to select the appropriate entry from the list of suggestions and press enter to execute the command.
-
-[TIP]
-====
-The Action Bar can be opened by clicking the Magnifying Glass icon in the top right hand corner of the UI or using the keyboard shortcut: `Ctrl` + `K` on Windows or `Cmd` + `K` on Mac.
-====
-
-
-=== Uninstalling a Graph App
-
-To uninstall a Graph App, you can click the downward arrow under the Graph App name and clicking the **Delete App** button.
-You will be asked to confirm that you wish to delete the Graph App, click **Remove** to confirm.
-
-== Featured Graph App
-
-[discrete]
-include::featured.adoc[tags=featured]
-
-xref:featured.adoc[View more featured Graph Apps,width=120,role=more information]
-
-[#build-graphapp]
-== How Can I Build my own Graph App?
-
-We have a guide to step through the process of building your own graph app, so you can provide your own unique tool on top of Neo4j.
-We cannot wait to see what you build!
-
-xref:building-a-graph-app.adoc[Build a Graph Application]
-
-[#graphapp-community]
-== Graph App Topic on Neo4j Community
-
-If you have any questions, comments, or would like to show off your own Graph App then there is a dedicated https://community.neo4j.com/c/neo4j-graph-platform/graph-apps/95[Graph Apps^] category on the https://community.neo4j.com/[Neo4j Community site^].
diff --git a/modules/graph-apps/videos/halin.mov b/modules/graph-apps/videos/halin.mov
deleted file mode 100644
index 66b5df61..00000000
Binary files a/modules/graph-apps/videos/halin.mov and /dev/null differ
diff --git a/modules/graph-data-science/examples/link-prediction/linkpred-gds.cql b/modules/graph-data-science/examples/link-prediction/linkpred-gds.cql
deleted file mode 100644
index 9ea86557..00000000
--- a/modules/graph-data-science/examples/link-prediction/linkpred-gds.cql
+++ /dev/null
@@ -1,110 +0,0 @@
-CALL gds.alpha.degree.write('linkpred',{
-    writeProperty: 'degree'
-});
-
-CALL gds.graph.create(
-  'linkpred', {
-      Author: {
-          properties: ["degree"]
-      }
-  }, {
-    CO_AUTHOR: {
-      orientation: 'UNDIRECTED'
-    }
-  }
-);
-
-//3. Add test train splits to in-memory graph
-CALL gds.alpha.ml.splitRelationships.mutate('linkpred', {
-  relationshipTypes: ['CO_AUTHOR'],
-  remainingRelationshipType: 'CO_AUTHOR_REMAINING',
-  holdoutRelationshipType: 'CO_AUTHOR_TESTGRAPH',
-  holdoutFraction: 0.2
-}) YIELD relationshipsWritten;
-
-CALL gds.alpha.ml.splitRelationships.mutate('linkpred', {
-  relationshipTypes: ['CO_AUTHOR_REMAINING'],
-  remainingRelationshipType: 'CO_AUTHOR_IGNORED_FOR_TRAINING',
-  holdoutRelationshipType: 'CO_AUTHOR_TRAINGRAPH',
-  holdoutFraction: 0.2
-}) YIELD relationshipsWritten;
-
-// Feature Engineering
-
-CALL gds.pageRank.mutate('linkpred',{
-	  maxIterations: 20,
-    dampingFactor: 0.05,
-    relationshipTypes: ["CO_AUTHOR"],
-    mutateProperty: 'pagerank'
-});
-
-CALL gds.triangleCount.mutate('linkpred',{
-    relationshipTypes: ["CO_AUTHOR"],
-    mutateProperty: 'triangles'
-});
-
-CALL gds.beta.fastRPExtended.mutate('linkpred',{
-    propertyDimension: 45,
-    embeddingDimension: 250,
-    featureProperties: ["degree", "pagerank", "triangles"],
-    relationshipTypes: ["CO_AUTHOR_IGNORED_FOR_TRAINING"],
-    iterationWeights: [0, 0, 1.0, 1.0],
-    normalizationStrength:0.05,
-    mutateProperty: 'fastRP_Extended_Embedding'
-});
-
-CALL gds.fastRP.mutate('linkpred',{
-    embeddingDimension: 250,
-    relationshipTypes: ["CO_AUTHOR_IGNORED_FOR_TRAINING"],
-    iterationWeights: [0, 0, 1.0, 1.0],
-    normalizationStrength:0.05,
-    mutateProperty: 'fastRP_Embedding'
-});
-
-
-
-
-//4.  train a link prediction model
-CALL gds.alpha.ml.linkPrediction.train('linkpred', {
-  trainRelationshipType: 'CO_AUTHOR_TRAINGRAPH',
-  testRelationshipType: 'CO_AUTHOR_TESTGRAPH',
-  modelName: 'model',
-  featureProperties: ['fastRP_Extended_Embedding', 'pagerank'],
-  validationFolds: 5,
-  classRatio: 1.33,
-  randomSeed: 2,
-  params: [
-    {penalty: 0.24, maxIterations: 1000},
-    {penalty: 0.5, maxIterations: 1000},
-    {penalty: 1.0, maxIterations: 1000},
-    {penalty: 0.0, maxIterations: 1000}
-  ]
-}) YIELD modelInfo
-RETURN
-  modelInfo.bestParameters AS winningModel,
-  modelInfo.metrics.AUCPR.outerTrain AS trainGraphScore,
-  modelInfo.metrics.AUCPR.test AS testGraphScore
-
-
-CALL gds.beta.model.drop("model");
-
-// Using normal FastRP
-CALL gds.alpha.ml.linkPrediction.train('linkpred', {
-  trainRelationshipType: 'CO_AUTHOR_TRAINGRAPH',
-  testRelationshipType: 'CO_AUTHOR_TESTGRAPH',
-  modelName: 'model',
-  featureProperties: ['fastRP_Embedding', 'pagerank', 'degree'],
-  validationFolds: 5,
-  classRatio: 1.33,
-  randomSeed: 2,
-  params: [
-    {penalty: 0.24, maxIterations: 1000},
-    {penalty: 0.5, maxIterations: 1000},
-    {penalty: 1.0, maxIterations: 1000},
-    {penalty: 0.0, maxIterations: 1000}
-  ]
-}) YIELD modelInfo
-RETURN
-  modelInfo.bestParameters AS winningModel,
-  modelInfo.metrics.AUCPR.outerTrain AS trainGraphScore,
-  modelInfo.metrics.AUCPR.test AS testGraphScore
\ No newline at end of file
diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/autopilot_best_candidate.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/autopilot_best_candidate.csv
deleted file mode 100644
index 29e8ccfe..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/autopilot_best_candidate.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-name,metric,score
-tuning-job-1-4bfc366b2ee34835bf-004-639a788f,validation:f1,0.93334
diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/autopilot_candidates.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/autopilot_candidates.csv
deleted file mode 100644
index e68f4263..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/autopilot_candidates.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-name,score
-tuning-job-1-4bfc366b2ee34835bf-004-639a788f,0.93334
-tuning-job-1-4bfc366b2ee34835bf-003-ab9eb474,0.93217
-tuning-job-1-4bfc366b2ee34835bf-002-5652dea7,0.91883
-tuning-job-1-4bfc366b2ee34835bf-001-34fdcf78,0.861742
-tuning-job-1-4bfc366b2ee34835bf-005-0f5278e7,0.805774
diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under.csv
deleted file mode 100644
index 040668e0..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under.csv
+++ /dev/null
@@ -1,148257 +0,0 @@
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diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under_all.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under_all.csv
deleted file mode 100644
index 80e814b3..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under_all.csv
+++ /dev/null
@@ -1,148257 +0,0 @@
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-1.0,1.0,1,1.0,1,200310,200311,4.0,1.0,1.0,3.0,1
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-7.0,0.6071428571428571,105,0.115171650055371,17,156070,200400,344.0,0.0,0.0,44.0,1
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-7.0,0.6071428571428571,105,0.115171650055371,16,156070,200401,344.0,0.0,0.0,44.0,1
-5.0,0.6071428571428571,17,0.6071428571428571,16,200400,200401,64.0,1.0,1.0,11.0,1
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-1.0,1.0,4,0.1111111111111111,1,139650,200411,18.0,1.0,1.0,10.0,1
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-2.0,1.0,6,0.6,3,192179,200427,15.0,1.0,1.0,6.0,1
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-7.0,0.9642857142857144,34,0.7555555555555555,27,195610,200438,80.0,1.0,1.0,11.0,1
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-7.0,0.9642857142857144,34,0.7555555555555555,27,195609,200438,80.0,1.0,1.0,11.0,1
-7.0,0.9642857142857144,34,0.7555555555555555,27,195609,200439,80.0,1.0,1.0,11.0,1
-7.0,0.9642857142857144,106,0.3840579710144928,27,180234,200439,192.0,0.0,0.0,25.0,1
-7.0,0.9642857142857144,34,0.7555555555555555,27,195610,200439,80.0,1.0,1.0,11.0,1
-7.0,0.9642857142857144,33,0.6,27,195613,200439,88.0,1.0,1.0,12.0,1
-7.0,0.9642857142857144,27,0.4363636363636363,24,187829,200439,88.0,1.0,1.0,12.0,1
-7.0,0.9642857142857144,27,0.9642857142857144,27,200438,200439,64.0,1.0,1.0,9.0,1
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-5.0,1.0,15,1.0,15,200952,200955,36.0,1.0,1.0,7.0,1
-5.0,1.0,32,0.2352941176470588,15,19991,200955,102.0,1.0,0.0,18.0,1
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-1.0,1.0,1,1.0,1,200976,200977,4.0,1.0,1.0,3.0,1
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-2.0,1.0,3,1.0,3,200980,200981,9.0,1.0,1.0,4.0,1
-2.0,1.0,4,0.26666666666666666,3,122912,200981,18.0,1.0,1.0,7.0,1
-2.0,1.0,3,1.0,3,200980,200982,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,200981,200982,9.0,1.0,1.0,4.0,1
-2.0,1.0,4,0.26666666666666666,3,122912,200982,18.0,1.0,1.0,7.0,1
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-1.0,1.0,1,1.0,1,201003,201004,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,201003,201005,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,201004,201005,4.0,1.0,1.0,3.0,1
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-3.0,1.0,8,0.42857142857142855,6,161546,201018,28.0,1.0,1.0,8.0,1
-3.0,1.0,6,1.0,6,201017,201018,16.0,1.0,1.0,5.0,1
-3.0,1.0,20,0.12418300653594773,6,51165,201019,72.0,1.0,1.0,19.0,1
-3.0,1.0,6,1.0,6,201017,201019,16.0,1.0,1.0,5.0,1
-3.0,1.0,8,0.42857142857142855,6,161546,201019,28.0,1.0,1.0,8.0,1
-3.0,1.0,6,1.0,6,201018,201019,16.0,1.0,1.0,5.0,1
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-3.0,0.8333333333333334,5,0.8333333333333334,5,201023,201024,16.0,1.0,1.0,5.0,1
-3.0,0.8333333333333334,15,0.1176470588235294,5,20006,201024,68.0,1.0,1.0,18.0,1
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-5.0,1.0,15,1.0,15,201108,201110,36.0,1.0,1.0,7.0,1
-5.0,1.0,28,0.509090909090909,15,192136,201110,66.0,1.0,1.0,12.0,1
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-2.0,0.20915032679738566,29,0.0,0,170363,201263,54.0,1.0,1.0,19.0,1
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-2.0,0.6666666666666666,6,0.6,2,179587,201269,15.0,1.0,1.0,6.0,1
-8.0,0.8611111111111112,40,0.4871794871794872,33,200840,201270,117.0,1.0,1.0,14.0,1
-8.0,0.8611111111111112,43,0.5256410256410257,33,200838,201270,117.0,1.0,1.0,14.0,1
-8.0,0.8611111111111112,70,0.17666666666666667,33,28397,201270,225.0,0.0,1.0,26.0,1
-8.0,0.8611111111111112,342,0.5848739495798321,33,145348,201270,315.0,0.0,0.0,36.0,1
-8.0,0.8611111111111112,354,0.4332171893147503,33,150645,201270,378.0,0.0,0.0,43.0,1
-8.0,0.4871794871794872,57,0.29473684210526313,40,200840,201271,260.0,1.0,1.0,25.0,1
-9.0,0.3283996299722479,319,0.29473684210526313,57,150161,201271,940.0,0.0,0.0,58.0,1
-10.0,0.5848739495798321,342,0.29473684210526313,57,145348,201271,700.0,0.0,0.0,45.0,1
-2.0,0.29473684210526313,66,0.07308970099667775,57,170797,201271,860.0,0.0,0.0,61.0,1
-7.0,0.6666666666666666,57,0.29473684210526313,25,18851,201271,180.0,0.0,0.0,22.0,1
-8.0,0.29473684210526313,70,0.17666666666666667,57,28397,201271,500.0,0.0,1.0,37.0,1
-8.0,0.5256410256410257,57,0.29473684210526313,43,200838,201271,260.0,1.0,1.0,25.0,1
-10.0,0.4332171893147503,354,0.29473684210526313,57,150645,201271,840.0,0.0,0.0,52.0,1
-9.0,0.3288135593220339,563,0.29473684210526313,57,58366,201271,1200.0,0.0,0.0,71.0,1
-8.0,0.8611111111111112,57,0.29473684210526313,33,201270,201271,180.0,1.0,1.0,21.0,1
-8.0,0.29473684210526313,114,0.21212121212121213,57,145244,201271,660.0,0.0,0.0,45.0,1
-8.0,0.9444444444444444,40,0.4871794871794872,36,200840,201272,117.0,1.0,1.0,14.0,1
-8.0,0.9444444444444444,57,0.29473684210526313,36,201271,201272,180.0,1.0,1.0,21.0,1
-8.0,0.9444444444444444,70,0.17666666666666667,36,28397,201272,225.0,0.0,1.0,26.0,1
-8.0,0.9444444444444444,36,0.8611111111111112,33,201270,201272,81.0,1.0,1.0,10.0,1
-8.0,0.9444444444444444,43,0.5256410256410257,36,200838,201272,117.0,1.0,1.0,14.0,1
-8.0,0.9444444444444444,342,0.5848739495798321,36,145348,201272,315.0,0.0,0.0,36.0,1
-8.0,0.9444444444444444,354,0.4332171893147503,36,150645,201272,378.0,0.0,0.0,43.0,1
-14.0,0.33230926779313874,601,0.30484330484330485,108,150641,201273,1701.0,0.0,1.0,76.0,1
-9.0,0.4332171893147503,354,0.30484330484330485,108,150645,201273,1134.0,0.0,0.0,60.0,1
-9.0,0.5848739495798321,342,0.30484330484330485,108,145348,201273,945.0,0.0,0.0,53.0,1
-9.0,0.30484330484330485,108,0.29473684210526313,57,201271,201273,540.0,0.0,0.0,38.0,1
-9.0,0.30484330484330485,108,0.17666666666666667,70,28397,201273,675.0,0.0,0.0,43.0,1
-8.0,0.9444444444444444,108,0.30484330484330485,36,201272,201273,243.0,0.0,0.0,28.0,1
-13.0,0.30484330484330485,114,0.21212121212121213,108,145244,201273,891.0,1.0,1.0,47.0,1
-8.0,0.5256410256410257,108,0.30484330484330485,43,200838,201273,351.0,0.0,0.0,32.0,1
-8.0,0.4871794871794872,108,0.30484330484330485,40,200840,201273,351.0,0.0,0.0,32.0,1
-8.0,0.8611111111111112,108,0.30484330484330485,33,201270,201273,243.0,0.0,0.0,28.0,1
-8.0,0.7777777777777778,342,0.5848739495798321,37,145348,201274,350.0,0.0,0.0,37.0,1
-8.0,0.7777777777777778,57,0.29473684210526313,37,201271,201274,200.0,1.0,1.0,22.0,1
-8.0,0.7777777777777778,40,0.4871794871794872,37,200840,201274,130.0,1.0,1.0,15.0,1
-8.0,0.9444444444444444,37,0.7777777777777778,36,201272,201274,90.0,1.0,1.0,11.0,1
-8.0,0.7777777777777778,70,0.17666666666666667,37,28397,201274,250.0,0.0,1.0,27.0,1
-8.0,0.7777777777777778,43,0.5256410256410257,37,200838,201274,130.0,1.0,1.0,15.0,1
-8.0,0.7777777777777778,108,0.30484330484330485,37,201273,201274,270.0,0.0,0.0,29.0,1
-8.0,0.8611111111111112,37,0.7777777777777778,33,201270,201274,90.0,1.0,1.0,11.0,1
-7.0,0.8571428571428571,57,0.2065217391304348,24,184351,201275,192.0,1.0,1.0,25.0,1
-7.0,0.8571428571428571,30,0.5454545454545454,24,160912,201275,88.0,1.0,1.0,12.0,1
-7.0,0.8571428571428571,35,0.3626373626373626,24,151394,201275,112.0,1.0,1.0,15.0,1
-7.0,0.8571428571428571,28,0.509090909090909,24,37000,201275,88.0,1.0,1.0,12.0,1
-7.0,0.8571428571428571,285,0.15601503759398494,24,3075,201275,456.0,0.0,0.0,58.0,1
-7.0,1.0,285,0.15601503759398494,28,3075,201276,456.0,0.0,0.0,58.0,1
-7.0,1.0,35,0.3626373626373626,28,151394,201276,112.0,1.0,1.0,15.0,1
-7.0,1.0,28,0.509090909090909,28,37000,201276,88.0,1.0,1.0,12.0,1
-7.0,1.0,57,0.2065217391304348,28,184351,201276,192.0,1.0,1.0,25.0,1
-7.0,1.0,28,0.8571428571428571,24,201275,201276,64.0,1.0,1.0,9.0,1
-7.0,1.0,30,0.5454545454545454,28,160912,201276,88.0,1.0,1.0,12.0,1
-7.0,1.0,28,1.0,28,201276,201277,64.0,1.0,1.0,9.0,1
-7.0,1.0,28,0.509090909090909,28,37000,201277,88.0,1.0,1.0,12.0,1
-7.0,1.0,35,0.3626373626373626,28,151394,201277,112.0,1.0,1.0,15.0,1
-7.0,1.0,285,0.15601503759398494,28,3075,201277,456.0,0.0,0.0,58.0,1
-7.0,1.0,28,0.8571428571428571,24,201275,201277,64.0,1.0,1.0,9.0,1
-7.0,1.0,57,0.2065217391304348,28,184351,201277,192.0,1.0,1.0,25.0,1
-7.0,1.0,30,0.5454545454545454,28,160912,201277,88.0,1.0,1.0,12.0,1
-7.0,1.0,35,0.3626373626373626,28,151394,201278,112.0,1.0,1.0,15.0,1
-7.0,1.0,285,0.15601503759398494,28,3075,201278,456.0,0.0,0.0,58.0,1
-7.0,1.0,28,1.0,28,201276,201278,64.0,1.0,1.0,9.0,1
-7.0,1.0,28,0.509090909090909,28,37000,201278,88.0,1.0,1.0,12.0,1
-7.0,1.0,28,1.0,28,201277,201278,64.0,1.0,1.0,9.0,1
-7.0,1.0,30,0.5454545454545454,28,160912,201278,88.0,1.0,1.0,12.0,1
-7.0,1.0,57,0.2065217391304348,28,184351,201278,192.0,1.0,1.0,25.0,1
-7.0,1.0,28,0.8571428571428571,24,201275,201278,64.0,1.0,1.0,9.0,1
-3.0,0.8333333333333334,7,0.3333333333333333,5,191512,201287,28.0,1.0,1.0,8.0,1
-3.0,0.8333333333333334,5,0.3333333333333333,5,170493,201287,24.0,1.0,1.0,7.0,1
-3.0,0.8333333333333334,10,0.1282051282051282,5,180109,201287,52.0,1.0,1.0,14.0,1
-3.0,1.0,6,0.3333333333333333,5,170493,201288,24.0,1.0,1.0,7.0,1
-3.0,1.0,10,0.1282051282051282,6,180109,201288,52.0,1.0,1.0,14.0,1
-3.0,1.0,6,0.8333333333333334,5,201287,201288,16.0,1.0,1.0,5.0,1
-3.0,1.0,7,0.3333333333333333,6,191512,201288,28.0,1.0,1.0,8.0,1
-1.0,1.0,1,1.0,1,201289,201290,4.0,1.0,1.0,3.0,1
-1.0,1.0,2,0.3333333333333333,1,201290,201291,8.0,1.0,1.0,5.0,1
-1.0,1.0,2,0.3333333333333333,1,201289,201291,8.0,1.0,1.0,5.0,1
-1.0,0.3333333333333333,2,0.13333333333333333,2,96203,201291,24.0,0.0,1.0,9.0,1
-0.0,0.0,0,0.0,0,129471,201304,1.0,1.0,1.0,2.0,1
-2.0,0.6666666666666666,12,0.3611111111111111,2,196526,201315,27.0,1.0,1.0,10.0,1
-2.0,0.6666666666666666,41,0.05365853658536585,2,52252,201315,123.0,0.0,0.0,42.0,1
-2.0,0.6666666666666666,119,0.03442340791738382,2,1678,201315,252.0,0.0,1.0,85.0,1
-10.0,1.0,122,0.11980676328502415,54,28135,201316,506.0,1.0,1.0,47.0,1
-10.0,1.0,56,0.7307692307692307,54,112961,201316,143.0,1.0,1.0,14.0,1
-10.0,1.0,122,0.11980676328502415,55,28135,201317,506.0,1.0,1.0,47.0,1
-10.0,1.0,55,1.0,54,201316,201317,121.0,1.0,1.0,12.0,1
-10.0,1.0,56,0.7307692307692307,55,112961,201317,143.0,1.0,1.0,14.0,1
-10.0,1.0,55,1.0,55,201317,201318,121.0,1.0,1.0,12.0,1
-10.0,1.0,56,0.7307692307692307,55,112961,201318,143.0,1.0,1.0,14.0,1
-10.0,1.0,122,0.11980676328502415,55,28135,201318,506.0,1.0,1.0,47.0,1
-10.0,1.0,55,1.0,54,201316,201318,121.0,1.0,1.0,12.0,1
-10.0,1.0,122,0.11980676328502415,55,28135,201319,506.0,1.0,1.0,47.0,1
-10.0,1.0,55,1.0,55,201318,201319,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201317,201319,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,54,201316,201319,121.0,1.0,1.0,12.0,1
-10.0,1.0,56,0.7307692307692307,55,112961,201319,143.0,1.0,1.0,14.0,1
-10.0,1.0,122,0.11980676328502415,55,28135,201320,506.0,1.0,1.0,47.0,1
-10.0,1.0,56,0.7307692307692307,55,112961,201320,143.0,1.0,1.0,14.0,1
-10.0,1.0,55,1.0,55,201317,201320,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201319,201320,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201318,201320,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,54,201316,201320,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201317,201321,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201318,201321,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,54,201316,201321,121.0,1.0,1.0,12.0,1
-10.0,1.0,122,0.11980676328502415,55,28135,201321,506.0,1.0,1.0,47.0,1
-10.0,1.0,55,1.0,55,201319,201321,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201320,201321,121.0,1.0,1.0,12.0,1
-10.0,1.0,56,0.7307692307692307,55,112961,201321,143.0,1.0,1.0,14.0,1
-10.0,1.0,55,1.0,54,201316,201322,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201319,201322,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201318,201322,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201320,201322,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201317,201322,121.0,1.0,1.0,12.0,1
-10.0,1.0,56,0.7307692307692307,55,112961,201322,143.0,1.0,1.0,14.0,1
-10.0,1.0,55,1.0,55,201321,201322,121.0,1.0,1.0,12.0,1
-10.0,1.0,122,0.11980676328502415,55,28135,201322,506.0,1.0,1.0,47.0,1
-10.0,1.0,55,1.0,55,201322,201323,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201317,201323,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201318,201323,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201321,201323,121.0,1.0,1.0,12.0,1
-10.0,1.0,122,0.11980676328502415,55,28135,201323,506.0,1.0,1.0,47.0,1
-10.0,1.0,56,0.7307692307692307,55,112961,201323,143.0,1.0,1.0,14.0,1
-10.0,1.0,55,1.0,55,201319,201323,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,54,201316,201323,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201320,201323,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201322,201324,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201318,201324,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,54,201316,201324,121.0,1.0,1.0,12.0,1
-10.0,1.0,56,0.7307692307692307,55,112961,201324,143.0,1.0,1.0,14.0,1
-10.0,1.0,55,1.0,55,201323,201324,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201320,201324,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201319,201324,121.0,1.0,1.0,12.0,1
-10.0,1.0,122,0.11980676328502415,55,28135,201324,506.0,1.0,1.0,47.0,1
-10.0,1.0,55,1.0,55,201321,201324,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201317,201324,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201321,201325,121.0,1.0,1.0,12.0,1
-10.0,1.0,56,0.7307692307692307,55,112961,201325,143.0,1.0,1.0,14.0,1
-10.0,1.0,55,1.0,55,201322,201325,121.0,1.0,1.0,12.0,1
-10.0,1.0,122,0.11980676328502415,55,28135,201325,506.0,1.0,1.0,47.0,1
-10.0,1.0,55,1.0,55,201319,201325,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201318,201325,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201320,201325,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201324,201325,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201323,201325,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,54,201316,201325,121.0,1.0,1.0,12.0,1
-10.0,1.0,55,1.0,55,201317,201325,121.0,1.0,1.0,12.0,1
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diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under_basic_sample.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under_basic_sample.csv
deleted file mode 100644
index 9ef72821..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under_basic_sample.csv
+++ /dev/null
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diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under_sample.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under_sample.csv
deleted file mode 100644
index c209a9a5..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_test_under_sample.csv
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diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under.csv
deleted file mode 100644
index 4b560132..00000000
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diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under_all.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under_all.csv
deleted file mode 100644
index 231efacd..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under_all.csv
+++ /dev/null
@@ -1,162193 +0,0 @@
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-4.0,0.4642857142857143,13,0.3611111111111111,13,1871,1875,72.0,1.0,1.0,13.0,1
-4.0,1.0,10,1.0,10,1874,1876,25.0,1.0,1.0,6.0,1
-4.0,1.0,13,0.4642857142857143,10,1871,1876,40.0,1.0,1.0,9.0,1
-4.0,1.0,10,1.0,10,1873,1876,25.0,1.0,1.0,6.0,1
-4.0,1.0,13,0.3611111111111111,10,1875,1876,45.0,1.0,1.0,10.0,1
-4.0,1.0,10,1.0,10,1872,1876,25.0,1.0,1.0,6.0,1
-1.0,0.26666666666666666,4,0.16666666666666666,1,1877,1878,24.0,0.0,1.0,9.0,1
-1.0,0.5272727272727272,29,0.26666666666666666,4,1878,1879,66.0,0.0,1.0,16.0,1
-1.0,0.5272727272727272,29,0.16666666666666666,1,1877,1879,44.0,0.0,1.0,14.0,1
-1.0,1.0,15,0.2272727272727273,1,1880,1881,24.0,0.0,1.0,13.0,1
-4.0,0.7,15,0.2272727272727273,7,1881,1882,60.0,0.0,1.0,13.0,1
-1.0,1.0,7,0.7,1,1880,1882,10.0,1.0,1.0,6.0,1
-2.0,0.2545454545454545,351,0.1432712215320911,14,1385,1883,770.0,0.0,1.0,79.0,1
-3.0,0.6666666666666666,14,0.2545454545454545,4,1883,1884,44.0,1.0,1.0,12.0,1
-2.0,0.2888888888888889,351,0.1432712215320911,14,1385,1885,700.0,0.0,1.0,78.0,1
-2.0,0.6666666666666666,14,0.2888888888888889,4,1884,1885,40.0,1.0,1.0,12.0,1
-9.0,0.2888888888888889,14,0.2545454545454545,14,1883,1885,110.0,1.0,1.0,12.0,1
-2.0,1.0,14,0.2545454545454545,3,1883,1886,33.0,1.0,1.0,12.0,1
-2.0,1.0,4,0.6666666666666666,3,1884,1886,12.0,1.0,1.0,5.0,1
-2.0,1.0,14,0.2888888888888889,3,1885,1886,30.0,1.0,1.0,11.0,1
-5.0,0.13333333333333333,58,0.07084785133565621,18,1312,1892,672.0,0.0,1.0,53.0,1
-0.0,0.0,0,0.0,0,1893,1894,1.0,1.0,1.0,2.0,1
-1.0,0.4,4,0.16666666666666666,1,1902,1903,20.0,0.0,1.0,8.0,1
-0.0,0.0,0,0.0,0,1904,1905,2.0,1.0,1.0,3.0,1
-0.0,0.14285714285714285,2,0.0,0,1906,1907,7.0,1.0,1.0,8.0,1
-1.0,0.3333333333333333,14,0.2545454545454545,1,1908,1909,44.0,0.0,1.0,14.0,1
-2.0,0.3333333333333333,1,0.2,1,1908,1910,20.0,0.0,1.0,7.0,1
-1.0,0.2545454545454545,14,0.2,1,1909,1910,55.0,0.0,1.0,15.0,1
-2.0,1.0,3,1.0,3,1911,1912,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,0.3,3,1911,1913,15.0,1.0,1.0,6.0,1
-2.0,1.0,3,0.3,3,1912,1913,15.0,1.0,1.0,6.0,1
-2.0,1.0,3,1.0,3,1911,1914,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,1912,1914,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,0.3,3,1913,1914,15.0,1.0,1.0,6.0,1
-2.0,1.0,3,1.0,3,1919,1920,9.0,1.0,1.0,4.0,1
-2.0,1.0,11,0.3333333333333333,3,1919,1921,27.0,1.0,1.0,10.0,1
-2.0,1.0,11,0.3333333333333333,3,1920,1921,27.0,1.0,1.0,10.0,1
-2.0,0.3333333333333333,16,0.2272727272727273,11,1921,1922,108.0,0.0,1.0,19.0,1
-2.0,1.0,16,0.2272727272727273,3,1920,1922,36.0,0.0,1.0,13.0,1
-2.0,1.0,16,0.2272727272727273,3,1919,1922,36.0,0.0,1.0,13.0,1
-3.0,1.0,6,1.0,6,1923,1924,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,1923,1925,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,1924,1925,16.0,1.0,1.0,5.0,1
-3.0,1.0,12,0.5714285714285714,6,1925,1926,28.0,0.0,1.0,8.0,1
-3.0,1.0,12,0.5714285714285714,6,1923,1926,28.0,0.0,1.0,8.0,1
-3.0,1.0,12,0.5714285714285714,6,1924,1926,28.0,0.0,1.0,8.0,1
-3.0,1.0,12,0.5714285714285714,6,1923,1927,28.0,0.0,1.0,8.0,1
-6.0,0.5714285714285714,12,0.5714285714285714,12,1926,1927,49.0,1.0,1.0,8.0,1
-3.0,1.0,12,0.5714285714285714,6,1925,1927,28.0,0.0,1.0,8.0,1
-3.0,1.0,12,0.5714285714285714,6,1924,1927,28.0,0.0,1.0,8.0,1
-0.0,0.0,0,0.0,0,1938,1939,1.0,1.0,1.0,2.0,1
-3.0,0.6666666666666666,5,0.5,3,1506,1941,20.0,1.0,1.0,6.0,1
-1.0,0.6666666666666666,3,0.6666666666666666,2,1503,1941,12.0,1.0,1.0,6.0,1
-2.0,1.0,3,1.0,3,1943,1944,9.0,1.0,1.0,4.0,1
-2.0,1.0,6,0.4,3,1943,1945,18.0,1.0,1.0,7.0,1
-2.0,1.0,6,0.4,3,1944,1945,18.0,1.0,1.0,7.0,1
-2.0,1.0,30,0.2857142857142857,3,1943,1946,45.0,1.0,1.0,16.0,1
-5.0,0.4,30,0.2857142857142857,6,1945,1946,90.0,1.0,1.0,16.0,1
-2.0,1.0,30,0.2857142857142857,3,1944,1946,45.0,1.0,1.0,16.0,1
-7.0,1.0,28,1.0,28,1950,1951,64.0,1.0,1.0,9.0,1
-7.0,1.0,28,1.0,28,1951,1952,64.0,1.0,1.0,9.0,1
-7.0,1.0,28,1.0,28,1950,1952,64.0,1.0,1.0,9.0,1
-7.0,1.0,38,0.4175824175824176,28,1952,1953,112.0,1.0,1.0,15.0,1
-7.0,1.0,38,0.4175824175824176,28,1950,1953,112.0,1.0,1.0,15.0,1
-7.0,1.0,38,0.4175824175824176,28,1951,1953,112.0,1.0,1.0,15.0,1
-7.0,0.7777777777777778,38,0.4175824175824176,28,1953,1954,126.0,1.0,1.0,16.0,1
-7.0,1.0,28,0.7777777777777778,28,1951,1954,72.0,1.0,1.0,10.0,1
-7.0,1.0,28,0.7777777777777778,28,1952,1954,72.0,1.0,1.0,10.0,1
-7.0,1.0,28,0.7777777777777778,28,1950,1954,72.0,1.0,1.0,10.0,1
-7.0,1.0,37,0.4743589743589744,28,1952,1955,104.0,1.0,1.0,14.0,1
-7.0,1.0,37,0.4743589743589744,28,1950,1955,104.0,1.0,1.0,14.0,1
-7.0,1.0,37,0.4743589743589744,28,1951,1955,104.0,1.0,1.0,14.0,1
-7.0,0.4743589743589744,38,0.4175824175824176,37,1953,1955,182.0,1.0,1.0,20.0,1
-7.0,0.7777777777777778,37,0.4743589743589744,28,1954,1955,117.0,1.0,1.0,15.0,1
-7.0,1.0,37,0.4743589743589744,28,1951,1956,104.0,1.0,1.0,14.0,1
-7.0,0.4743589743589744,38,0.4175824175824176,37,1953,1956,182.0,1.0,1.0,20.0,1
-7.0,0.7777777777777778,37,0.4743589743589744,28,1954,1956,117.0,1.0,1.0,15.0,1
-7.0,0.4743589743589744,37,0.4743589743589744,37,1955,1956,169.0,1.0,1.0,19.0,1
-7.0,1.0,37,0.4743589743589744,28,1952,1956,104.0,1.0,1.0,14.0,1
-7.0,1.0,37,0.4743589743589744,28,1950,1956,104.0,1.0,1.0,14.0,1
-7.0,1.0,37,0.4743589743589744,28,1955,1957,104.0,1.0,1.0,14.0,1
-7.0,1.0,38,0.4175824175824176,28,1953,1957,112.0,1.0,1.0,15.0,1
-7.0,1.0,28,1.0,28,1950,1957,64.0,1.0,1.0,9.0,1
-7.0,1.0,37,0.4743589743589744,28,1956,1957,104.0,1.0,1.0,14.0,1
-7.0,1.0,28,1.0,28,1951,1957,64.0,1.0,1.0,9.0,1
-7.0,1.0,28,0.7777777777777778,28,1954,1957,72.0,1.0,1.0,10.0,1
-7.0,1.0,28,1.0,28,1952,1957,64.0,1.0,1.0,9.0,1
-7.0,1.0,38,0.4175824175824176,28,1953,1958,112.0,1.0,1.0,15.0,1
-7.0,1.0,28,0.7777777777777778,28,1954,1958,72.0,1.0,1.0,10.0,1
-7.0,1.0,37,0.4743589743589744,28,1956,1958,104.0,1.0,1.0,14.0,1
-7.0,1.0,28,1.0,28,1952,1958,64.0,1.0,1.0,9.0,1
-7.0,1.0,37,0.4743589743589744,28,1955,1958,104.0,1.0,1.0,14.0,1
-7.0,1.0,28,1.0,28,1950,1958,64.0,1.0,1.0,9.0,1
-7.0,1.0,28,1.0,28,1957,1958,64.0,1.0,1.0,9.0,1
-7.0,1.0,28,1.0,28,1951,1958,64.0,1.0,1.0,9.0,1
-0.0,0.2,9,0.0,0,1961,1962,10.0,1.0,1.0,11.0,1
-1.0,1.0,7,0.3333333333333333,1,1963,1964,14.0,0.0,1.0,8.0,1
-1.0,1.0,1,1.0,1,1963,1965,4.0,1.0,1.0,3.0,1
-1.0,1.0,7,0.3333333333333333,1,1964,1965,14.0,0.0,1.0,8.0,1
-1.0,1.0,1,1.0,1,1967,1968,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,1968,1969,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,1967,1969,4.0,1.0,1.0,3.0,1
-4.0,1.0,16,0.5714285714285714,10,1976,1977,40.0,1.0,1.0,9.0,1
-7.0,0.5714285714285714,26,0.2058823529411765,16,1977,1978,136.0,1.0,1.0,18.0,1
-4.0,1.0,26,0.2058823529411765,10,1976,1978,85.0,1.0,1.0,18.0,1
-4.0,1.0,16,0.5714285714285714,10,1977,1979,40.0,1.0,1.0,9.0,1
-4.0,1.0,10,1.0,10,1976,1979,25.0,1.0,1.0,6.0,1
-4.0,1.0,26,0.2058823529411765,10,1978,1979,85.0,1.0,1.0,18.0,1
-4.0,1.0,16,0.5714285714285714,10,1977,1980,40.0,1.0,1.0,9.0,1
-4.0,1.0,10,1.0,10,1979,1980,25.0,1.0,1.0,6.0,1
-4.0,1.0,26,0.2058823529411765,10,1978,1980,85.0,1.0,1.0,18.0,1
-4.0,1.0,10,1.0,10,1976,1980,25.0,1.0,1.0,6.0,1
-2.0,0.4,5,0.3333333333333333,4,1986,1987,30.0,0.0,1.0,9.0,1
-2.0,1.0,5,0.3333333333333333,3,1986,1988,18.0,0.0,1.0,7.0,1
-2.0,1.0,4,0.4,3,1987,1988,15.0,0.0,1.0,6.0,1
-2.0,1.0,5,0.2380952380952381,3,1988,1989,21.0,1.0,1.0,8.0,1
-4.0,0.3333333333333333,5,0.2380952380952381,5,1986,1989,42.0,0.0,1.0,9.0,1
-2.0,0.4,5,0.2380952380952381,4,1987,1989,35.0,0.0,1.0,10.0,1
-3.0,1.0,6,1.0,6,1994,1995,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,1995,1996,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,1994,1996,16.0,1.0,1.0,5.0,1
-3.0,1.0,7,0.7,6,1996,1997,20.0,1.0,1.0,6.0,1
-3.0,1.0,7,0.7,6,1994,1997,20.0,1.0,1.0,6.0,1
-3.0,1.0,7,0.7,6,1995,1997,20.0,1.0,1.0,6.0,1
-3.0,1.0,10,0.35714285714285715,6,1994,1998,32.0,1.0,1.0,9.0,1
-3.0,1.0,10,0.35714285714285715,6,1995,1998,32.0,1.0,1.0,9.0,1
-3.0,1.0,10,0.35714285714285715,6,1996,1998,32.0,1.0,1.0,9.0,1
-4.0,0.7,10,0.35714285714285715,7,1997,1998,40.0,1.0,1.0,9.0,1
-1.0,1.0,2,0.2,1,2000,2001,10.0,0.0,1.0,6.0,1
-1.0,1.0,1,1.0,1,2000,2002,4.0,1.0,1.0,3.0,1
-1.0,1.0,2,0.2,1,2001,2002,10.0,0.0,1.0,6.0,1
-1.0,0.17777777777777778,7,0.09090909090909093,6,2004,2005,110.0,0.0,1.0,20.0,1
-5.0,0.17777777777777778,10,0.10476190476190476,7,2004,2006,150.0,0.0,1.0,20.0,1
-5.0,0.10476190476190476,10,0.09090909090909093,6,2005,2006,165.0,1.0,1.0,21.0,1
-0.0,0.0,0,0.0,0,2010,2011,1.0,1.0,1.0,2.0,1
-1.0,1.0,17,0.4722222222222222,1,2012,2013,18.0,1.0,1.0,10.0,1
-1.0,1.0,7,0.7,1,2012,2014,10.0,1.0,1.0,6.0,1
-4.0,0.7,17,0.4722222222222222,7,2013,2014,45.0,1.0,1.0,10.0,1
-0.0,0.0,0,0.0,0,2015,2016,1.0,1.0,1.0,2.0,1
-1.0,1.0,5,0.2380952380952381,1,2018,2019,14.0,0.0,1.0,8.0,1
-1.0,1.0,1,1.0,1,2019,2020,4.0,1.0,1.0,3.0,1
-1.0,1.0,5,0.2380952380952381,1,2018,2020,14.0,0.0,1.0,8.0,1
-0.0,0.3333333333333333,1,0.0,0,2025,2026,3.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,2038,2039,9.0,1.0,1.0,4.0,1
-2.0,1.0,21,0.13450292397660818,3,2039,2040,57.0,0.0,1.0,20.0,1
-2.0,1.0,21,0.13450292397660818,3,2038,2040,57.0,0.0,1.0,20.0,1
-2.0,1.0,3,1.0,3,2039,2041,9.0,1.0,1.0,4.0,1
-2.0,1.0,21,0.13450292397660818,3,2040,2041,57.0,0.0,1.0,20.0,1
-2.0,1.0,3,1.0,3,2038,2041,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,2046,2047,9.0,1.0,1.0,4.0,1
-2.0,1.0,6,0.4,3,2047,2048,18.0,1.0,1.0,7.0,1
-2.0,1.0,6,0.4,3,2046,2048,18.0,1.0,1.0,7.0,1
-2.0,1.0,18,0.1978021978021978,3,2047,2049,42.0,1.0,1.0,15.0,1
-5.0,0.4,18,0.1978021978021978,6,2048,2049,84.0,1.0,1.0,15.0,1
-2.0,1.0,18,0.1978021978021978,3,2046,2049,42.0,1.0,1.0,15.0,1
-1.0,1.0,1,1.0,1,2050,2051,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,2051,2052,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,2050,2052,4.0,1.0,1.0,3.0,1
-0.0,0.0,0,0.0,0,2062,2063,1.0,1.0,1.0,2.0,1
-1.0,0.3333333333333333,1,0.16666666666666666,1,2066,2067,12.0,0.0,1.0,6.0,1
-1.0,0.16666666666666666,1,0.1,1,2066,2068,20.0,0.0,1.0,8.0,1
-2.0,0.3333333333333333,1,0.1,1,2067,2068,15.0,0.0,1.0,6.0,1
-0.0,0.21212121212121213,14,0.0,0,2073,2074,12.0,1.0,1.0,13.0,1
-2.0,1.0,3,1.0,3,2075,2076,9.0,1.0,1.0,4.0,1
-2.0,1.0,4,0.26666666666666666,3,2076,2077,18.0,0.0,1.0,7.0,1
-2.0,1.0,4,0.26666666666666666,3,2075,2077,18.0,0.0,1.0,7.0,1
-2.0,0.26666666666666666,5,0.2380952380952381,4,2077,2078,42.0,0.0,1.0,11.0,1
-2.0,1.0,5,0.2380952380952381,3,2076,2078,21.0,1.0,1.0,8.0,1
-2.0,1.0,5,0.2380952380952381,3,2075,2078,21.0,1.0,1.0,8.0,1
-0.0,0.13333333333333333,2,0.0,0,2079,2080,6.0,1.0,1.0,7.0,1
-1.0,0.1471861471861472,27,0.1111111111111111,4,1263,2083,220.0,0.0,1.0,31.0,1
-9.0,0.5555555555555556,30,0.25,24,2085,2086,160.0,1.0,1.0,17.0,1
-1.0,1.0,30,0.25,1,2085,2087,32.0,1.0,1.0,17.0,1
-1.0,1.0,24,0.5555555555555556,1,2086,2087,20.0,1.0,1.0,11.0,1
-11.0,0.5384615384615384,58,0.30526315789473685,49,1605,2093,280.0,1.0,1.0,23.0,1
-11.0,0.9848484848484848,65,0.30526315789473685,58,1604,2093,240.0,1.0,1.0,21.0,1
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-8.0,1.0,36,1.0,36,191968,191969,81.0,1.0,1.0,10.0,1
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-3.0,1.0,6,1.0,6,200318,200322,16.0,1.0,1.0,5.0,1
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-1.0,1.0,7,0.7,1,112109,200332,10.0,1.0,1.0,6.0,1
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-3.0,1.0,6,1.0,6,200337,200338,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200337,200339,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200338,200339,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,161469,200339,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200339,200340,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200337,200340,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,161469,200340,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200338,200340,16.0,1.0,1.0,5.0,1
-1.0,1.0,1,1.0,1,200343,200344,4.0,1.0,1.0,3.0,1
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-4.0,1.0,49,0.35294117647058826,8,118017,200360,90.0,1.0,1.0,19.0,1
-4.0,1.0,73,0.12063492063492065,8,1200,200360,180.0,1.0,1.0,37.0,1
-4.0,1.0,21,0.3047619047619048,8,84464,200360,75.0,1.0,1.0,16.0,1
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-2.0,1.0,3,0.3,3,200377,200378,15.0,1.0,1.0,6.0,1
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-2.0,1.0,3,1.0,3,200376,200379,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,200377,200379,9.0,1.0,1.0,4.0,1
-1.0,1.0,1,1.0,1,200380,200381,4.0,1.0,1.0,3.0,1
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-1.0,1.0,1,1.0,1,200380,200382,4.0,1.0,1.0,3.0,1
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-1.0,0.3333333333333333,1,0.3333333333333333,1,200384,200385,9.0,1.0,1.0,5.0,1
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-3.0,1.0,23,0.3484848484848485,5,84240,200387,48.0,1.0,1.0,13.0,1
-3.0,1.0,18,0.3818181818181817,5,28479,200388,44.0,0.0,1.0,12.0,1
-3.0,1.0,23,0.3484848484848485,5,84240,200388,48.0,1.0,1.0,13.0,1
-3.0,1.0,30,0.41025641025641024,5,84241,200388,52.0,1.0,1.0,14.0,1
-3.0,1.0,5,1.0,5,200387,200388,16.0,1.0,1.0,5.0,1
-3.0,0.8333333333333334,37,0.3523809523809524,6,78272,200390,60.0,0.0,1.0,16.0,1
-3.0,0.8333333333333334,12,0.21212121212121213,6,78271,200390,48.0,1.0,1.0,13.0,1
-3.0,0.8333333333333334,7,0.6,6,101600,200390,20.0,1.0,1.0,6.0,1
-3.0,0.8333333333333334,7,0.6,6,101600,200391,20.0,1.0,1.0,6.0,1
-3.0,0.8333333333333334,12,0.21212121212121213,6,78271,200391,48.0,1.0,1.0,13.0,1
-3.0,0.8333333333333334,37,0.3523809523809524,6,78272,200391,60.0,0.0,1.0,16.0,1
-3.0,0.8333333333333334,6,0.8333333333333334,6,200390,200391,16.0,1.0,1.0,5.0,1
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-1.0,1.0,1,1.0,1,200404,200405,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200404,200406,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200405,200406,4.0,1.0,1.0,3.0,1
-4.0,0.9,25,0.09956709956709957,9,36782,200408,110.0,0.0,0.0,23.0,1
-4.0,0.9,9,0.3611111111111111,9,65664,200408,45.0,1.0,1.0,10.0,1
-4.0,0.9,12,0.21818181818181814,9,19478,200408,55.0,1.0,1.0,12.0,1
-4.0,0.9,10,0.9,9,65665,200408,25.0,1.0,1.0,6.0,1
-4.0,1.0,12,0.21818181818181814,9,19478,200409,55.0,1.0,1.0,12.0,1
-4.0,1.0,9,0.3611111111111111,9,65664,200409,45.0,1.0,1.0,10.0,1
-4.0,1.0,10,0.9,9,65665,200409,25.0,1.0,1.0,6.0,1
-4.0,1.0,9,0.9,9,200408,200409,25.0,1.0,1.0,6.0,1
-4.0,1.0,25,0.09956709956709957,9,36782,200409,110.0,0.0,0.0,23.0,1
-0.0,0.0,0,0.0,0,64944,200413,2.0,1.0,1.0,3.0,1
-0.0,0.15384615384615385,13,0.0,0,58841,200414,13.0,1.0,1.0,14.0,1
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-2.0,0.6666666666666666,3,0.3333333333333333,3,96704,200418,9.0,1.0,1.0,4.0,1
-2.0,0.6666666666666666,4,0.2,3,77245,200418,15.0,1.0,1.0,6.0,1
-2.0,0.6666666666666666,3,0.3333333333333333,3,96704,200419,9.0,1.0,1.0,4.0,1
-2.0,0.6666666666666666,4,0.2,3,77245,200419,15.0,1.0,1.0,6.0,1
-2.0,0.6666666666666666,3,0.6666666666666666,3,200418,200419,9.0,1.0,1.0,4.0,1
-1.0,1.0,1,1.0,1,200420,200421,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200421,200422,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200420,200422,4.0,1.0,1.0,3.0,1
-4.0,0.9,49,0.0873440285204991,9,20681,200424,170.0,0.0,1.0,35.0,1
-4.0,0.9,13,0.2888888888888889,9,140148,200424,50.0,1.0,1.0,11.0,1
-4.0,0.9,9,0.32142857142857145,9,35432,200424,40.0,1.0,1.0,9.0,1
-4.0,1.0,13,0.2888888888888889,10,140148,200425,50.0,1.0,1.0,11.0,1
-4.0,1.0,10,0.9,9,200424,200425,25.0,1.0,1.0,6.0,1
-4.0,1.0,49,0.0873440285204991,10,20681,200425,170.0,0.0,1.0,35.0,1
-4.0,1.0,10,0.32142857142857145,9,35432,200425,40.0,1.0,1.0,9.0,1
-4.0,0.4722222222222222,17,0.32142857142857145,9,35432,200426,72.0,1.0,1.0,13.0,1
-5.0,0.4722222222222222,17,0.2888888888888889,13,140148,200426,90.0,1.0,1.0,14.0,1
-5.0,0.4722222222222222,17,0.20512820512820512,16,170501,200426,117.0,0.0,0.0,17.0,1
-3.0,0.4722222222222222,26,0.19117647058823528,17,139850,200426,153.0,0.0,0.0,23.0,1
-4.0,1.0,17,0.4722222222222222,10,200425,200426,45.0,1.0,1.0,10.0,1
-5.0,0.4722222222222222,49,0.0873440285204991,17,20681,200426,306.0,0.0,1.0,38.0,1
-4.0,0.9,17,0.4722222222222222,9,200424,200426,45.0,1.0,1.0,10.0,1
-2.0,1.0,6,0.4,3,124230,200430,18.0,1.0,1.0,7.0,1
-2.0,1.0,3,1.0,3,200430,200431,9.0,1.0,1.0,4.0,1
-2.0,1.0,6,0.4,3,124230,200431,18.0,1.0,1.0,7.0,1
-2.0,1.0,3,1.0,3,200430,200432,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,200431,200432,9.0,1.0,1.0,4.0,1
-2.0,1.0,6,0.4,3,124230,200432,18.0,1.0,1.0,7.0,1
-2.0,1.0,8,0.19696969696969696,2,2897,200434,36.0,0.0,1.0,13.0,1
-2.0,1.0,15,0.1,2,45235,200434,63.0,0.0,1.0,22.0,1
-1.0,0.1,1,0.0,0,140233,200436,10.0,1.0,1.0,6.0,1
-1.0,0.3333333333333333,1,0.0,0,140232,200436,6.0,1.0,1.0,4.0,1
-2.0,1.0,3,0.5,3,78502,200442,12.0,1.0,1.0,5.0,1
-2.0,1.0,3,1.0,3,118524,200442,9.0,1.0,1.0,4.0,1
-2.0,1.0,22,0.12280701754385966,3,11109,200442,57.0,0.0,1.0,20.0,1
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-1.0,0.4,4,0.3333333333333333,1,37256,200444,15.0,1.0,1.0,7.0,1
-2.0,0.3333333333333333,2,0.3333333333333333,2,18493,200450,16.0,1.0,1.0,6.0,1
-0.0,0.19444444444444445,5,0.0,0,180109,200456,9.0,1.0,1.0,10.0,1
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-2.0,1.0,8,0.17777777777777778,3,123958,200471,30.0,0.0,0.0,11.0,1
-2.0,1.0,3,0.2,3,37073,200471,18.0,1.0,0.0,7.0,1
-2.0,1.0,3,0.2,3,200470,200471,18.0,1.0,1.0,7.0,1
-1.0,1.0,1,0.3333333333333333,1,58616,200473,6.0,1.0,1.0,4.0,1
-1.0,1.0,1,1.0,1,200473,200474,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,0.3333333333333333,1,58616,200474,6.0,1.0,1.0,4.0,1
-1.0,1.0,3,0.19047619047619047,0,11927,200475,14.0,1.0,1.0,8.0,1
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-1.0,1.0,1,1.0,1,200476,200477,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200477,200478,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200476,200478,4.0,1.0,1.0,3.0,1
-1.0,1.0,5,0.2380952380952381,1,52582,200482,14.0,1.0,1.0,8.0,1
-1.0,1.0,5,0.5,1,83761,200482,10.0,0.0,1.0,6.0,1
-2.0,1.0,6,0.21428571428571427,3,155932,200484,24.0,1.0,1.0,9.0,1
-2.0,1.0,3,0.5,3,183863,200484,12.0,1.0,1.0,5.0,1
-2.0,1.0,3,1.0,3,200484,200485,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,0.5,3,183863,200485,12.0,1.0,1.0,5.0,1
-2.0,1.0,6,0.21428571428571427,3,155932,200485,24.0,1.0,1.0,9.0,1
-3.0,1.0,6,1.0,6,200486,200487,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200486,200488,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200487,200488,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200487,200489,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200486,200489,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200488,200489,16.0,1.0,1.0,5.0,1
-3.0,1.0,7,0.4666666666666667,6,200487,200490,24.0,1.0,1.0,7.0,1
-3.0,1.0,7,0.4666666666666667,6,200489,200490,24.0,1.0,1.0,7.0,1
-3.0,1.0,7,0.4666666666666667,6,200488,200490,24.0,1.0,1.0,7.0,1
-3.0,1.0,7,0.4666666666666667,6,200486,200490,24.0,1.0,1.0,7.0,1
-3.0,1.0,5,0.8333333333333334,4,200368,200496,16.0,1.0,1.0,5.0,1
-3.0,1.0,11,0.26666666666666666,5,150885,200496,40.0,1.0,1.0,11.0,1
-3.0,1.0,5,0.6,5,72173,200496,20.0,1.0,1.0,6.0,1
-3.0,1.0,9,0.16363636363636366,5,3350,200496,44.0,0.0,1.0,12.0,1
-2.0,1.0,12,0.11029411764705882,3,50852,200499,51.0,0.0,1.0,18.0,1
-2.0,1.0,3,1.0,3,200499,200500,9.0,1.0,1.0,4.0,1
-2.0,1.0,12,0.11029411764705882,3,50852,200500,51.0,0.0,1.0,18.0,1
-2.0,1.0,3,1.0,3,200499,200501,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,200500,200501,9.0,1.0,1.0,4.0,1
-2.0,1.0,12,0.11029411764705882,3,50852,200501,51.0,0.0,1.0,18.0,1
-0.0,0.25,7,0.0,0,19407,200504,8.0,1.0,1.0,9.0,1
-2.0,0.6666666666666666,11,0.047619047619047616,2,19738,200508,66.0,0.0,1.0,23.0,1
-1.0,0.6666666666666666,6,0.1388888888888889,2,130044,200508,27.0,0.0,1.0,11.0,1
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-0.0,0.2380952380952381,5,0.0,0,140094,200511,7.0,1.0,1.0,8.0,1
-3.0,1.0,5,0.19444444444444445,4,180109,200513,36.0,1.0,1.0,10.0,1
-3.0,1.0,11,0.3611111111111111,4,71088,200513,36.0,1.0,1.0,10.0,1
-3.0,1.0,11,0.4642857142857143,4,71087,200513,32.0,1.0,1.0,9.0,1
-3.0,1.0,11,0.4642857142857143,6,71087,200514,32.0,1.0,1.0,9.0,1
-3.0,1.0,11,0.3611111111111111,6,71088,200514,36.0,1.0,1.0,10.0,1
-3.0,1.0,6,1.0,4,200513,200514,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,0.19444444444444445,5,180109,200514,36.0,1.0,1.0,10.0,1
-0.0,0.0,0,0.0,0,29003,200515,1.0,1.0,1.0,2.0,1
-0.0,0.0,0,0.0,0,200516,200517,1.0,1.0,1.0,2.0,1
-0.0,0.0,0,0.0,0,144864,200518,1.0,1.0,1.0,2.0,1
-0.0,0.16666666666666666,12,0.0,0,35822,200523,13.0,1.0,1.0,14.0,1
-0.0,0.2857142857142857,6,0.0,0,52536,200524,7.0,1.0,1.0,8.0,1
-3.0,1.0,6,0.6,6,170100,200526,20.0,1.0,1.0,6.0,1
-3.0,1.0,6,1.0,6,200526,200527,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,0.6,6,170100,200527,20.0,1.0,1.0,6.0,1
-3.0,1.0,6,1.0,6,200527,200528,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,0.6,6,170100,200528,20.0,1.0,1.0,6.0,1
-3.0,1.0,6,1.0,6,200526,200528,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200526,200529,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,1.0,6,200527,200529,16.0,1.0,1.0,5.0,1
-3.0,1.0,6,0.6,6,170100,200529,20.0,1.0,1.0,6.0,1
-3.0,1.0,6,1.0,6,200528,200529,16.0,1.0,1.0,5.0,1
-1.0,1.0,1,1.0,1,175490,200530,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,175490,200531,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200530,200531,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200532,200533,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200533,200534,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200532,200534,4.0,1.0,1.0,3.0,1
-1.0,0.13333333333333333,8,0.0,0,166256,200537,20.0,1.0,1.0,11.0,1
-1.0,0.10909090909090907,6,0.0,0,2976,200537,22.0,0.0,1.0,12.0,1
-1.0,0.3333333333333333,2,0.0,1,78187,200540,6.0,1.0,1.0,4.0,1
-1.0,0.3333333333333333,2,0.0,1,78189,200540,6.0,1.0,1.0,4.0,1
-1.0,1.0,16,0.1523809523809524,1,44627,200543,30.0,0.0,1.0,16.0,1
-1.0,1.0,16,0.1523809523809524,1,44627,200544,30.0,0.0,1.0,16.0,1
-1.0,1.0,1,1.0,1,200543,200544,4.0,1.0,1.0,3.0,1
-1.0,1.0,13,0.2888888888888889,1,170413,200545,20.0,0.0,1.0,11.0,1
-1.0,1.0,13,0.2888888888888889,1,170413,200546,20.0,0.0,1.0,11.0,1
-1.0,1.0,1,1.0,1,200545,200546,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200548,200549,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200549,200550,4.0,1.0,1.0,3.0,1
-1.0,1.0,1,1.0,1,200548,200550,4.0,1.0,1.0,3.0,1
-2.0,1.0,9,0.12121212121212123,3,44425,200554,36.0,1.0,1.0,13.0,1
-2.0,1.0,3,0.5,3,90215,200554,12.0,1.0,1.0,5.0,1
-2.0,1.0,9,0.12121212121212123,3,44425,200555,36.0,1.0,1.0,13.0,1
-2.0,1.0,3,0.5,3,90215,200555,12.0,1.0,1.0,5.0,1
-2.0,1.0,3,1.0,3,200554,200555,9.0,1.0,1.0,4.0,1
-1.0,1.0,2,0.2,1,83646,200556,10.0,1.0,1.0,6.0,1
-1.0,1.0,2,0.2,1,59074,200556,12.0,0.0,1.0,7.0,1
-1.0,1.0,4,0.26666666666666666,1,161487,200560,12.0,1.0,1.0,7.0,1
-1.0,1.0,4,0.26666666666666666,1,161487,200561,12.0,1.0,1.0,7.0,1
-1.0,1.0,1,1.0,1,200560,200561,4.0,1.0,1.0,3.0,1
-2.0,1.0,6,0.17857142857142858,3,123676,200562,24.0,0.0,0.0,9.0,1
-2.0,1.0,4,0.26666666666666666,3,64795,200562,18.0,0.0,1.0,7.0,1
-2.0,1.0,3,1.0,3,200562,200563,9.0,1.0,1.0,4.0,1
-2.0,1.0,6,0.17857142857142858,3,123676,200563,24.0,0.0,0.0,9.0,1
-2.0,1.0,4,0.26666666666666666,3,64795,200563,18.0,0.0,1.0,7.0,1
-2.0,1.0,12,0.3333333333333333,3,72293,200564,27.0,1.0,1.0,10.0,1
-2.0,1.0,6,0.6,3,72292,200564,15.0,1.0,1.0,6.0,1
-2.0,1.0,3,1.0,3,200564,200565,9.0,1.0,1.0,4.0,1
-2.0,1.0,12,0.3333333333333333,3,72293,200565,27.0,1.0,1.0,10.0,1
-2.0,1.0,6,0.6,3,72292,200565,15.0,1.0,1.0,6.0,1
-2.0,0.6666666666666666,5,0.5,2,187916,200573,15.0,1.0,1.0,6.0,1
-2.0,0.6666666666666666,6,0.6,2,187915,200573,15.0,1.0,1.0,6.0,1
-2.0,0.6666666666666666,5,0.1111111111111111,2,19338,200573,30.0,1.0,1.0,11.0,1
-4.0,1.0,9,0.3928571428571429,8,10274,200578,40.0,1.0,1.0,9.0,1
-4.0,1.0,23,0.20833333333333331,8,44555,200578,80.0,0.0,0.0,17.0,1
-4.0,1.0,8,0.7333333333333333,7,107351,200578,30.0,1.0,1.0,7.0,1
-4.0,1.0,14,0.2727272727272727,8,107352,200578,55.0,1.0,0.0,12.0,1
-4.0,1.0,8,1.0,8,200578,200579,25.0,1.0,1.0,6.0,1
-4.0,1.0,9,0.3928571428571429,8,10274,200579,40.0,1.0,1.0,9.0,1
-4.0,1.0,8,0.7333333333333333,7,107351,200579,30.0,1.0,1.0,7.0,1
-4.0,1.0,23,0.20833333333333331,8,44555,200579,80.0,0.0,0.0,17.0,1
-4.0,1.0,14,0.2727272727272727,8,107352,200579,55.0,1.0,0.0,12.0,1
-2.0,1.0,4,0.26666666666666666,3,51493,200581,18.0,1.0,1.0,7.0,1
-2.0,1.0,3,1.0,3,101057,200581,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,64641,200581,9.0,1.0,1.0,4.0,1
-0.0,0.0873440285204991,49,0.0,0,20681,200582,34.0,1.0,1.0,35.0,1
-0.0,0.2857142857142857,8,0.0,0,112574,200587,8.0,1.0,1.0,9.0,1
-1.0,1.0,7,0.6,1,151276,200589,10.0,1.0,1.0,6.0,1
-1.0,1.0,44,0.24210526315789474,1,90463,200589,40.0,0.0,1.0,21.0,1
-2.0,0.6666666666666666,5,0.1111111111111111,2,10309,200596,27.0,0.0,1.0,10.0,1
-2.0,0.6666666666666666,3,0.14285714285714285,2,11949,200596,21.0,1.0,1.0,8.0,1
-2.0,0.6666666666666666,2,0.6666666666666666,2,2961,200596,9.0,1.0,1.0,4.0,1
-1.0,1.0,4,0.4,1,19377,200597,10.0,0.0,1.0,6.0,1
-1.0,1.0,1,1.0,1,200597,200598,4.0,1.0,1.0,3.0,1
-1.0,1.0,4,0.4,1,19377,200598,10.0,0.0,1.0,6.0,1
-4.0,0.7,20,0.19166666666666668,6,64859,200599,80.0,1.0,0.0,17.0,1
-1.0,0.7,9,0.2222222222222222,6,10711,200599,50.0,0.0,0.0,14.0,1
-3.0,0.7,9,0.18181818181818185,6,37115,200599,60.0,1.0,1.0,14.0,1
-3.0,0.7,6,0.4666666666666667,5,196163,200599,30.0,1.0,1.0,8.0,1
-3.0,0.7,13,0.08823529411764706,6,1264,200599,85.0,1.0,1.0,19.0,1
-1.0,1.0,7,0.4666666666666667,1,200490,200601,12.0,0.0,1.0,7.0,1
-1.0,1.0,7,0.4666666666666667,1,200490,200602,12.0,0.0,1.0,7.0,1
-1.0,1.0,1,1.0,1,200601,200602,4.0,1.0,1.0,3.0,1
-0.0,0.3333333333333333,7,0.3333333333333333,1,155845,200607,21.0,0.0,0.0,10.0,1
-1.0,1.0,20,0.12105263157894736,1,43602,200608,40.0,0.0,1.0,21.0,1
-1.0,1.0,10,0.7333333333333333,1,155850,200608,12.0,1.0,1.0,7.0,1
-2.0,1.0,17,0.3090909090909091,3,1779,200610,33.0,0.0,1.0,12.0,1
-2.0,1.0,3,1.0,3,200610,200611,9.0,1.0,1.0,4.0,1
-2.0,1.0,17,0.3090909090909091,3,1779,200611,33.0,0.0,1.0,12.0,1
-2.0,1.0,3,1.0,3,200610,200612,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,200611,200612,9.0,1.0,1.0,4.0,1
-2.0,1.0,17,0.3090909090909091,3,1779,200612,33.0,0.0,1.0,12.0,1
-4.0,1.0,10,1.0,10,200616,200617,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200617,200618,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200616,200618,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200617,200619,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200616,200619,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200618,200619,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200618,200620,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200616,200620,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200617,200620,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200619,200620,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200618,200621,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200619,200621,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200620,200621,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200616,200621,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200617,200621,25.0,1.0,1.0,6.0,1
-0.0,0.0,0,0.0,0,200624,200625,1.0,1.0,1.0,2.0,1
-1.0,1.0,5,0.2380952380952381,1,52214,200626,14.0,1.0,1.0,8.0,1
-0.0,0.0,0,0.0,0,37296,200627,2.0,1.0,1.0,3.0,1
-1.0,0.13725490196078433,23,0.0,0,44476,200631,36.0,1.0,0.0,19.0,1
-1.0,0.054878048780487805,45,0.0,0,10057,200631,82.0,0.0,1.0,42.0,1
-2.0,1.0,11,0.24444444444444444,3,77691,200633,30.0,1.0,1.0,11.0,1
-2.0,1.0,5,0.17857142857142858,3,140434,200633,24.0,1.0,1.0,9.0,1
-2.0,1.0,3,1.0,3,200633,200634,9.0,1.0,1.0,4.0,1
-2.0,1.0,5,0.17857142857142858,3,140434,200634,24.0,1.0,1.0,9.0,1
-2.0,1.0,11,0.24444444444444444,3,77691,200634,30.0,1.0,1.0,11.0,1
-2.0,1.0,3,0.5,3,139768,200639,12.0,1.0,1.0,5.0,1
-2.0,1.0,3,1.0,3,200639,200640,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,0.5,3,139768,200640,12.0,1.0,1.0,5.0,1
-2.0,1.0,3,1.0,3,200639,200641,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,0.5,3,139768,200641,12.0,1.0,1.0,5.0,1
-2.0,1.0,3,1.0,3,200640,200641,9.0,1.0,1.0,4.0,1
-0.0,0.1,1,0.0,0,191751,200644,5.0,1.0,1.0,6.0,1
-0.0,0.0,0,0.0,0,184346,200651,1.0,1.0,1.0,2.0,1
-0.0,0.3090909090909091,14,0.0,0,96869,200652,11.0,1.0,1.0,12.0,1
-0.0,0.3333333333333333,1,0.0,0,27424,200653,3.0,1.0,1.0,4.0,1
-4.0,0.9,20,0.3636363636363637,7,83491,200658,60.0,0.0,1.0,13.0,1
-4.0,0.9,13,0.6190476190476191,7,58285,200658,35.0,1.0,1.0,8.0,1
-4.0,0.9,20,0.28205128205128205,7,52068,200658,65.0,0.0,1.0,14.0,1
-4.0,0.9,53,0.15669515669515668,7,52067,200658,135.0,0.0,1.0,28.0,1
-4.0,0.9,11,0.5714285714285714,7,200658,200659,35.0,0.0,1.0,8.0,1
-4.0,0.5714285714285714,20,0.28205128205128205,11,52068,200659,91.0,0.0,1.0,16.0,1
-2.0,0.5714285714285714,11,0.3055555555555556,11,107822,200659,63.0,1.0,0.0,14.0,1
-4.0,0.5714285714285714,20,0.3636363636363637,11,83491,200659,84.0,0.0,1.0,15.0,1
-4.0,0.6190476190476191,13,0.5714285714285714,11,58285,200659,49.0,0.0,1.0,10.0,1
-6.0,0.5714285714285714,53,0.15669515669515668,11,52067,200659,189.0,0.0,1.0,28.0,1
-2.0,1.0,8,0.42857142857142855,2,118202,200661,21.0,0.0,0.0,8.0,1
-2.0,1.0,6,0.16666666666666666,2,106480,200661,27.0,0.0,0.0,10.0,1
-2.0,1.0,2,1.0,2,84944,200661,9.0,1.0,1.0,4.0,1
-3.0,1.0,16,0.37777777777777777,6,102365,200662,40.0,1.0,1.0,11.0,1
-3.0,1.0,32,0.16017316017316016,6,1356,200662,88.0,1.0,1.0,23.0,1
-5.0,0.7333333333333333,16,0.37777777777777777,11,102365,200663,60.0,1.0,1.0,11.0,1
-5.0,0.7333333333333333,32,0.16017316017316016,11,1356,200663,132.0,1.0,1.0,23.0,1
-3.0,1.0,11,0.7333333333333333,6,200662,200663,24.0,1.0,1.0,7.0,1
-3.0,1.0,32,0.16017316017316016,6,1356,200664,88.0,1.0,1.0,23.0,1
-3.0,1.0,6,1.0,6,200662,200664,16.0,1.0,1.0,5.0,1
-3.0,1.0,11,0.7333333333333333,6,200663,200664,24.0,1.0,1.0,7.0,1
-3.0,1.0,16,0.37777777777777777,6,102365,200664,40.0,1.0,1.0,11.0,1
-4.0,1.0,10,1.0,10,140460,200665,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200665,200666,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,140460,200666,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,140460,200667,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200665,200667,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200666,200667,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200667,200668,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200666,200668,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,140460,200668,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,200665,200668,25.0,1.0,1.0,6.0,1
-4.0,1.0,11,0.4761904761904762,10,200665,200669,35.0,1.0,1.0,8.0,1
-4.0,1.0,11,0.4761904761904762,10,200666,200669,35.0,1.0,1.0,8.0,1
-4.0,1.0,11,0.4761904761904762,10,200668,200669,35.0,1.0,1.0,8.0,1
-1.0,0.4761904761904762,11,0.3,4,78874,200669,35.0,0.0,0.0,11.0,1
-4.0,1.0,11,0.4761904761904762,10,200667,200669,35.0,1.0,1.0,8.0,1
-4.0,1.0,11,0.4761904761904762,10,140460,200669,35.0,1.0,1.0,8.0,1
-1.0,1.0,3,0.3,1,71594,200674,10.0,1.0,1.0,6.0,1
-1.0,1.0,3,0.2,1,20104,200674,12.0,0.0,1.0,7.0,1
-0.0,0.0,0,0.0,0,200679,200680,1.0,1.0,1.0,2.0,1
-1.0,1.0,24,0.04033613445378152,1,9859,200684,70.0,1.0,1.0,36.0,1
-1.0,1.0,7,0.7,1,179572,200684,10.0,1.0,1.0,6.0,1
-2.0,1.0,3,0.3,3,156242,200689,15.0,1.0,1.0,6.0,1
-2.0,1.0,68,0.13709677419354838,3,2497,200689,96.0,0.0,1.0,33.0,1
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-8.0,1.0,36,1.0,36,201001,201002,81.0,1.0,1.0,10.0,1
-1.0,1.0,5,0.2380952380952381,1,97071,201006,14.0,0.0,1.0,8.0,1
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-4.0,1.0,10,1.0,10,101541,201008,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201008,201009,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,101541,201009,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,101541,201010,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201009,201010,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201008,201010,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201008,201011,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,101541,201011,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201010,201011,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201009,201011,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,101541,201012,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201009,201012,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201008,201012,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201011,201012,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201010,201012,25.0,1.0,1.0,6.0,1
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-1.0,1.0,27,0.07407407407407407,1,27403,201020,54.0,0.0,1.0,28.0,1
-1.0,1.0,27,0.07407407407407407,1,27403,201021,54.0,0.0,1.0,28.0,1
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-2.0,1.0,3,1.0,3,201030,201032,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,201031,201032,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,201032,201033,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,201030,201033,9.0,1.0,1.0,4.0,1
-2.0,1.0,3,1.0,3,201031,201033,9.0,1.0,1.0,4.0,1
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-1.0,1.0,2,0.3333333333333333,1,145015,201036,8.0,1.0,1.0,5.0,1
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-2.0,1.0,3,1.0,3,201064,201066,9.0,1.0,1.0,4.0,1
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-3.0,1.0,8,0.8,6,66361,201085,20.0,1.0,1.0,6.0,1
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-2.0,1.0,3,1.0,3,201283,201285,9.0,1.0,1.0,4.0,1
-2.0,1.0,4,0.4,3,11608,201285,15.0,1.0,1.0,6.0,1
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-2.0,1.0,6,0.42857142857142855,3,45053,201294,21.0,1.0,1.0,8.0,1
-2.0,1.0,6,0.5333333333333333,3,201292,201294,18.0,1.0,1.0,7.0,1
-2.0,1.0,3,1.0,3,201293,201294,9.0,1.0,1.0,4.0,1
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-1.0,1.0,6,0.25,1,19672,201298,16.0,0.0,1.0,9.0,1
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-0.0,0.7142857142857143,20,0.0,0,201300,201301,8.0,1.0,1.0,9.0,1
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-14.0,1.0,106,0.7794117647058824,105,196208,201305,255.0,1.0,1.0,18.0,1
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-14.0,1.0,106,0.7794117647058824,105,58396,201305,255.0,1.0,1.0,18.0,1
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-1.0,1.0,1,1.0,1,201310,201311,4.0,1.0,1.0,3.0,1
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-1.0,1.0,1,1.0,1,201364,201365,4.0,1.0,1.0,3.0,1
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-4.0,1.0,10,1.0,10,201378,201382,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201380,201382,25.0,1.0,1.0,6.0,1
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-4.0,1.0,10,1.0,10,201379,201383,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201381,201383,25.0,1.0,1.0,6.0,1
-4.0,1.0,10,1.0,10,201378,201383,25.0,1.0,1.0,6.0,1
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diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under_basic_sample.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under_basic_sample.csv
deleted file mode 100644
index 3d7fabae..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under_basic_sample.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-node1,node2,label
-101053,101054,1
-35945,77989,1
-2657,2658,1
-213982,228457,0
-95994,71626,0
diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under_sample.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under_sample.csv
deleted file mode 100644
index a00be573..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/df_train_under_sample.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-cn,maxCoefficient,maxTriangles,minCoefficient,minTriangles,pa,sl,sp,tn,label
-4,1,10,1,10,25,1,1,6,1
-2,1,3,0.333333,2,12,1,1,5,1
-2,1,3,1,3,9,1,1,4,1
-0,1,10,1,3,15,0,1,8,0
-0,1,5,0.833333,1,8,0,1,6,0
diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/download/autopilot_best_candidate.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/download/autopilot_best_candidate.csv
deleted file mode 100644
index 8835ea34..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/download/autopilot_best_candidate.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-name,metric,score
-tuning-job-1-4c1fbf19caa24e2992-005-1281d468,validation:accuracy,0.96412
diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/download/autopilot_candidates.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/download/autopilot_candidates.csv
deleted file mode 100644
index b4789f2d..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/download/autopilot_candidates.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-name,score
-tuning-job-1-4c1fbf19caa24e2992-005-1281d468,0.96412
-tuning-job-1-4c1fbf19caa24e2992-003-4e9f7b9f,0.96134
-tuning-job-1-4c1fbf19caa24e2992-002-0dbfe572,0.95656
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-tuning-job-1-4c1fbf19caa24e2992-004-dfa3c8ea,0.819107
diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/download/inference_results.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/download/inference_results.csv
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index 20016de4..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/download/inference_results.csv
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diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/model-eval.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/model-eval.csv
deleted file mode 100644
index 0fc7918a..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/model-eval.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-Measure,Score
-Accuracy,0.9636844377293331
-Precision,0.9607506702412869
-Recall,0.9668681200086338
diff --git a/modules/graph-data-science/examples/link-prediction/notebooks/data/sagemaker-model-eval.csv b/modules/graph-data-science/examples/link-prediction/notebooks/data/sagemaker-model-eval.csv
deleted file mode 100644
index 8862c9e2..00000000
--- a/modules/graph-data-science/examples/link-prediction/notebooks/data/sagemaker-model-eval.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-Measure,Score
-Accuracy,0.9648176127778977
-Precision,0.9643994172242607
-Recall,0.9652067075311209
diff --git a/modules/graph-data-science/examples/link-prediction/py/00_Environment.py b/modules/graph-data-science/examples/link-prediction/py/00_Environment.py
deleted file mode 100644
index d1fc4fa3..00000000
--- a/modules/graph-data-science/examples/link-prediction/py/00_Environment.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.5.2
-#   kernelspec:
-#     display_name: Python 3
-#     language: python
-#     name: python3
-# ---
-
-# # Setting up your environment
-#
-# In this notebook we're going setup the environment for the course.
-#
-# ## Libraries
-#
-# First let's install some libraries that we'll be using.
-
-# tag::pip-install[]
-# !pip install neo4j pandas matplotlib sklearn
-# end::pip-install[]
-
-# We'll start by importing py2neo library which we'll use to import the data into Neo4j. py2neo is a client library and toolkit for working with Neo4j from within Python applications. It is well suited for Data Science workflows and has great integration with other Python Data Science tools.
-
-from neo4j import GraphDatabase
-
-driver = GraphDatabase.driver("bolt://link-prediction-neo4j", auth=("neo4j", "admin"))        
-print(driver.address)
-
-# If that works fine, and no exceptions are thrown, we're ready to continue with the rest of the course!
-#
-# **Keep this notebook open as we'll need to copy the credentials into the other notebooks!**
diff --git a/modules/graph-data-science/examples/link-prediction/py/01_DataLoading.py b/modules/graph-data-science/examples/link-prediction/py/01_DataLoading.py
deleted file mode 100644
index d8c2e721..00000000
--- a/modules/graph-data-science/examples/link-prediction/py/01_DataLoading.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.5.2
-#   kernelspec:
-#     display_name: Python 3
-#     language: python
-#     name: python3
-# ---
-
-# # Citation Dataset Loading
-#
-# In this notebook we're going to load the citation dataset into Neo4j.
-#
-# First let's import a couple of Python libraries that will help us with this process.
-
-# We'll start by importing py2neo library which we'll use to import the data into Neo4j. py2neo is a client library and toolkit for working with Neo4j from within Python applications. It is well suited for Data Science workflows and has great integration with other Python Data Science tools.
-
-from neo4j import GraphDatabase
-
-driver = GraphDatabase.driver("bolt://link-prediction-neo4j", auth=("neo4j", "admin"))        
-print(driver.address)
-
-# ## Create Constraints
-#
-# First let's create some constraints to make sure we don't import duplicate data:
-
-with driver.session(database="neo4j") as session:
-    display(session.run("CREATE CONSTRAINT ON (a:Article) ASSERT a.index IS UNIQUE").consume().counters)
-    display(session.run("CREATE CONSTRAINT ON (a:Author) ASSERT a.name IS UNIQUE").consume().counters)
-    display(session.run("CREATE CONSTRAINT ON (v:Venue) ASSERT v.name IS UNIQUE").consume().counters)
-
-# ## Loading the data
-#
-# Now let's load the data into the database. We'll create nodes for Articles, Venues, and Authors.
-#
-
-# +
-query = """
-CALL apoc.periodic.iterate(
-  'UNWIND ["dblp-ref-0.json", "dblp-ref-1.json", "dblp-ref-2.json", "dblp-ref-3.json"] AS file
-   CALL apoc.load.json("https://github.com/mneedham/link-prediction/raw/master/data/" + file)
-   YIELD value WITH value
-   return value',
-  'MERGE (a:Article {index:value.id})
-   SET a += apoc.map.clean(value,["id","authors","references", "venue"],[0])
-   WITH a, value.authors as authors, value.references AS citations, value.venue AS venue
-   MERGE (v:Venue {name: venue})
-   MERGE (a)-[:VENUE]->(v)
-   FOREACH(author in authors | 
-     MERGE (b:Author{name:author})
-     MERGE (a)-[:AUTHOR]->(b))
-   FOREACH(citation in citations | 
-     MERGE (cited:Article {index:citation})
-     MERGE (a)-[:CITED]->(cited))', 
-   {batchSize: 1000, iterateList: true});
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-    for row in result:
-        print(row)
-
-# +
-query = """
-MATCH (a:Article) 
-WHERE not(exists(a.title))
-DETACH DELETE a
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-    print(result.consume().counters)
-# -
-
-# In the next notebook we'll explore the data that we've imported. 
diff --git a/modules/graph-data-science/examples/link-prediction/py/02_Co-Author_Graph.py b/modules/graph-data-science/examples/link-prediction/py/02_Co-Author_Graph.py
deleted file mode 100644
index 42ab778a..00000000
--- a/modules/graph-data-science/examples/link-prediction/py/02_Co-Author_Graph.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     formats: py:light,ipynb
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.5.2
-#   kernelspec:
-#     display_name: Python 3
-#     language: python
-#     name: python3
-# ---
-
-# # Building a co-author graph
-#
-# In this notebook we're going to build an inferred graph of co-authors based on people collaborating on the same papers. We're also going to store a property on the relationship indicating the year of their first collaboration.
-
-# tag::imports[]
-from neo4j import GraphDatabase
-# end::imports[]
-
-# +
-# tag::driver[]
-bolt_uri = "bolt://link-prediction-neo4j"
-driver = GraphDatabase.driver(bolt_uri, auth=("neo4j", "admin"))
-# end::driver[]
-
-print(driver.address)
-# -
-
-# We can create the co-author graph by running the query below to do this:
-
-# +
-# tag::data-import[]
-query = """
-CALL apoc.periodic.iterate(
-  "MATCH (a1)<-[:AUTHOR]-(paper)-[:AUTHOR]->(a2:Author)
-   WITH a1, a2, paper
-   ORDER BY a1, paper.year
-   RETURN a1, a2, collect(paper)[0].year AS year, count(*) AS collaborations",
-  "MERGE (a1)-[coauthor:CO_AUTHOR {year: year}]-(a2)
-   SET coauthor.collaborations = collaborations",
-  {batchSize: 100})
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-# end::data-import[]
-    for row in result:
-        print(row)
-# -
-
-# Now that we've created our co-author graph, we want to come up with an approach that will allow us to predict future links (relationships) that will be created between people.
diff --git a/modules/graph-data-science/examples/link-prediction/py/03_Train_Test_Split.py b/modules/graph-data-science/examples/link-prediction/py/03_Train_Test_Split.py
deleted file mode 100644
index abf2e8cc..00000000
--- a/modules/graph-data-science/examples/link-prediction/py/03_Train_Test_Split.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# -*- coding: utf-8 -*-
-# ---
-# jupyter:
-#   jupytext:
-#     formats: py:light,ipynb
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.5.2
-#   kernelspec:
-#     display_name: Python 3
-#     language: python
-#     name: python3
-# ---
-
-# # Train and test datasets
-#
-# Now that we've decided we're going to use a machine learning approach, we need to come up with train and test datasets on which we can build, and then evaluate a model.
-#
-# ### Positive examples
-#
-# The tricky thing when working with graph data is that we can't just randomly split the data, as this could lead to data leakage.
-#
-# Data leakage can occur when data outside of your training data is inadvertently used to create your model. This can easily happen when working with graphs because pairs of nodes in our training set may be connected to those in the test set.
-#
-# When we compute link prediction measures over that training set the __measures computed contain information from the test set__ that we’ll later evaluate our model against.
-#
-# Instead we need to split our graph into training and test sub graphs. If our graph has a concept of time our life is easy — we can split the graph at a point in time and the training set will be from before the time, the test set after.
-#
-# This is still not a perfect solution and we’ll need to try and ensure that the general network structure in the training and test sub graphs is similar.
-#
-# Once we’ve done that we’ll have pairs of nodes in our train and test set that have relationships between them. They will be the __positive examples__ in our machine learning model.
-#
-# We are lucky that our citation graph contains a times. We can create train and test graphs by splitting the data on a particular year. Now we need to figure out what year that should be. Let's have a look at the distribution of the first year that co-authors collaborated:
-
-# +
-from neo4j import GraphDatabase
-
-# tag::imports[]
-import pandas as pd
-import matplotlib
-import matplotlib.pyplot as plt
-
-plt.style.use('fivethirtyeight')
-pd.set_option('display.float_format', lambda x: '%.3f' % x)
-# end::imports[]
-
-# +
-bolt_uri = "bolt://link-prediction-neo4j"
-driver = GraphDatabase.driver(bolt_uri, auth=("neo4j", "admin"))
-
-print(driver.address)
-# -
-
-# We can create the co-author graph by running the query below to do this:
-
-# +
-# tag::determine-split[]
-query = """
-MATCH p=()-[r:CO_AUTHOR]->()
-WITH r.year AS year, count(*) AS count
-ORDER BY year
-RETURN toString(year) AS year, count
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-    by_year = pd.DataFrame([dict(record) for record in result])
-
-ax = by_year.plot(kind='bar', x='year', y='count', legend=None, figsize=(15,8))
-ax.xaxis.set_label_text("")
-plt.tight_layout()
-
-plt.show()
-# end::determine-split[]
-# -
-
-# It looks like 2006 would act as a good year on which to split the data. We'll take all the co-authorships from 2005 and earlier as our train graph, and everything from 2006 onwards as the test graph.
-#
-# Let's create explicit `CO_AUTHOR_EARLY` and `CO_AUTHOR_LATE` relationships in our graph based on that year. The following code will create these relationships for us:
-
-# +
-# tag::sub-graphs[]
-query = """
-MATCH (a)-[r:CO_AUTHOR]->(b)
-where r.year < 2006
-MERGE (a)-[:CO_AUTHOR_EARLY {year: r.year}]-(b);
-"""
-
-with driver.session(database="neo4j") as session:
-    display(session.run(query).consume().counters)
-
-query = """
-MATCH (a)-[r:CO_AUTHOR]->(b)
-where r.year >= 2006
-MERGE (a)-[:CO_AUTHOR_LATE {year: r.year}]-(b);
-"""
-
-with driver.session(database="neo4j") as session:
-    display(session.run(query).consume().counters)
-# end::sub-graphs[]
-# -
-
-# Let's quickly check how many co-author relationship we have in each of these sub graphs:
-
-# +
-query = """
-MATCH ()-[:CO_AUTHOR_EARLY]->()
-RETURN count(*) AS count
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-    df = pd.DataFrame([dict(record) for record in result])
-df
-
-# +
-query = """
-MATCH ()-[:CO_AUTHOR_LATE]->()
-RETURN count(*) AS count
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-    df = pd.DataFrame([dict(record) for record in result])
-df
-# -
-
-# We have a split of 52-48, which is a bit on the high side, but should be ok. Now for the __negative examples__.
-
-# ### Negative examples
-#
-# The simplest approach would be to use all pair of nodes that don’t have a relationship. __The problem with this approach is that there are significantly more examples of pairs of nodes that don’t have a relationship than there are pairs of nodes that do__.
-#
-# The maximum number of negative examples is equal to:
-#
-# ```
-# # negative examples = (# nodes)² - (# relationships) - (# nodes)
-# ```
-#
-# i.e. the number of nodes squared, minus the relationships that the graph has, minus self relationships.
-#
-# If we use all of these negative examples in our training set we will have a massive class imbalance — there are many negative examples and relatively few positive ones.
-#
-# A model trained using data that’s this imbalanced will achieve very high accuracy by __predicting that any pair of nodes don’t have a relationship__ between them, which is not quite what we want!
-#
-# So we need to try and reduce the number of negative examples. An approach described in several link prediction papers is to use pairs of nodes that are a __specific number of hops away from each other__.
-#
-# This will significantly reduce the number of negative examples, although there will still be a lot more negative examples than positive.
-#
-# To solve this problem we either need to down sample the negative examples or up sample the positive examples.
-#
-# We're going to take the down sampling approach in this guide, and the following function will do this for us:
-
-# tag::positive-negative-examples[]
-with driver.session(database="neo4j") as session:
-    result = session.run("""
-             MATCH (author:Author)-[:CO_AUTHOR_EARLY]->(other:Author)
-             RETURN id(author) AS node1, id(other) AS node2, 1 AS label""")
-    train_existing_links = pd.DataFrame([dict(record) for record in result])
-
-    result = session.run("""
-             MATCH (author:Author)
-             WHERE (author)-[:CO_AUTHOR_EARLY]-()
-             MATCH (author)-[:CO_AUTHOR_EARLY*2..3]-(other)
-             WHERE not((author)-[:CO_AUTHOR_EARLY]-(other))
-             RETURN id(author) AS node1, id(other) AS node2, 0 AS label""")
-    train_missing_links = pd.DataFrame([dict(record) for record in result])    
-    train_missing_links = train_missing_links.drop_duplicates()
-# end::positive-negative-examples[]
-
-# +
-# tag::count-positive-negative[]
-training_df = train_missing_links.append(train_existing_links, ignore_index=True)
-training_df['label'] = training_df['label'].astype('category')
-
-count_class_0, count_class_1 = training_df.label.value_counts()
-print(f"Negative examples: {count_class_0}")
-print(f"Positive examples: {count_class_1}")
-# end::count-positive-negative[]
-
-# tag::down-sample[]
-df_class_0 = training_df[training_df['label'] == 0]
-df_class_1 = training_df[training_df['label'] == 1]
-
-df_class_0_under = df_class_0.sample(count_class_1)
-df_train_under = pd.concat([df_class_0_under, df_class_1], axis=0)
-
-print('Random downsampling:')
-print(df_train_under.label.value_counts())
-# end::down-sample[]
-# -
-
-# Let's now do the same thing for our test set:
-
-# tag::test-positive-negative-examples[]
-with driver.session(database="neo4j") as session:
-    result = session.run("""
-             MATCH (author:Author)-[:CO_AUTHOR_LATE]->(other:Author)
-             RETURN id(author) AS node1, id(other) AS node2, 1 AS label""")
-    test_existing_links = pd.DataFrame([dict(record) for record in result])
-
-    result = session.run("""
-             MATCH (author:Author)
-             WHERE (author)-[:CO_AUTHOR_LATE]-()
-             MATCH (author)-[:CO_AUTHOR_LATE*2..3]-(other)
-             WHERE not((author)-[:CO_AUTHOR_LATE]-(other))
-             RETURN id(author) AS node1, id(other) AS node2, 0 AS label""")
-    test_missing_links = pd.DataFrame([dict(record) for record in result])    
-    test_missing_links = test_missing_links.drop_duplicates()
-# end::test-positive-negative-examples[]
-
-# +
-# Compute positive and negative examples
-
-# tag::count-test-positive-negative[]
-test_df = test_missing_links.append(test_existing_links, ignore_index=True)
-test_df['label'] = test_df['label'].astype('category')
-
-count_class_0, count_class_1 = test_df.label.value_counts()
-print(f"Negative examples: {count_class_0}")
-print(f"Positive examples: {count_class_1}")
-# end::count-test-positive-negative[]
-
-# Down sample the negative examples
-
-# tag::down-sample-test[]
-df_class_0 = test_df[test_df['label'] == 0]
-df_class_1 = test_df[test_df['label'] == 1]
-
-df_class_0_under = df_class_0.sample(count_class_1)
-df_test_under = pd.concat([df_class_0_under, df_class_1], axis=0)
-
-print('Random downsampling:')
-print(df_test_under.label.value_counts())
-# end::down-sample-test[]
-# -
-
-# Before we move on, let's have a look at the contents of our train and test DataFrames:
-
-# tag::train-preview[]
-df_train_under.sample(5, random_state=42)
-# end::train-preview[]
-
-# tag::test-preview[]
-df_test_under.sample(5, random_state=42)
-# end::test-preview[]
-
-# +
-# Save our DataFrames to CSV files for use in the next notebook
-
-# df_train_under.to_csv("data/df_train_under.csv", index=False)
-# df_test_under.to_csv("data/df_test_under.csv", index=False)
-
-df_train_under = pd.read_csv("data/df_train_under.csv")
-df_test_under = pd.read_csv("data/df_test_under.csv")
-
-df_train_under.sample(5, random_state=42).to_csv("data/df_train_under_basic_sample.csv", index=False)
-df_test_under.sample(5, random_state=42).to_csv("data/df_test_under_basic_sample.csv", index=False)
diff --git a/modules/graph-data-science/examples/link-prediction/py/04_Model_Feature_Engineering.py b/modules/graph-data-science/examples/link-prediction/py/04_Model_Feature_Engineering.py
deleted file mode 100644
index fd027038..00000000
--- a/modules/graph-data-science/examples/link-prediction/py/04_Model_Feature_Engineering.py
+++ /dev/null
@@ -1,379 +0,0 @@
-# -*- coding: utf-8 -*-
-# ---
-# jupyter:
-#   jupytext:
-#     formats: py:light,ipynb
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.5.2
-#   kernelspec:
-#     display_name: Python 3
-#     language: python
-#     name: python3
-# ---
-
-# # Feature Engineering
-#
-# In this notebook we're going to generate features for our link prediction classifier.
-
-# +
-from neo4j import GraphDatabase
-
-import pandas as pd
-import matplotlib
-import matplotlib.pyplot as plt
-
-plt.style.use('fivethirtyeight')
-pd.set_option('display.float_format', lambda x: '%.3f' % x)
-
-# tag::imports[]
-from sklearn.ensemble import RandomForestClassifier
-# end::imports[]
-
-# +
-bolt_uri = "bolt://link-prediction-neo4j"
-driver = GraphDatabase.driver(bolt_uri, auth=("neo4j", "admin"))
-
-print(driver.address)
-# -
-
-# We can create our classifier with the following code:
-
-# +
-# Load the CSV files saved in the train/test notebook
-
-df_train_under = pd.read_csv("data/df_train_under.csv")
-df_test_under = pd.read_csv("data/df_test_under.csv")
-# -
-
-df_train_under.sample(5)
-
-df_test_under.sample(5)
-
-
-# # Generating graphy features
-#
-# We’ll start by creating a simple model that tries to predict whether two authors will have a future collaboration based on features extracted from common authors, preferential attachment, and the total union of neighbors.
-#
-# The following function computes each of these measures for pairs of nodes:
-
-# tag::graphy-features[]
-def apply_graphy_features(data, rel_type):
-    query = """
-    UNWIND $pairs AS pair
-    MATCH (p1) WHERE id(p1) = pair.node1
-    MATCH (p2) WHERE id(p2) = pair.node2
-    RETURN pair.node1 AS node1,
-           pair.node2 AS node2,
-           gds.alpha.linkprediction.commonNeighbors(p1, p2, {
-             relationshipQuery: $relType}) AS cn,
-           gds.alpha.linkprediction.preferentialAttachment(p1, p2, {
-             relationshipQuery: $relType}) AS pa,
-           gds.alpha.linkprediction.totalNeighbors(p1, p2, {
-             relationshipQuery: $relType}) AS tn
-    """
-    pairs = [{"node1": node1, "node2": node2}  for node1,node2 in data[["node1", "node2"]].values.tolist()]
-
-    with driver.session(database="neo4j") as session:
-        result = session.run(query, {"pairs": pairs, "relType": rel_type})
-        features = pd.DataFrame([dict(record) for record in result])
-    return pd.merge(data, features, on = ["node1", "node2"])
-# end::graphy-features[]
-
-
-# Let's apply the function to our training DataFrame:
-
-# tag::apply-graphy-features[]
-df_train_under = apply_graphy_features(df_train_under, "CO_AUTHOR_EARLY")
-df_test_under = apply_graphy_features(df_test_under, "CO_AUTHOR")
-# end::apply-graphy-features[]
-
-# Now we're going to add some new features that are generated from graph algorithms.
-#
-# ## Triangles and The Clustering Coefficient
-#
-# We'll start by running the [triangle count](clusteringCoefficientProperty) algorithm over our test and train sub graphs. This algorithm will return the number of triangles that each node forms, as well as each node's clustering coefficient. The clustering coefficient of a node indicates the likelihood that its neighbours are also connected.
-
-# +
-# tag::train-triangles[]
-query = """
-CALL gds.triangleCount.write({
-  nodeProjection: 'Author',
-  relationshipProjection: {
-    CO_AUTHOR_EARLY: {
-      type: 'CO_AUTHOR_EARLY',
-      orientation: 'UNDIRECTED'
-    }
-  },
-  writeProperty: 'trianglesTrain'
-});
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-# end::train-triangles[]
-    df = pd.DataFrame([dict(record) for record in result])
-df
-
-# +
-# tag::test-triangles[]
-query = """
-CALL gds.triangleCount.write({
-  nodeProjection: 'Author',
-  relationshipProjection: {
-    CO_AUTHOR: {
-      type: 'CO_AUTHOR',
-      orientation: 'UNDIRECTED'
-    }
-  },
-  writeProperty: 'trianglesTest'
-});
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-# end::test-triangles[]
-    df = pd.DataFrame([dict(record) for record in result])
-df
-
-# +
-# tag::train-coefficient[]
-query = """
-CALL gds.localClusteringCoefficient.write({
-  nodeProjection: 'Author',
-  relationshipProjection: {
-    CO_AUTHOR_EARLY: {
-      type: 'CO_AUTHOR',
-      orientation: 'UNDIRECTED'
-    }
-  },
-  writeProperty: 'coefficientTrain'
-});
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-# end::train-coefficient[]
-    df = pd.DataFrame([dict(record) for record in result])
-df
-
-# +
-# tag::test-coefficient[]
-query = """
-CALL gds.localClusteringCoefficient.write({
-  nodeProjection: 'Author',
-  relationshipProjection: {
-    CO_AUTHOR_EARLY: {
-      type: 'CO_AUTHOR',
-      orientation: 'UNDIRECTED'
-    }
-  },
-  writeProperty: 'coefficientTest'
-});
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-# end::test-coefficient[]
-    df = pd.DataFrame([dict(record) for record in result])
-df
-
-
-# -
-
-# The following function will add these features to our train and test DataFrames:
-
-# tag::triangles-coefficient-features[]
-def apply_triangles_features(data, triangles_prop, coefficient_prop):
-    query = """
-    UNWIND $pairs AS pair
-    MATCH (p1) WHERE id(p1) = pair.node1
-    MATCH (p2) WHERE id(p2) = pair.node2
-    RETURN pair.node1 AS node1,
-    pair.node2 AS node2,
-    apoc.coll.min([p1[$trianglesProp], p2[$trianglesProp]]) AS minTriangles,
-    apoc.coll.max([p1[$trianglesProp], p2[$trianglesProp]]) AS maxTriangles,
-    apoc.coll.min([p1[$coefficientProp], p2[$coefficientProp]]) AS minCoefficient,
-    apoc.coll.max([p1[$coefficientProp], p2[$coefficientProp]]) AS maxCoefficient
-    """
-    pairs = [{"node1": node1, "node2": node2}  for node1,node2 in data[["node1", "node2"]].values.tolist()]
-    params = {
-    "pairs": pairs,
-    "trianglesProp": triangles_prop,
-    "coefficientProp": coefficient_prop
-    }
-
-    with driver.session(database="neo4j") as session:
-        result = session.run(query, params)
-        features = pd.DataFrame([dict(record) for record in result])
-
-    return pd.merge(data, features, on = ["node1", "node2"])
-# end::triangles-coefficient-features[]
-
-
-# tag::apply-triangles-coefficient-features[]
-df_train_under = apply_triangles_features(df_train_under, "trianglesTrain", "coefficientTrain")
-df_test_under = apply_triangles_features(df_test_under, "trianglesTest", "coefficientTest")
-# end::apply-triangles-coefficient-features[]
-
-df_train_under.sample(5)
-
-df_test_under.sample(5)
-
-# ## Community Detection
-#
-# Community detection algorithms evaluate how a group is clustered or partitioned. Nodes are considered more similar to nodes that fall in their community than to nodes in other communities.
-#
-# We'll run two community detection algorithms over the train and test sub graphs - Label Propagation and Louvain. First up, Label Propagation:
-
-# +
-# tag::train-lpa[]
-query = """
-CALL gds.labelPropagation.write({
-  nodeProjection: "Author",
-  relationshipProjection: {
-    CO_AUTHOR_EARLY: {
-      type: 'CO_AUTHOR_EARLY',
-      orientation: 'UNDIRECTED'
-    }
-  },
-  writeProperty: "partitionTrain"
-});
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-# end::train-lpa[]
-    df = pd.DataFrame([dict(record) for record in result])
-df
-
-# +
-# tag::test-lpa[]
-query = """
-CALL gds.labelPropagation.write({
-  nodeProjection: "Author",
-  relationshipProjection: {
-    CO_AUTHOR: {
-      type: 'CO_AUTHOR',
-      orientation: 'UNDIRECTED'
-    }
-  },
-  writeProperty: "partitionTest"
-});
-"""
-
-with driver.session(database="neo4j") as session:
-    result = session.run(query)
-# end::test-lpa[]
-    df = pd.DataFrame([dict(record) for record in result])
-df
-# -
-
-# And now Louvain. The Louvain algorithm returns intermediate communities, which are useful for finding fine grained communities that exist in a graph. We'll add a property to each node containing the community revealed on the first iteration of the algorithm:
-
-# +
-# tag::train-louvain[]
-query = """
-CALL gds.louvain.stream({
-  nodeProjection: 'Author',
-  relationshipProjection: {
-    CO_AUTHOR_EARLY: {
-      type: 'CO_AUTHOR_EARLY',
-      orientation: 'UNDIRECTED'
-    }
-  },
-  includeIntermediateCommunities: true
-})
-YIELD nodeId, communityId, intermediateCommunityIds
-WITH gds.util.asNode(nodeId) AS node, intermediateCommunityIds[0] AS smallestCommunity
-SET node.louvainTrain = smallestCommunity;
-"""
-
-with driver.session(database="neo4j") as session:
-    display(session.run(query).consume().counters)
-# end::train-louvain[]
-
-# +
-# tag::test-louvain[]
-query = """
-CALL gds.louvain.stream({
-  nodeProjection: 'Author',
-  relationshipProjection: {
-    CO_AUTHOR: {
-      type: 'CO_AUTHOR',
-      orientation: 'UNDIRECTED'
-    }
-  },
-  includeIntermediateCommunities: true
-})
-YIELD nodeId, communityId, intermediateCommunityIds
-WITH gds.util.asNode(nodeId) AS node, intermediateCommunityIds[0] AS smallestCommunity
-SET node.louvainTest = smallestCommunity;
-"""
-
-with driver.session(database="neo4j") as session:
-    display(session.run(query).consume().counters)
-# end::test-louvain[]
-# -
-
-# tag::community-features[]
-def apply_community_features(data, partition_prop, louvain_prop):
-    query = """
-    UNWIND $pairs AS pair
-    MATCH (p1) WHERE id(p1) = pair.node1
-    MATCH (p2) WHERE id(p2) = pair.node2
-    RETURN pair.node1 AS node1,
-    pair.node2 AS node2,
-    gds.alpha.linkprediction.sameCommunity(p1, p2, $partitionProp) AS sp,
-    gds.alpha.linkprediction.sameCommunity(p1, p2, $louvainProp) AS sl
-    """
-    pairs = [{"node1": node1, "node2": node2}  for node1,node2 in data[["node1", "node2"]].values.tolist()]
-    params = {
-    "pairs": pairs,
-    "partitionProp": partition_prop,
-    "louvainProp": louvain_prop
-    }
-
-    with driver.session(database="neo4j") as session:
-        result = session.run(query, params)
-        features = pd.DataFrame([dict(record) for record in result])
-
-    return pd.merge(data, features, on = ["node1", "node2"])
-# end::community-features[]
-
-
-# tag::apply-community-features[]
-df_train_under = apply_community_features(df_train_under, "partitionTrain", "louvainTrain")
-df_test_under = apply_community_features(df_test_under, "partitionTest", "louvainTest")
-# end::apply-community-features[]
-
-# tag::train-after-features[]
-df_train_under.drop(columns=["node1", "node2"]).sample(5, random_state=42)
-# end::train-after-features[]
-
-# tag::test-after-features[]
-df_test_under.drop(columns=["node1", "node2"]).sample(5, random_state=42)
-# end::test-after-features[]
-
-# +
-# Re-order so that label is last
-df_train_under = df_train_under.reindex(columns=sorted(df_train_under.columns))
-df_train_under = df_train_under.reindex(columns=(list([a for a in df_train_under.columns if a != 'label']) + ['label'] ))
-
-df_test_under = df_test_under.reindex(columns=sorted(df_test_under.columns))
-df_test_under = df_test_under.reindex(columns=(list([a for a in df_test_under.columns if a != 'label']) + ['label'] ))
-
-
-# Save our DataFrames to CSV files for use in the next notebook
-
-df_train_under.to_csv("data/df_train_under_all.csv", index=False)
-df_test_under.to_csv("data/df_test_under_all.csv", index=False)
-
-# df_train_under = pd.read_csv("data/df_train_under_all.csv")
-# df_test_under = pd.read_csv("data/df_test_under_all.csv")
-
-# Save the samples as CSV files as well
-df_train_under.drop(columns=["node1", "node2"]).sample(5, random_state=42).to_csv("data/df_train_under_sample.csv", index=False, float_format='%g')
-df_test_under.drop(columns=["node1", "node2"]).sample(5, random_state=42).to_csv("data/df_test_under_sample.csv", index=False, float_format='%g')
diff --git a/modules/graph-data-science/examples/link-prediction/py/05_Train_Evaluate_Model.py b/modules/graph-data-science/examples/link-prediction/py/05_Train_Evaluate_Model.py
deleted file mode 100644
index d33ac665..00000000
--- a/modules/graph-data-science/examples/link-prediction/py/05_Train_Evaluate_Model.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# -*- coding: utf-8 -*-
-# ---
-# jupyter:
-#   jupytext:
-#     formats: ipynb,py:light
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.5.2
-#   kernelspec:
-#     display_name: Python 3
-#     language: python
-#     name: python3
-# ---
-
-# # Training the machine learning model
-#
-# In this notebook we're going to create a machine learning model and train it against the features that we engineered in the previous notebook.
-#
-# We’ll be using a random forest classifier. This method is well suited as our data set will be comprised of a mix of strong and weak features. While the weak features will sometimes be helpful, the random forest method will ensure we don’t create a model that only fits our training data.
-
-# +
-import pandas as pd
-import matplotlib
-import matplotlib.pyplot as plt
-
-plt.style.use('fivethirtyeight')
-pd.set_option('display.float_format', lambda x: '%.3f' % x)
-
-# tag::imports[]
-from sklearn.ensemble import RandomForestClassifier
-# end::imports[]
-# -
-
-# Let's first load the features that we engineered in the previous notebook:
-
-# +
-# Load the CSV files saved in the train/test notebook
-
-df_train_under = pd.read_csv("data/df_train_under_all.csv")
-df_test_under = pd.read_csv("data/df_test_under_all.csv")
-# -
-
-df_train_under.sample(5)
-
-df_test_under.sample(5)
-
-# We can create our random forest classifier with the following code:
-
-# tag::create-classifier[]
-classifier = RandomForestClassifier(n_estimators=30, max_depth=10, random_state=0)
-# end::create-classifier[]
-
-# And now let's train the model:
-
-# +
-# tag::train-model[]
-columns = [
-    "cn", "pa", "tn", # graph features
-    "minTriangles", "maxTriangles", "minCoefficient", "maxCoefficient", # triangle features  
-    "sp", "sl" # community features
-]
-
-X = df_train_under[columns]
-y = df_train_under["label"]
-classifier.fit(X, y)
-# end::train-model[]
-# -
-
-# Next we're going to evaluate our model and see which features are the most influential. The following two functions will help us do this:
-
-# +
-# tag::evaluation-imports[]
-from sklearn.metrics import recall_score
-from sklearn.metrics import precision_score
-from sklearn.metrics import accuracy_score
-# end::evaluation-imports[]
-
-# tag::evaluation-functions[]
-def evaluate_model(predictions, actual):
-    return pd.DataFrame({
-        "Measure": ["Accuracy", "Precision", "Recall"],
-        "Score": [accuracy_score(actual, predictions), 
-                  precision_score(actual, predictions), 
-                  recall_score(actual, predictions)]
-    })
-# end::evaluation-functions[]
-
-def feature_importance(columns, classifier):        
-    display("Feature Importance")
-    df = pd.DataFrame({
-        "Feature": columns,
-        "Importance": classifier.feature_importances_
-    })
-    df = df.sort_values("Importance", ascending=False)    
-    ax = df.plot(kind='bar', x='Feature', y='Importance', legend=None)
-    ax.xaxis.set_label_text("")
-    plt.tight_layout()
-    plt.show()
-
-
-# -
-
-# Now let's see how well our model does against the test set:
-
-# +
-# tag::test-model[]
-predictions = classifier.predict(df_test_under[columns])
-y_test = df_test_under["label"]
-
-evaluate_model(predictions, y_test)
-# end::test-model[]
-# -
-
-evaluate_model(predictions, y_test).to_csv("data/model-eval.csv", index=False)
-
-# 96% on all the metrics, not bad. And finally we can see which features are having the most influence:
-
-feature_importance(columns, classifier)
diff --git a/modules/graph-data-science/examples/link-prediction/py/06_SageMaker.py b/modules/graph-data-science/examples/link-prediction/py/06_SageMaker.py
deleted file mode 100644
index 5ed20f80..00000000
--- a/modules/graph-data-science/examples/link-prediction/py/06_SageMaker.py
+++ /dev/null
@@ -1,363 +0,0 @@
-# -*- coding: utf-8 -*-
-# ---
-# jupyter:
-#   jupytext:
-#     formats: ipynb,py:light
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.5.2
-#   kernelspec:
-#     display_name: Python 3
-#     language: python
-#     name: python3
-# ---
-
-# # Training a model with Amazon SageMaker Autopilot
-#
-# In this notebook we're going to learn how to train a model with Amazon's SageMaker Autopilot. Amazon SageMaker Autopilot is an automated machine learning (commonly referred to as AutoML) solution for tabular datasets. 
-#
-# First let's install the sagemaker library:
-
-# tag::pip-install-sagemaker[]
-# !pip install sagemaker
-# end::pip-install-sagemaker[]
-
-# +
-import pandas as pd
-
-# tag::imports[]
-import sagemaker
-import boto3
-import os
-from time import gmtime, strftime, sleep
-# end::imports[]
-# -
-
-# Let's first load the features that we engineered in the previous notebook:
-
-# +
-# Load the CSV files saved in the train/test notebook
-
-# tag::load-csv-files[]
-df_train_under = pd.read_csv("data/df_train_under_all.csv")
-df_test_under = pd.read_csv("data/df_test_under_all.csv")
-# end::load-csv-files[]
-# -
-
-# tag::train-features[]
-df_train_under.drop(columns=["node1", "node2"]).sample(5, random_state=42)
-# end::train-features[]
-
-# tag::test-features[]
-df_test_under.drop(columns=["node1", "node2"]).sample(5, random_state=42)
-# end::test-features[]
-
-# +
-# tag::prerequisites[]
-boto_session = boto3.Session(
-    aws_access_key_id=os.environ["ACCESS_ID"],
-    aws_secret_access_key= os.environ["ACCESS_KEY"])
-
-region = boto_session.region_name
-
-session = sagemaker.Session(boto_session=boto_session)
-bucket = session.default_bucket()
-
-timestamp_suffix = strftime('%Y-%m-%d-%H-%M-%S', gmtime())
-
-prefix = 'sagemaker/link-prediction-developer-guide-' + timestamp_suffix
-
-role = os.environ["SAGEMAKER_ROLE"]
-
-sm = boto_session.client(service_name='sagemaker',region_name=region)
-# end::prerequisites[]
-
-print(timestamp_suffix, prefix)
-# -
-
-# ## Upload the dataset to Amazon S3
-#
-# Copy the file to Amazon Simple Storage Service (Amazon S3) in a .csv format for Amazon SageMaker training to use.
-
-# +
-# tag::upload-dataset-s3[]
-train_columns = [
-    "cn", "pa", "tn", "minTriangles", "maxTriangles", "minCoefficient", "maxCoefficient", "sp", "sl", "label"
-]
-df_train_under = df_train_under[train_columns]
-
-test_columns = [
-    "cn", "pa", "tn", "minTriangles", "maxTriangles", "minCoefficient", "maxCoefficient", "sp", "sl"
-]
-df_test_under = df_test_under.drop(columns=["label"])[test_columns]
-
-train_file = 'data/upload/train_data_binary_classifier.csv';
-df_train_under.to_csv(train_file, index=False, header=True)
-train_data_s3_path = session.upload_data(path=train_file, key_prefix=prefix + "/train")
-print('Train data uploaded to: ' + train_data_s3_path)
-
-test_file = 'data/upload/test_data_binary_classifier.csv';
-df_test_under.to_csv(test_file, index=False, header=False)
-test_data_s3_path = session.upload_data(path=test_file, key_prefix=prefix + "/test")
-print('Test data uploaded to: ' + test_data_s3_path)
-# end::upload-dataset-s3[]
-# -
-
-# ## Setting up the SageMaker Autopilot Job
-#
-# Now that we've uploaded the dataset to S3, we're going to call Autopilot and have it find the best model for the dataset. Autopilot's required inputs are as follows:
-#
-# * Amazon S3 location for input dataset and for all output artifacts
-# * Name of the column of the dataset you want to predict (`label` in this case) 
-# * An IAM role
-#
-# The training CSV file that we send to Autopilot should have a header row.
-
-# +
-# tag::autopilot-setup[]
-input_data_config = [{
-      'DataSource': {
-        'S3DataSource': {
-          'S3DataType': 'S3Prefix',
-          'S3Uri': 's3://{}/{}/train'.format(bucket,prefix)
-        }
-      },
-      'TargetAttributeName': 'label'
-    }
-  ]
-
-automl_job_config = {
-    "CompletionCriteria": {
-        "MaxRuntimePerTrainingJobInSeconds": 300,
-        "MaxCandidates": 5,
-    }
-}
-
-output_data_config = {
-    'S3OutputPath': 's3://{}/{}/output'.format(bucket,prefix)
-  }
-# end::autopilot-setup[]
-# -
-
-# ## Launching the SageMaker Autopilot Job
-#
-# We can now launch the Autopilot job by calling the `create_auto_ml_job` API. 
-
-# +
-# tag::autopilot-launch[]
-auto_ml_job_name = 'automl-link-' + timestamp_suffix
-print('AutoMLJobName: ' + auto_ml_job_name)
-
-sm.create_auto_ml_job(AutoMLJobName=auto_ml_job_name,
-                      InputDataConfig=input_data_config,
-                      OutputDataConfig=output_data_config,
-                      ProblemType="BinaryClassification",
-                      AutoMLJobObjective={"MetricName": "Accuracy"},
-                      AutoMLJobConfig=automl_job_config,
-                      RoleArn=role)
-# end::autopilot-launch[]
-# -
-
-# ### Tracking SageMaker Autopilot job progress
-#
-# SageMaker Autopilot job consists of the following high-level steps : 
-#
-# * Analyzing Data, where the dataset is analyzed and Autopilot comes up with a list of ML pipelines that should be tried out on the dataset. The dataset is also split into train and validation sets.
-# * Feature Engineering, where Autopilot performs feature transformation on individual features of the dataset as well as at an aggregate level.
-# * Model Tuning, where the top performing pipeline is selected along with the optimal hyperparameters for the training algorithm (the last stage of the pipeline). 
-
-# +
-print ('JobStatus - Secondary Status')
-print('------------------------------')
-
-# auto_ml_job_name = "automl-link-2020-08-20-11-26-33"
-
-# tag::autopilot-track-progress[]
-describe_response = sm.describe_auto_ml_job(AutoMLJobName=auto_ml_job_name)
-print (describe_response['AutoMLJobStatus'] + " - " + describe_response['AutoMLJobSecondaryStatus'])
-job_run_status = describe_response['AutoMLJobStatus']
-    
-while job_run_status not in ('Failed', 'Completed', 'Stopped'):
-    describe_response = sm.describe_auto_ml_job(AutoMLJobName=auto_ml_job_name)
-    job_run_status = describe_response['AutoMLJobStatus']
-    
-    print (describe_response['AutoMLJobStatus'] + " - " + describe_response['AutoMLJobSecondaryStatus'])
-    sleep(30)
-# end::autopilot-track-progress[]
-
-# +
-# tag::autopilot-all-candidates[]
-candidates = sm.list_candidates_for_auto_ml_job(
-    AutoMLJobName=auto_ml_job_name, 
-    SortBy='FinalObjectiveMetricValue')['Candidates']
-
-candidates_df = pd.DataFrame({
-    "name": [c["CandidateName"] for c in candidates],
-    "score": [c["FinalAutoMLJobObjectiveMetric"]["Value"] for c in candidates]
-})
-candidates_df
-# end::autopilot-all-candidates[]
-
-display(candidates_df)
-candidates_df.to_csv("data/download/autopilot_candidates.csv", index=False, float_format='%g')
-
-# +
-# tag::autopilot-best-candidate[]
-best_candidate = sm.describe_auto_ml_job(
-    AutoMLJobName=auto_ml_job_name)['BestCandidate']
-
-best_df = pd.DataFrame({
-    "name": [best_candidate['CandidateName']],
-    "metric": [best_candidate['FinalAutoMLJobObjectiveMetric']['MetricName']],
-    "score": [best_candidate['FinalAutoMLJobObjectiveMetric']['Value']]
-})
-best_df
-# end::autopilot-best-candidate[]
-
-display(best_df)
-best_df.to_csv("data/download/autopilot_best_candidate.csv", index=False, float_format='%g')
-# -
-
-# ### Perform batch inference using the best candidate
-#
-# Now that we have successfully completed the SageMaker Autopilot job on the dataset, create a model from any of the candidates by using [Inference Pipelines](https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipelines.html). 
-
-# +
-# timestamp_suffix = "automl-link-2020-08-20-09-25-03"
-
-# tag::autopilot-create-model[]
-model_name = 'automl-link-pred-model-' + timestamp_suffix
-
-model = sm.create_model(Containers=best_candidate['InferenceContainers'],
-                            ModelName=model_name,
-                            ExecutionRoleArn=role)
-
-print('Model ARN corresponding to the best candidate is : {}'.format(model['ModelArn']))
-# end::autopilot-create-model[]
-# -
-
-# ### Evaluating the model
-#
-# And now we're going to create a transform job based on this model.
-# A transform job uses a trained model to get inferences on a dataset and saves these results to S3.
-
-# +
-# test_data_s3_path = "s3://sagemaker-us-east-1-715633473519/sagemaker/link-prediction-developer-guide-2020-08-20-11-26-33/train/train_data_copy.csv"
-# timestamp_suffix = "2020-08-20-11-26-33"
-# timestamp_suffix = strftime('%Y-%m-%d-%H-%M-%S', gmtime())
-
-# tag::autopilot-create-transform-job[]
-transform_job_name = 'automl-link-pred-transform-job-' + timestamp_suffix
-
-print(test_data_s3_path, transform_job_name, model_name)
-
-transform_input = {
-        'DataSource': {
-            'S3DataSource': {
-                'S3DataType': 'S3Prefix',
-                'S3Uri': test_data_s3_path
-            }
-        },
-        'ContentType': 'text/csv',
-        'CompressionType': 'None',
-        'SplitType': 'Line'
-    }
-
-transform_output = {
-        'S3OutputPath': 's3://{}/{}/inference-results'.format(bucket,prefix),
-    }
-
-transform_resources = {
-        'InstanceType': 'ml.m5.4xlarge',
-        'InstanceCount': 1
-    }
-
-sm.create_transform_job(TransformJobName = transform_job_name,
-                        ModelName = model_name,
-                        TransformInput = transform_input,
-                        TransformOutput = transform_output,
-                        TransformResources = transform_resources
-)
-# end::autopilot-create-transform-job[]
-
-# +
-print ('JobStatus')
-print('----------')
-
-# tag::autopilot-track-transform-job[]
-describe_response = sm.describe_transform_job(TransformJobName = transform_job_name)
-job_run_status = describe_response['TransformJobStatus']
-print (job_run_status)
-
-while job_run_status not in ('Failed', 'Completed', 'Stopped'):
-    describe_response = sm.describe_transform_job(TransformJobName = transform_job_name)
-    job_run_status = describe_response['TransformJobStatus']
-    print (job_run_status)
-    sleep(30)
-# end::autopilot-track-transform-job[]    
-
-print(describe_response)
-# -
-
-# Now let's view the results of the transform job:
-
-# +
-prefix = "sagemaker/link-prediction-developer-guide-2020-09-22-10-53-59"
-
-# tag::autopilot-transform-job-results[]
-s3_output_key = '{}/inference-results/test_data_binary_classifier.csv.out'.format(prefix);
-local_inference_results_path = 'data/download/inference_results.csv'
-
-inference_results_bucket = boto_session.resource("s3").Bucket(session.default_bucket())
-inference_results_bucket.download_file(s3_output_key, local_inference_results_path);
-
-data = pd.read_csv(local_inference_results_path, sep=';', header=None)
-data.sample(10, random_state=42)
-# end::autopilot-transform-job-results[]
-
-# +
-# tag::evaluation-imports[]
-from sklearn.metrics import recall_score
-from sklearn.metrics import precision_score
-from sklearn.metrics import accuracy_score
-# end::evaluation-imports[]
-
-# tag::evaluation-functions[]
-def evaluate_model(predictions, actual):
-    return pd.DataFrame({
-        "Measure": ["Accuracy", "Precision", "Recall"],
-        "Score": [accuracy_score(actual, predictions), 
-                  precision_score(actual, predictions), 
-                  recall_score(actual, predictions)]
-    })
-# end::evaluation-functions[]
-
-def feature_importance(columns, classifier):        
-    display("Feature Importance")
-    df = pd.DataFrame({
-        "Feature": columns,
-        "Importance": classifier.feature_importances_
-    })
-    df = df.sort_values("Importance", ascending=False)    
-    ax = df.plot(kind='bar', x='Feature', y='Importance', legend=None)
-    ax.xaxis.set_label_text("")
-    plt.tight_layout()
-    plt.show()
-
-
-# +
-# tag::test-model[]
-df_test_under = pd.read_csv("data/df_test_under_all.csv")
-
-predictions = data[0]
-y_test = df_test_under["label"]
-
-evaluate_model(y_test, predictions)
-# end::test-model[]
-# -
-
-evaluate_model(y_test, predictions).to_csv("data/sagemaker-model-eval.csv", index=False)
-
-feature_importance(columns, classifier)
diff --git a/modules/graph-data-science/examples/nlp/guardian-football.xml b/modules/graph-data-science/examples/nlp/guardian-football.xml
deleted file mode 100644
index 22251bf8..00000000
--- a/modules/graph-data-science/examples/nlp/guardian-football.xml
+++ /dev/null
@@ -1,1198 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<rss xmlns:media="http://search.yahoo.com/mrss/" xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
-  <channel>
-    <title>Football | The Guardian
-    https://www.theguardian.com/football
-    Football news, results, fixtures, blogs, podcasts and comment on the Premier League, European and World football from the Guardian, the world's leading liberal voice
-    en-gb
-    Guardian News and Media Limited or its affiliated companies. All rights reserved. 2020
-    Mon, 30 Nov 2020 16:56:18 GMT
-    2020-11-30T16:56:18Z
-    en-gb
-    Guardian News and Media Limited or its affiliated companies. All rights reserved. 2020
-    
-      The Guardian
-      https://assets.guim.co.uk/images/guardian-logo-rss.c45beb1bafa34b347ac333af2e6fe23f.png
-      https://www.theguardian.com
-    
-    
-      Manchester United's Edinson Cavani apologises for 'racist' Instagram post
-      https://www.theguardian.com/football/2020/nov/30/manchester-uniteds-edinson-cavani-apologies-for-racist-instagram-post
-      <ul><li>Striker insists he meant no offence and is backed by club</li><li>Cavani could still be hit with FA ban despite deleting post</li></ul><p>Edinson Cavani has “sincerely apologised” for using the word “<em>negrito</em>” in a social media post following <a href="https://www.theguardian.com/football/2020/nov/29/southampton-manchester-united-premier-league-match-report">Manchester United’s 3-2 win at Southampton on Sunday</a>, stating it was an affectionate greeting to a friend. The club have also moved to support the 33-year-old Uruguayan.</p><p>Cavani may face a three-match ban if the Football Association deems the word to be discriminatory or racist, with the governing body scrutinising the incident.</p> <a href="https://www.theguardian.com/football/2020/nov/30/manchester-uniteds-edinson-cavani-apologies-for-racist-instagram-post">Continue reading...</a>
-      Manchester United
-      The FA
-      Football
-      Sport
-      Mon, 30 Nov 2020 15:59:06 GMT
-      https://www.theguardian.com/football/2020/nov/30/manchester-uniteds-edinson-cavani-apologies-for-racist-instagram-post
-      
-        Photograph: Martin Rickett/PA
-      
-      
-        Photograph: Martin Rickett/PA
-      
-      Jamie Jackson
-      2020-11-30T15:59:06Z
-    
-    
-      The need for concussion substitutes – Football Weekly
-      https://www.theguardian.com/football/audio/2020/nov/30/the-need-for-concussion-substitutes-football-weekly
-      <p> <a href="https://twitter.com/maxrushden"><strong>Max Rushden</strong></a> is joined<strong> </strong>by <strong><a href="https://twitter.com/bglendenning">Barry Glendenning</a>, <a href="https://twitter.com/larssivertsen">Lars Sivertsen</a>, <a href="https://twitter.com/mrewanmurray">Ewan Murray</a></strong> and <strong><a href="https://twitter.com/FayeCarruthers">Faye Carruthers</a> </strong>to discuss the need for concussion substitutes and Manchester United’s victory over Southampton</p><p><strong>Rate, review, share on </strong><a href="https://itunes.apple.com/podcast/football-weekly-the-guardian/id188674007?mt=2"><strong>Apple Podcasts</strong></a><strong>, </strong><a href="https://soundcloud.com/guardianfootballweekly"><strong>Soundcloud</strong></a><strong>, </strong><a href="https://audioboom.com/channel/football-weekly"><strong>Audioboom</strong></a><strong>, </strong><a href="https://www.mixcloud.com/guardianfootballweekly/"><strong>Mixcloud</strong></a><strong>, </strong><a href="https://www.acast.com/footballweekly"><strong>Acast</strong></a><strong> and </strong><a href="http://www.stitcher.com/podcast/guardianuk/football-weekly"><strong>Stitcher</strong></a><strong>, and join the conversation on </strong><a href="https://www.facebook.com/GuardianPodcasts/"><strong>Facebook</strong></a><strong>, </strong><a href="https://twitter.com/guardianaudio"><strong>Twitter</strong></a><strong> and </strong><a href="mailto:footballweekly@theguardian.com"><strong>email</strong></a><strong>.</strong></p><p> The pod discuss the need for concussion subs after the head injury suffered by Wolves’s Raúl Jiménez against Arsenal and the decision for David Luiz to play on.</p> <a href="https://www.theguardian.com/football/audio/2020/nov/30/the-need-for-concussion-substitutes-football-weekly">Continue reading...</a>
-      Football
-      Sport
-      Mon, 30 Nov 2020 16:23:50 GMT
-      https://www.theguardian.com/football/audio/2020/nov/30/the-need-for-concussion-substitutes-football-weekly
-      
-        Photograph: Catherine Ivill/Getty Images
-      
-      
-        Photograph: Catherine Ivill/Getty Images
-      
-      Presented by Max Rushden with Barry Glendenning Faye Carruthers, Ewan Murray and Lars Sivertson
-      2020-11-30T16:23:50Z
-    
-    
-      Lennon the fall guy for Celtic's failure to build on domestic dominance
-      https://www.theguardian.com/football/2020/nov/30/neil-lennon-the-fallguy-celtic-domestic-dominance-scottish-football
-      <p>Ugly scenes after the League Cup defeat to Ross County reflected a growing sense of frustration among fans <br></p><p>So much for managing Celtic as football’s equivalent to living on Easy Street. Fifteen years ago, Martin O’Neill’s tenure ended with a painful concession of the title on its final day to Rangers. Having stayed a season too long – even by his own admission – Gordon Strachan departed in 2009 with Rangers again the champions; the Celtic support were tired of one-liners and one-dimensional play.</p><p>Tony Mowbray toiled before being sacked. Having taken charge for the first time, Neil Lennon resigned in 2014 with the staleness of a one-horse race unable to stimulate him any longer. By the time the Ronny Deila project ended, two years later, there was acceptance that this punt was worthy of a smaller club. Celtic raised the bar in hiring Brendan Rodgers but there was only so long the current Leicester City manager would handle being the only fish in a small pond; internal rancour at the onset of season 2018-19 preceded the inevitable.</p> <a href="https://www.theguardian.com/football/2020/nov/30/neil-lennon-the-fallguy-celtic-domestic-dominance-scottish-football">Continue reading...</a>
-      Celtic
-      Football
-      Sport
-      Neil Lennon
-      Mon, 30 Nov 2020 13:37:05 GMT
-      https://www.theguardian.com/football/2020/nov/30/neil-lennon-the-fallguy-celtic-domestic-dominance-scottish-football
-      
-        Photograph: Jeff Holmes/PA
-      
-      
-        Photograph: Jeff Holmes/PA
-      
-      Ewan Murray
-      2020-11-30T13:37:05Z
-    
-    
-      Napoli find truest tribute to Maradona by mirroring his magic against Roma | Nicky Bandini
-      https://www.theguardian.com/football/2020/nov/30/napoli-truest-tribute-maradona-magic-victory-roma-serie-a
-      <p>Naples had struggled to find the right way to say farewell to a player who felt like family but the 4-0 win over Roma was fitting</p><p>Before Napoli kicked off against Roma on Sunday night, their captain Lorenzo Insigne went on a lap of the Stadio San Paolo, placing bouquets of flowers beneath the empty stands behind each goal. From the Curva A hung a banner prepared by fans, addressed to Diego Maradona: “Your death comes as a blow to the chest,” it read. “A pain in the heart. Naples swears you eternal love.”</p><p> <span>Related: </span><a href="https://www.theguardian.com/football/2020/nov/29/european-roundup-lionel-messi-pays-tribute-to-diego-maradona-in-barca-q">European roundup: Napoli and Lionel Messi pay tribute to Diego Maradona</a> </p> <a href="https://www.theguardian.com/football/2020/nov/30/napoli-truest-tribute-maradona-magic-victory-roma-serie-a">Continue reading...</a>
-      Serie A
-      Napoli
-      Diego Maradona
-      European club football
-      Football
-      Sport
-      Mon, 30 Nov 2020 11:37:00 GMT
-      https://www.theguardian.com/football/2020/nov/30/napoli-truest-tribute-maradona-magic-victory-roma-serie-a
-      
-        Photograph: Ciro Fusco/EPA
-      
-      
-        Photograph: Ciro Fusco/EPA
-      
-      Nicky Bandini
-      2020-11-30T11:37:00Z
-    
-    
-      Concussion substitutes trial could begin in Premier League early next year
-      https://www.theguardian.com/football/2020/nov/30/wolves-raul-jimenez-comfortable-surgery-fractured-skull-david-luiz-arsenal
-      <ul><li>Rule change would give teams an extra sub for head injury<br></li><li>News follows Raúl Jiménez’s clash of heads with David Luiz </li></ul><p>Trials of additional permanent substitutes for concussion injuries in the Premier League could begin early in the new year, the Guardian understands. The news comes after Wolves confirmed that their striker Raúl Jiménez had surgery on a fractured skull after a sickening clash of heads <a href="https://www.theguardian.com/football/2020/nov/29/arsenal-wolves-premier-league-match-report">with Arsenal’s David Luiz</a> on Sunday evening.</p><p>Wolves said the Mexico striker was comfortable after the operation. “He has since seen his partner Daniela and is now resting,” the club added in a statement. “He will remain under observation for a few days while he begins his recovery.”</p> <a href="https://www.theguardian.com/football/2020/nov/30/wolves-raul-jimenez-comfortable-surgery-fractured-skull-david-luiz-arsenal">Continue reading...</a>
-      Concussion in sport
-      Wolverhampton Wanderers
-      Arsenal
-      Football
-      Sport
-      Mon, 30 Nov 2020 13:51:52 GMT
-      https://www.theguardian.com/football/2020/nov/30/wolves-raul-jimenez-comfortable-surgery-fractured-skull-david-luiz-arsenal
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-        Photograph: John Walton/Reuters
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-        Photograph: John Walton/Reuters
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-      Sean Ingle
-      2020-11-30T13:51:52Z
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-      Köln rejoice in 'miracle of Dortmund' sealed by Haaland's incredible miss | Andy Brassell
-      https://www.theguardian.com/football/blog/2020/nov/30/koln-rejoice-miracle-of-dortmund-haaland-miss-bundesliga
-      <p>Two almost identical goals by Ellyes Skhiri and a last-minute blunder by Haaland ended a 267-day wait for a Bundesliga win</p><p>The moments of tension tugged more frequently at Kölsch hearts as the afternoon went on, as the prize got closer and the stakes were raised. Yet Markus Gisdol stood there impassively, as watchful and inscrutable as Professor Yaffle, the woodpecker bookend in Bagpuss. When the final whistle went, the facade melted like an ice sculpture in the Sahara.</p><p>Gisdol celebrated wildly, as his players did all over the Westfalen pitch, a mixture of relief and disbelief. The coach grabbed his assistant, Frank Kaspari, for a bear hug, then embraced Kingsley Ehizibue as he made it on to the field, with the defender seeming taken aback by the boss and his sudden eruption of joy. The wait, finally, was over.</p> <a href="https://www.theguardian.com/football/blog/2020/nov/30/koln-rejoice-miracle-of-dortmund-haaland-miss-bundesliga">Continue reading...</a>
-      Bundesliga
-      Cologne
-      Borussia Dortmund
-      Football
-      Sport
-      European club football
-      Mon, 30 Nov 2020 12:18:48 GMT
-      https://www.theguardian.com/football/blog/2020/nov/30/koln-rejoice-miracle-of-dortmund-haaland-miss-bundesliga
-      
-        Photograph: Martin Meissner/AP
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-        Photograph: Martin Meissner/AP
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-      Andy Brassell
-      2020-11-30T12:18:48Z
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-    
-      Hatem Ben Arfa rolls back the years for Bordeaux against PSG
-      https://www.theguardian.com/football/2020/nov/30/hatem-ben-arfa-rolls-back-years-bordeaux-psg-ligue-1
-      <p>Ben Arfa was brilliant at the weekend, setting up a goal and wreaking havoc against his old club</p><p>By <a href="https://twitter.com/_adam_white_?lang=en">Adam White</a> and <a href="https://twitter.com/ericdevin_">Eric Devin</a> for <a href="http://www.getfootballnewsfrance.com/">Get French Football News</a></p><p>Hatem Ben Arfa has some distance to go before he reaches the proverbial nine lives of a cat, but <a href="https://www.youtube.com/watch?v=HTudxAfayjo&amp;ab_channel=Ligue1UberEats">Bordeaux’s 2-2 draw with PSG</a> at the weekend offered evidence that he may be able to write another chapter to his career, even at the age of 33. There were hints that he was clicking into gear against Rennes last weekend, when <a href="https://youtu.be/7pKFKiElHYU?t=77">he scored the winner in a 1-0 victory over his former club</a>, and he was electric on Saturday in another of his old haunts, setting up a goal and generally wreaking havoc at the Parc des Princes.</p><p>Aside from an ill-fated sojourn at PSG, Ben Arfa has generally been impressive since his return to France from England five years ago. At Nice he formed a strike partnership with Valère Germain that was as unlikely as it was effective. Nice’s brand of attacking football under Claude Puel was scintillating but it seemed that, given their financial limitations, a realistic tilt at the Champions League places was beyond their means. As a result, Ben Arfa was soon lured to pastures greener, acknowledging as he did so the debt that he owed to the southern club, who had to wait some eight months for him to make his debut following a transfer imbroglio over his aborted spell with Hull City.</p> <a href="https://www.theguardian.com/football/2020/nov/30/hatem-ben-arfa-rolls-back-years-bordeaux-psg-ligue-1">Continue reading...</a>
-      Ligue 1
-      Football
-      Sport
-      European club football
-      Bordeaux
-      Mon, 30 Nov 2020 14:34:02 GMT
-      https://www.theguardian.com/football/2020/nov/30/hatem-ben-arfa-rolls-back-years-bordeaux-psg-ligue-1
-      
-        Photograph: Franck Fife/AFP/Getty Images
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-        Photograph: Franck Fife/AFP/Getty Images
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-      Adam White and Eric Devin
-      2020-11-30T14:34:02Z
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-      Manchester City's Ederson calls for concussion substitutes after David Luiz incident
-      https://www.theguardian.com/football/2020/nov/30/manchester-citys-ederson-calls-for-concussion-substitutes-after-david-luiz-incident
-      <ul><li>Arsenal defender clashed heads with Raúl Jiménez</li><li>‘You may feel ok but after the game you feel the consequences’</li></ul><p>Manchester City goalkeeper Ederson has called for the automatic replacement of players who are involved in a clash of heads after Raúl Jiménez suffered a fractured skull in a collision with David Luiz in Wolves’ <a href="https://www.theguardian.com/football/2020/nov/29/arsenal-wolves-premier-league-match-report">win at Arsenal</a> on Sunday.</p><p>While Jiménez was taken to hospital where <a href="https://www.theguardian.com/football/2020/nov/30/wolves-raul-jimenez-comfortable-surgery-fractured-skull-david-luiz-arsenal">he had surgery</a> immediately after the 15th-minute incident, David Luiz played on until halftime before being subsituted. In September 2017, Ederson was stretchered off during <a href="https://www.theguardian.com/football/2017/sep/09/manchester-city-liverpool-premier-league-match-report">City’s 5-0 win</a> at the Etihad Stadium following a high challenge from Liverpool’s Sadio Mané which required him to have eight stitches in his face</p> <a href="https://www.theguardian.com/football/2020/nov/30/manchester-citys-ederson-calls-for-concussion-substitutes-after-david-luiz-incident">Continue reading...</a>
-      Manchester City
-      Arsenal
-      Football
-      Sport
-      Wolverhampton Wanderers
-      Mon, 30 Nov 2020 14:50:13 GMT
-      https://www.theguardian.com/football/2020/nov/30/manchester-citys-ederson-calls-for-concussion-substitutes-after-david-luiz-incident
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-        Photograph: David Price/Arsenal FC/Getty Images
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-        Photograph: David Price/Arsenal FC/Getty Images
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-      Jamie Jackson
-      2020-11-30T14:50:13Z
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-      Premier League: 10 talking points from the weekend's action
-      https://www.theguardian.com/football/blog/2020/nov/30/premier-league-10-talking-points-from-the-weekends-action
-      <p>Mikel Arteta does not seem to have a plan of attack, Everton’s defence must improve and Jürgen Klopp should pick his battles</p><p>Mikel Arteta has been the Arsenal manager for just under a year, and though some things have improved under him – attitude, intensity, FA Cup tally – it’s still not clear what the plan is, especially going forward. And while it is important to emphasise how much Thomas Partey is being missed, the feeling persists that it really doesn’t have to be like this. When Arteta was appointed, he was gifted half a season of minimal expectations, meaning he could experiment in order to find the personnel and formation best able to execute his ideals. But though he did brilliantly to win a trophy, he settled on very little, so his current position is not dissimilar to the one in which he began – except now, he is under pressure. Maybe it is time to give Bukayo Saka, Reiss Nelson and Joe Willock extended runs because really, how much worse – and more tedious – could things get? <strong>Daniel Harris</strong></p> <a href="https://www.theguardian.com/football/blog/2020/nov/30/premier-league-10-talking-points-from-the-weekends-action">Continue reading...</a>
-      Football
-      Sport
-      Premier League
-      Liverpool
-      Crystal Palace
-      Everton
-      Manchester City
-      Burnley
-      West Bromwich Albion
-      Arsenal
-      Chelsea
-      Leeds United
-      Manchester United
-      Mon, 30 Nov 2020 08:27:52 GMT
-      https://www.theguardian.com/football/blog/2020/nov/30/premier-league-10-talking-points-from-the-weekends-action
-      
-        Composite: Getty/PA/EPA
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-        Composite: Getty/PA/EPA
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-      Guardian sport
-      2020-11-30T08:27:52Z
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-      N'Golo Kanté is back where he belongs as Chelsea's Pac-Man in midfield | Jonathan Wilson
-      https://www.theguardian.com/football/2020/nov/29/pac-man-ngolo-kante-is-back-where-he-belongs-in-chelseas-midfield
-      <p>Gobbling up possession from a holding role is what the Chelsea midfielder does best, as he showed against Tottenham</p><p>A run of just two goals conceded in nine games is some riposte to the doubts about Chelsea’s defending. Some doubts remain, but given how exposed Frank Lampard’s side were to the break last season, when they had the worst defensive record of any Chelsea team in 23 years, stifling Tottenham, the kings of the counterattack, is not an achievement to be underplayed.</p><p> <span>Related: </span><a href="https://www.theguardian.com/football/2020/nov/29/chelsea-tottenham-premier-league-match-report">Tottenham return to top of table after José Mourinho frustrates Chelsea</a> </p> <a href="https://www.theguardian.com/football/2020/nov/29/pac-man-ngolo-kante-is-back-where-he-belongs-in-chelseas-midfield">Continue reading...</a>
-      N'Golo Kanté
-      Chelsea
-      Tottenham Hotspur
-      Football
-      Sport
-      Football tactics
-      Sun, 29 Nov 2020 20:41:42 GMT
-      https://www.theguardian.com/football/2020/nov/29/pac-man-ngolo-kante-is-back-where-he-belongs-in-chelseas-midfield
-      
-        Photograph: Justin Tallis/AFP/Getty Images
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-        Photograph: Justin Tallis/AFP/Getty Images
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-      Jonathan Wilson
-      2020-11-29T20:41:42Z
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-      Burdened by genius: Maradona reminds us how peaking young brings its problems | Vic Marks
-      https://www.theguardian.com/sport/blog/2020/nov/30/maradona-reminds-us-how-peaking-young-brings-its-problems
-      <p>Following a Mexico 86 or Headingley 81 can weigh hard on superstars, which might be why many top coaches come from a lower rung</p><p>The death of Diego Maradona provided another reminder that life is seldom simple for the sporting superstar. Maradona was <a href="https://www.theguardian.com/football/2020/nov/26/maradona-the-footballer-had-no-flaws-maradona-the-man-was-a-victim" title="">obviously a footballing genius</a>, whose later life was, by all accounts, chaotic. In 1986, at the age of 26, the brilliance of his feet, his vision and his deception led to Argentina winning the World Cup. It doesn’t get any better than that. And that surely is the problem.</p><p>It is not a common problem since it applies only to geniuses. Where do you go after scaling the highest peaks? This came to mind when I was <a href="https://www.theguardian.com/sport/2020/nov/24/ian-botham-covid-19-brexit-headingley-vic-marks" title="">chatting to Ian Botham</a> recently. How could he ever hope to surpass what he achieved in the second half of the summer of 1981 at the age of 25? Geoff Hurst was never going to score another hat-trick in a World Cup final after 1966. How can Ben Stokes ever play a better innings than the <a href="https://www.theguardian.com/sport/2019/aug/25/australia-ashes-england-third-test-ben-stokes-sensational" title="">one at Headingley in 2019</a>? In the same way, how could Maradona top what he did in Mexico in 1986? I suppose one could also speculate whether Elton John was haunted by the thought that he might never produce a better track than Your Song, which was released in 1970. If so, it did not stop him trying.</p> <a href="https://www.theguardian.com/sport/blog/2020/nov/30/maradona-reminds-us-how-peaking-young-brings-its-problems">Continue reading...</a>
-      Sport
-      Football
-      Cricket
-      Mon, 30 Nov 2020 08:00:01 GMT
-      https://www.theguardian.com/sport/blog/2020/nov/30/maradona-reminds-us-how-peaking-young-brings-its-problems
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-        Photograph: Rodrigo Abd/AP
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-        Photograph: Rodrigo Abd/AP
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-      Vic Marks
-      2020-11-30T08:00:01Z
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-      Leah Williamson leads way as England's defensive trio delight Phil Neville
-      https://www.theguardian.com/football/2020/nov/30/leah-williamson-leads-way-as-englands-defensive-trio-delight-phil-neville
-      <ul><li>Williamson, Wubben-Moy and Turner excel in back three </li><li>‘I love the system, we had the right players in the right positions’</li></ul><p>The seasoned England midfielder Jill Scott was full of praise for the attitude of the young players in the Lionesses’ latest training camp, after a back three of Manchester United’s Millie Turner and the Arsenal duo Lotte Wubben-Moy and Leah Williamson all impressed.</p><p>“They were brilliant,” said Scott. “The communication from behind made my job very easy today and technically they are such good players. One thing I like about all three of them is they are front-footed defenders and they want to put a tackle in.”</p> <a href="https://www.theguardian.com/football/2020/nov/30/leah-williamson-leads-way-as-englands-defensive-trio-delight-phil-neville">Continue reading...</a>
-      England women's football team
-      Football
-      Sport
-      Women's football
-      Arsenal Women
-      Mon, 30 Nov 2020 08:00:01 GMT
-      https://www.theguardian.com/football/2020/nov/30/leah-williamson-leads-way-as-englands-defensive-trio-delight-phil-neville
-      
-        Photograph: Paul Greenwood for The FA/Shutterstock
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-        Photograph: Paul Greenwood for The FA/Shutterstock
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-      Suzanne Wrack
-      2020-11-30T08:00:01Z
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-      José Mourinho says Tottenham 'just a pony' in title race after draw at Chelsea
-      https://www.theguardian.com/football/2020/nov/29/chelsea-tottenham-premier-league-match-report
-      <p>José Mourinho has previous for spoiling 1,000th game celebrations at Stamford Bridge. Remember what happened to Arsène Wenger when he brought up the landmark in charge of Arsenal here? Mourinho’s Chelsea beat them 6-0.</p><p>This was the occasion of Roman Abramovich’s 1,000th match as the Chelsea owner and it was a time to reflect on how much he has brought to the club. The Tottenham manager made sure that he could not celebrate with a victory which would have taken Chelsea to the top of the table.</p> <a href="https://www.theguardian.com/football/2020/nov/29/chelsea-tottenham-premier-league-match-report">Continue reading...</a>
-      Premier League
-      Chelsea
-      Tottenham Hotspur
-      Football
-      Sport
-      Sun, 29 Nov 2020 18:55:07 GMT
-      https://www.theguardian.com/football/2020/nov/29/chelsea-tottenham-premier-league-match-report
-      
-        Photograph: Matthew Childs/EPA
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-        Photograph: Matthew Childs/EPA
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-      David Hytner at Stamford Bridge
-      2020-11-29T18:55:07Z
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-      Papa Bouba Diop, Senegal's World Cup hero and FA Cup winner, dies aged 42
-      https://www.theguardian.com/football/2020/nov/29/papa-douba-diop-senegal-world-cup-hero-fulham-portsmouth-midfielder-dies-aged-42
-      <ul><li>Former midfielder dies after reportedly suffering long illness</li><li>Diop played for Fulham and Portsmouth after 2002 heroics</li></ul><p>Papa Bouba Diop, the former Senegal midfielder who scored the first goal of the 2002 World Cup against France<a href="https://www.theguardian.com/football/2002/jun/01/worldcupfootball2002.sport9">,</a> has died at the age of 42.</p><p>World football’s governing body posted a tribute to Diop on Twitter. “Fifa is saddened to learn of the passing of Senegal legend Papa Bouba Diop,” Sunday’s statement read. “Once a World Cup hero, always a World Cup hero.”</p> <a href="https://www.theguardian.com/football/2020/nov/29/papa-douba-diop-senegal-world-cup-hero-fulham-portsmouth-midfielder-dies-aged-42">Continue reading...</a>
-      Senegal football team
-      Football
-      Sport
-      Portsmouth
-      Fulham
-      West Ham United
-      Senegal
-      Africa
-      Sun, 29 Nov 2020 19:00:42 GMT
-      https://www.theguardian.com/football/2020/nov/29/papa-douba-diop-senegal-world-cup-hero-fulham-portsmouth-midfielder-dies-aged-42
-      
-        Photograph: Getty Images/Getty Images Sport
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-        Photograph: Getty Images/Getty Images Sport
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-      Reuters
-      2020-11-29T19:00:42Z
-    
-    
-      European roundup: Napoli and Lionel Messi pay tribute to Diego Maradona
-      https://www.theguardian.com/football/2020/nov/29/european-roundup-lionel-messi-pays-tribute-to-diego-maradona-in-barca-q
-      <ul><li>Napoli wear special kit for 4-0 home win over Roma</li><li>Messi pays tribute after scoring in Barcelona win</li></ul><p><strong>Napoli</strong> honoured the late Diego Maradona with an Argentina-style kit as they overcame <strong>Roma</strong> 4-0, hours after Lionel Messi paid a personal tribute to his compatriot as Barcelona beat Osasuna by the same scoreline.</p><p>Lorenzo Insigne put Napoli ahead with a sublime free kick worthy of Maradona himself in the 30th minute and celebrated by running halfway across the pitch to collect a shirt with the Napoli legend’s name on it. Insigne then set up Fabián Ruiz for the second goal at Stadio San Paulo, which is expected to be renamed in Maradona’s honour.</p> <a href="https://www.theguardian.com/football/2020/nov/29/european-roundup-lionel-messi-pays-tribute-to-diego-maradona-in-barca-q">Continue reading...</a>
-      European club football
-      Lionel Messi
-      Diego Maradona
-      Barcelona
-      Football
-      Sport
-      La Liga
-      Osasuna
-      Serie A
-      Udinese
-      Lazio
-      Sun, 29 Nov 2020 23:34:49 GMT
-      https://www.theguardian.com/football/2020/nov/29/european-roundup-lionel-messi-pays-tribute-to-diego-maradona-in-barca-q
-      
-        Photograph: Alessandro Garofalo/AP
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-        Photograph: Alessandro Garofalo/AP
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-      Reuters
-      2020-11-29T23:34:49Z
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-      Edinson Cavani on target as Manchester United fight back to beat Southampton
-      https://www.theguardian.com/football/2020/nov/29/southampton-manchester-united-premier-league-match-report
-      <p>The early signs, it has to be said, were not auspicious. As the whistle blew at the start of the second half, Manchester United’s substitute was still at the side of the pitch, fiddling with his bootlaces. On the game went, with Edinson Cavani still on his haunches adjusting his footwear. And with United 2-0 down against a bold and well-organised Southampton side, this little vignette felt like a microcosm of United’s afternoon: a ragged, expensive shambles.</p><p> <span>Related: </span><a href="https://www.theguardian.com/football/2020/nov/30/edinson-cavani-fa-investigation-social-media-instagram-post">Edinson Cavani could face three-game ban as FA investigates social media post</a> </p> <a href="https://www.theguardian.com/football/2020/nov/29/southampton-manchester-united-premier-league-match-report">Continue reading...</a>
-      Premier League
-      Southampton
-      Manchester United
-      Football
-      Sport
-      Sun, 29 Nov 2020 16:14:14 GMT
-      https://www.theguardian.com/football/2020/nov/29/southampton-manchester-united-premier-league-match-report
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-        Photograph: Robin Jones/Getty Images
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-        Photograph: Robin Jones/Getty Images
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-      Jonathan Liew at St Mary's Stadium
-      2020-11-29T16:14:14Z
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-      Newcastle cancel training with trip to Aston Villa in doubt amid Covid-19 fears
-      https://www.theguardian.com/football/2020/nov/29/newcastle-scrap-training-session-amid-fears-of-coronavirus-outbreak
-      <ul><li>Five people among players and staff are now self-isolating </li><li>‘It’s been a tough week,’ says manager Steve Bruce</li></ul><p>Newcastle United were forced to abandon plans to stage a first-team training session on Sunday amid fears they are in the grip of a coronavirus outbreak. Two more players have tested positive over the weekend, meaning five members of the first-team bubble – understood to be four players and one member of staff – are now self-isolating.</p><p>The situation has prompted another round of testing, with Steve Bruce waiting anxiously on the results as the club’s training ground is deep-cleaned. Already preparations for Friday night’s trip to Aston Villa have been disrupted and there now has to be some degree of doubt as to whether that Premier League fixture will take place.</p> <a href="https://www.theguardian.com/football/2020/nov/29/newcastle-scrap-training-session-amid-fears-of-coronavirus-outbreak">Continue reading...</a>
-      Newcastle United
-      Football
-      Sport
-      Aston Villa
-      Sun, 29 Nov 2020 17:59:54 GMT
-      https://www.theguardian.com/football/2020/nov/29/newcastle-scrap-training-session-amid-fears-of-coronavirus-outbreak
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-        Photograph: Daniel Leal-Olivas/AP
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-        Photograph: Daniel Leal-Olivas/AP
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-      Louise Taylor
-      2020-11-29T17:59:54Z
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-      Raúl Jiménez head injury overshadows Wolves' impressive win at Arsenal
-      https://www.theguardian.com/football/2020/nov/29/arsenal-wolves-premier-league-match-report
-      <p>The most important news to emerge from an unsettling evening was that Raúl Jiménez, who had been carried off on a stretcher with a head injury that left everyone inside the stadium fraught with worry, was conscious and responding to treatment by the time he reached hospital.</p><p>Nobody will want to see replays of the collision with David Luiz that held up this game for 10 minutes, and much less will those present want to relive the sound. The shockwave of anxiety that followed was palpable and, while clearer information about Jiménez’s prognosis will emerge in time, the relief when updates about his condition began to circulate was equally pronounced.</p> <a href="https://www.theguardian.com/football/2020/nov/29/arsenal-wolves-premier-league-match-report">Continue reading...</a>
-      Premier League
-      Arsenal
-      Wolverhampton Wanderers
-      Football
-      Sport
-      Sun, 29 Nov 2020 21:25:23 GMT
-      https://www.theguardian.com/football/2020/nov/29/arsenal-wolves-premier-league-match-report
-      
-        Photograph: Catherine Ivill/AP
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-        Photograph: Catherine Ivill/AP
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-      Nick Ames at the Emirates Stadium
-      2020-11-29T21:25:23Z
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-      Diego Maradona's personal doctor denies responsibility for death
-      https://www.theguardian.com/football/2020/nov/29/argentine-prosecutors-investigate-death-of-diego-maradona
-      <p>Leopoldo Luque in tears after officials search his home and office in Buenos Aires</p><p>Diego Maradona’s personal physician has denied responsibility for the former footballer’s death after police raided his home and surgery on Sunday, seizing laptops, medical records and mobile devices.</p><p>Argentinian media reported that police were trying to establish if there was negligence in Maradona’s treatment and that searches of premises belonging to the neurosurgeon Leopoldo Luque were carried out as part of an investigation into involuntary manslaughter.</p> <a href="https://www.theguardian.com/football/2020/nov/29/argentine-prosecutors-investigate-death-of-diego-maradona">Continue reading...</a>
-      Diego Maradona
-      Argentina
-      Americas
-      World news
-      Football
-      Sport
-      Sun, 29 Nov 2020 18:52:39 GMT
-      https://www.theguardian.com/football/2020/nov/29/argentine-prosecutors-investigate-death-of-diego-maradona
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-        Photograph: Diego Maradona press office/AFP/Getty Images
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-        Photograph: Diego Maradona press office/AFP/Getty Images
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-      Tom Phillips Latin America correspondent
-      2020-11-29T18:52:39Z
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-      In Naples, a city steeped in pagan rites, I saw Maradona cast his spell
-      https://www.theguardian.com/football/2020/nov/29/in-naples-a-city-steeped-in-pagan-rites-i-saw-maradona-cast-his-spell
-      <p>A personal memoir of the passionate, intense bond between the Argentinian and his adopted city, which saw his triumphs and his tragic downfall</p><p>On the evening that Napoli football club won its first ever championship, in May 1987, a graffito was scrawled on the wall of Poggioreale cemetery, on the city’s shoulders as it rises from the gulf of Naples: “<em>Voi non sapete che cosa vi siete persi</em>” – roughly, you don’t know what you missed. Overnight came the reply: “<em>E chi u l’he detto</em>?” – who told you we missed it?</p><p>This is how Naples thinks, and holds in its heart the mercurial genius largely responsible for winning that scudetto: Diego Maradona. And now that <a href="https://www.theguardian.com/world/2020/nov/26/naples-mourns-death-of-diego-maradona-their-little-rascal" title="">“Dieguito” himself has passed over to the other side</a>, the city’s head of cemeteries, Alessio Castiello, and funeral director Gennaro Tammaro, urge the city to commission and erect a statue of, and monument to, Maradona.</p> <a href="https://www.theguardian.com/football/2020/nov/29/in-naples-a-city-steeped-in-pagan-rites-i-saw-maradona-cast-his-spell">Continue reading...</a>
-      Diego Maradona
-      Napoli
-      Argentina
-      Italy
-      Football
-      Sport
-      Sun, 29 Nov 2020 09:00:23 GMT
-      https://www.theguardian.com/football/2020/nov/29/in-naples-a-city-steeped-in-pagan-rites-i-saw-maradona-cast-his-spell
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-        Photograph: Alessandro Garofalo/AP
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-        Photograph: Alessandro Garofalo/AP
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-      Ed Vulliamy
-      2020-11-29T09:00:23Z
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-      Echoing stadium may be a wintry void but Arsenal fans cannot wait to return
-      https://www.theguardian.com/football/2020/nov/29/echoing-stadium-may-be-a-wintry-void-but-arsenal-fans-cannot-wait-to-return
-      <p>There is a sense of building excitement as Arsenal prepare to become the first elite club to open their turnstiles since March</p><p>Never has a Europa League dead rubber carried this much weight. Arsenal can do more or less as they please when Rapid Vienna visit on Thursday, with qualification from Group B assured and top spot almost certain. They might, however, feel possessed to play out of their skins. In normal times this would be the toughest sell of the season but, for many of the 2,000 supporters who will be allowed inside the Emirates, its meaning is beyond measure.</p><p>“A historic moment for the club,” is how Arsenal hailed the reopening of their turnstiles, which will click back into action before those at any other top-flight club owing to the calendar’s quirks. It will come almost nine months after <a href="https://www.theguardian.com/football/2020/mar/07/arsenal-west-ham-premier-league-match-report">Arsenal beat West Ham with a goal from Alexandre Lacazette</a>. That brought a near-capacity house down but then football fell silent; its return in front of gaping, windswept stands has felt decidedly strange but now a slice of what makes the sport beautiful is coming back, too.</p> <a href="https://www.theguardian.com/football/2020/nov/29/echoing-stadium-may-be-a-wintry-void-but-arsenal-fans-cannot-wait-to-return">Continue reading...</a>
-      Arsenal
-      Europa League
-      Premier League
-      Football
-      Sport
-      Sun, 29 Nov 2020 08:00:22 GMT
-      https://www.theguardian.com/football/2020/nov/29/echoing-stadium-may-be-a-wintry-void-but-arsenal-fans-cannot-wait-to-return
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-        Photograph: Toby Melville/Reuters
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-        Photograph: Toby Melville/Reuters
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-      Nick Ames
-      2020-11-29T08:00:22Z
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-      It was Maradona's defiance that most inspired me | Kenan Malik
-      https://www.theguardian.com/commentisfree/2020/nov/29/it-was-maradonas-defiance-that-most-inspired-me
-      <p>Footballing genius aside, his greatness lay in transcending his all too human flaws</p><p>I can still remember where I was when, in 1986, Diego Maradona scored with the <a href="https://www.theguardian.com/football/video/2020/nov/25/diego-maradona-recounts-hand-of-god-incident-2006-bbc-interview-video">“hand of God”</a>. In a flat on the Coventry Cross estate, in east London. An Asian family lived there, one of a handful on the estate, who had faced vicious racist attacks. An England-Argentina game, just four years after the Falklands conflict, was a threatening proposition. I was part of a group that organised support for besieged black families. So, there I was, with half an eye on what might be happening outside, <a href="https://www.youtube.com/watch?v=-ccNkksrfls">watching Maradona destroy England</a>, first with his left hand and then, four minutes later, by <a href="https://www.fifa.com/worldcup/videos/maradona-greatest-goal-1986-fifa-world-cup-england-days-brazil-2296355">slaloming through the England team</a>, finding space where none should have been, scoring possibly the most audacious goal in World Cup history.</p><p>Amid the praise heaped upon Maradona over the past week, it’s easy to forget how despised he was in Britain in those days. Or why many, like me, took to him because he was so despised. He was to football <a href="https://kenanmalik.com/2016/06/05/i-dont-have-to-be-what-you-want-me-to-be/">as Muhammad Ali had been to boxing</a>.</p> <a href="https://www.theguardian.com/commentisfree/2020/nov/29/it-was-maradonas-defiance-that-most-inspired-me">Continue reading...</a>
-      Diego Maradona
-      Sport
-      World news
-      Football
-      Race
-      UK news
-      Sun, 29 Nov 2020 06:45:21 GMT
-      https://www.theguardian.com/commentisfree/2020/nov/29/it-was-maradonas-defiance-that-most-inspired-me
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-        Photograph: Ricardo Moraes/Reuters
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-        Photograph: Ricardo Moraes/Reuters
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-      Kenan Malik
-      2020-11-29T06:45:21Z
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-      Being there: football fans in stadiums – buy a Guardian photograph
-      https://www.theguardian.com/football/gallery/2020/nov/28/being-there-football-fans-in-stadiums-buy-a-guardian-photograph
-      <p><strong>Guardian sports photographer Tom Jenkins has been attending many top level matches behind closed doors since football restarted and like many of us keeps thinking about the lack of fans at the games. To mark their significance the Guardian is offering readers the chance to buy exclusive prints from Tom’s archive featuring fans attending historical games over the years. A percentage of profits from every print sold will be donated to the charity <a href="https://www.footballbeyondborders.org/">Football Beyond Borders</a>, an education charity that supports disadvantaged young people in the UK.</strong></p><ul><li><strong><a href="https://guardianprintshop.com/collections/remembering-football-fans-in-stadiums">Buy your print here</a> for just £55 including free delivery (30x40cm print size).</strong></li><li><strong>Photographs are presented on museum-grade, fine-art paper stocks and are dispatched via Royal Mail. Global shipping is available. Email: <a href="mailto:guardianprintsales@theprintspace.co.uk">guardianprintsales@theprintspace.co.uk</a></strong></li></ul> <a href="https://www.theguardian.com/football/gallery/2020/nov/28/being-there-football-fans-in-stadiums-buy-a-guardian-photograph">Continue reading...</a>
-      Football
-      Sport
-      Premier League
-      Championship
-      Art and design
-      Photography
-      Sat, 28 Nov 2020 11:08:30 GMT
-      https://www.theguardian.com/football/gallery/2020/nov/28/being-there-football-fans-in-stadiums-buy-a-guardian-photograph
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-        Photograph: Tom Jenkins/The Guardian
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-        Photograph: Tom Jenkins/The Guardian
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-      Tom Jenkins
-      2020-11-28T11:08:30Z
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-      Tony Pulis: 'I'm softer but there’s still that little streak in me'
-      https://www.theguardian.com/football/2020/nov/27/tony-pulis-sheffield-wednesday-manager-i-am-softer-but-there-is-still-that-little-streak-in-me
-      <p>Sheffield Wednesday’s first goal under their new manager was from a set piece but there is more to him, says a former player</p><p>Perhaps it was inevitable the first goal of the Tony Pulis era at Sheffield Wednesday would stem from a superbly executed set piece. Adam Reach racing from back post to penalty spot to sidefoot home Barry Bannan’s corner may do little to enhance Brand Pulis but, then again, a manager pigeonholed as a firefighter has long given up trying to alter perceptions.</p><p>Between <a href="https://www.theguardian.com/football/2019/may/17/tony-pulis-leaves-middlesbrough-manager">departing Middlesbrough 18 months ago</a> and returning to the game to again succeed Garry Monk, this time at Hillsborough, a fortnight ago Pulis spent time swotting up on the Antarctic explorer Sir Ernest Shackleton – “I don’t like fiction, I like reading proper history,” he says – and went to Corsica (to celebrate Napoleon’s 250th birthday), California and visited <a href="https://www.theguardian.com/global/2009/apr/21/david-smith-rorkes-drift-africa">Rorke’s Drift in South Africa</a>.</p> <a href="https://www.theguardian.com/football/2020/nov/27/tony-pulis-sheffield-wednesday-manager-i-am-softer-but-there-is-still-that-little-streak-in-me">Continue reading...</a>
-      Tony Pulis
-      Sheffield Wednesday
-      Championship
-      Football
-      Sport
-      Fri, 27 Nov 2020 13:19:07 GMT
-      https://www.theguardian.com/football/2020/nov/27/tony-pulis-sheffield-wednesday-manager-i-am-softer-but-there-is-still-that-little-streak-in-me
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-        Photograph: Athena Pictures/Getty Images
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-        Photograph: Athena Pictures/Getty Images
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-      Ben Fisher
-      2020-11-27T13:19:07Z
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-      Director Asif Kapadia: ‘Diego and Maradona were two different people’
-      https://www.theguardian.com/football/2020/nov/27/diego-and-maradona-were-two-different-people
-      <p>Film director recalls the long and rocky road to meeting the mercurial subject of his film</p><p>Football is a huge part of my life. I was 14 when <a href="https://www.theguardian.com/football/2020/nov/25/diego-maradona-obituary">Diego Maradona</a> scored the two goals against England – the hand of God and the wonder goal. Despite the first goal, I always thought he was the best player in the world. I’ve always been a fan of outsiders, rebels.</p><p>Everyone wanted to be Maradona. He was the global phenomenon. The pope wanted to meet him. Fidel Castro would sit and listen to Diego tell a story.</p> <a href="https://www.theguardian.com/football/2020/nov/27/diego-and-maradona-were-two-different-people">Continue reading...</a>
-      Diego Maradona
-      Asif Kapadia
-      Argentina
-      Americas
-      Film
-      Culture
-      Football
-      Sport
-      World news
-      Fri, 27 Nov 2020 17:03:45 GMT
-      https://www.theguardian.com/football/2020/nov/27/diego-and-maradona-were-two-different-people
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-        Photograph: Leo la Valle/EPA
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-        Photograph: Leo la Valle/EPA
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-      Asif Kapadia was talking to Simon Hattenstone
-      2020-11-27T17:03:45Z
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-      'We cannot change it': Klopp frustrated as VAR controversy denies Liverpool – video
-      https://www.theguardian.com/football/video/2020/nov/28/we-cannot-change-it-klopp-frustrated-as-var-controversy-denies-liverpool-win-video
-      <p>Mohamed Salah and Saido Mane both had goals disallowed by the video referee, before referee Stuart Attwell awarded Brighton a late penalty after reviewing contact between Andy Robertson and Danny Welbeck on his monitor. Pascal Gross struck home the penalty to salvage a 1-1 draw for the home side.</p><p>'It's a penalty because the ref whistled it,' said Klopp, who had sarcastically clapped the assistant referee when the penalty was called. 'When the ref thinks it's enough, we cannot change that.'</p><ul><li><a href="https://www.theguardian.com/football/2020/nov/28/brighton-liverpool-premier-league-match-report">Pascal Gross hits late penalty after VAR drama as Brighton deny Liverpool</a></li><li><a href="https://www.theguardian.com/football/live/2020/nov/28/brighton-v-liverpool-premier-league-live">Brighton 1-1 Liverpool: Premier League – as it happened&nbsp;</a></li></ul> <a href="https://www.theguardian.com/football/video/2020/nov/28/we-cannot-change-it-klopp-frustrated-as-var-controversy-denies-liverpool-win-video">Continue reading...</a>
-      Liverpool
-      Video assistant referees (VARs)
-      Jürgen Klopp
-      Premier League
-      Football
-      Sport
-      Sat, 28 Nov 2020 17:05:33 GMT
-      https://www.theguardian.com/football/video/2020/nov/28/we-cannot-change-it-klopp-frustrated-as-var-controversy-denies-liverpool-win-video
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-        Photograph: AP
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-        Photograph: AP
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-      2020-11-28T17:05:33Z
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-      Gary Lineker: Maradona was 'like a messiah' in Argentina – video
-      https://www.theguardian.com/football/video/2020/nov/27/gary-lineker-maradona-was-like-a-messiah-in-argentina-video
-      <p>Former England forward Gary Lineker paid tribute to Argentina's legendary footballer Diego Maradona, who died on Wednesday at the age of 60 after he suffered heart failure at his home in Buenos Aires. Lineker, who played against the Argentine in the 1986 World Cup, said Maradona was understandably 'treated almost like a messiah' in Argentina because he was one of, if not, the greatest footballer of all time.&nbsp; Maradona led his country to World Cup glory in Mexico in 1986 with Argentina and won multiple honours with club sides Barcelona and Napoli – where he is also revered. On Wednesday, Lineker had split opinion when he referenced Maradona's infamous 'Hand of God' goal which knocked England out of the 1986 World Cup quarter-final when <a href="https://twitter.com/GaryLineker/status/1331637479640870917?s=20">paying his respects on Twitter</a>.</p><ul><li><a href="https://www.theguardian.com/football/2020/nov/27/brave-intelligent-diego-maradona-was-a-man-who-moved-through-different-air">Brave, intelligent Diego Maradona was a man who moved through different air</a></li></ul> <a href="https://www.theguardian.com/football/video/2020/nov/27/gary-lineker-maradona-was-like-a-messiah-in-argentina-video">Continue reading...</a>
-      Diego Maradona
-      Gary Lineker
-      Argentina
-      Sport
-      Fri, 27 Nov 2020 12:34:12 GMT
-      https://www.theguardian.com/football/video/2020/nov/27/gary-lineker-maradona-was-like-a-messiah-in-argentina-video
-      
-        Photograph: AP / Getty
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-        Photograph: AP / Getty
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-      2020-11-27T12:34:12Z
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-      'Happiness, sadness, rebellion': what Maradona meant to his fans – video
-      https://www.theguardian.com/football/video/2020/nov/26/happiness-sadness-rebellion-what-maradona-meant-to-his-fans-video
-      <p>'Yesterday not only he died. Yesterday the entire city of Naples died together with Argentina.' Fans across the world are mourning the loss of footballing legend Diego Maradona. 'Diego was rebellion. You had to feel Diego,' said one fan. Groups of supporters gathered in Naples and Buenos Aires to show impassioned support for the Argentinian icon.&nbsp;</p><ul><li><a href="https://www.theguardian.com/football/video/2020/nov/25/remembering-diego-maradona-football-legend-dies-aged-60-video-obituary">Remembering Diego Maradona: football legend dies aged 60 – video obituary</a></li></ul> <a href="https://www.theguardian.com/football/video/2020/nov/26/happiness-sadness-rebellion-what-maradona-meant-to-his-fans-video">Continue reading...</a>
-      Diego Maradona
-      Football
-      Napoli
-      Argentina
-      Argentina
-      Sport
-      Thu, 26 Nov 2020 15:13:20 GMT
-      https://www.theguardian.com/football/video/2020/nov/26/happiness-sadness-rebellion-what-maradona-meant-to-his-fans-video
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-        Photograph: EPA
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-        Photograph: EPA
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-      Nicholas Williams
-      2020-11-26T15:13:20Z
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-      'The greatest idol': football world pays tribute to Diego Maradona – video
-      https://www.theguardian.com/football/video/2020/nov/26/the-greatest-idol-football-world-pays-tribute-to-diego-maradona-video
-      <p>The footballing world has paid tribute to Diego Maradona after his death at the age of 60. 'The amount of joy and pleasure and his commitment for world football. He made world football better,' said Manchester City manager Pep Guardiola. Maradona's former teammate Jorge Valdano broke down and could hardly speak when asked about his memories of the Argentinian legend</p><ul><li><a href="https://www.theguardian.com/football/blog/2020/nov/26/the-best-and-worst-moments-of-diego-maradona-turbulent-career">The best and worst moments of Diego Maradona’s turbulent career</a></li></ul> <a href="https://www.theguardian.com/football/video/2020/nov/26/the-greatest-idol-football-world-pays-tribute-to-diego-maradona-video">Continue reading...</a>
-      Diego Maradona
-      Football
-      Sport
-      Zinedine Zidane
-      Pep Guardiola
-      Diego Simeone
-      Jürgen Klopp
-      Thu, 26 Nov 2020 11:00:27 GMT
-      https://www.theguardian.com/football/video/2020/nov/26/the-greatest-idol-football-world-pays-tribute-to-diego-maradona-video
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-        Photograph: Movistar, Grid
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-        Photograph: Movistar, Grid
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-      Nicholas Williams
-      2020-11-26T11:00:27Z
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-      Remembering Diego Maradona: football legend dies aged 60 – video obituary
-      https://www.theguardian.com/football/video/2020/nov/25/remembering-diego-maradona-football-legend-dies-aged-60-video-obituary
-      <p><a href="https://www.theguardian.com/football/diego-maradona">Diego Maradona</a>, widely regarded as one of the greatest football players of all time, has died at the age of 60.</p><p>Maradona&nbsp;was a uniquely gifted player with a bewitching style that was all his own. He rose from the streets of Buenos Aires to reach the pinnacle of his sport leading Argentina to World Cup glory in 1986 and scoring the infamous '<a href="https://www.theguardian.com/football/blog/2014/apr/08/world-cup-moments-maradona-hand-god">Hand of God'</a>&nbsp;goal along the way. Maradona played for Boca Juniors, Barcelona and Napoli in a storied club career, and later managed Argentina at the 2010 World Cup.</p><p>Declaring three days of national mourning, Alberto Fernández, the president of Argentina, said: 'You took us to the top of the world. You made us immensely happy. You were the greatest of them all'.<br></p><ul><li><a href="https://www.theguardian.com/football/2020/nov/25/diego-maradona-dies-one-of-greatest-footballers-of-all-time-argentina-world-cup-1986">Diego Maradona, one of the greatest footballers of all time, dies aged 60</a></li><li><a href="https://www.theguardian.com/football/live/2020/nov/25/diego-maradona-dies-aged-60-latest-tributes-and-reaction">Maradona dies aged 60: tributes and reaction – latest updates</a></li></ul> <a href="https://www.theguardian.com/football/video/2020/nov/25/remembering-diego-maradona-football-legend-dies-aged-60-video-obituary">Continue reading...</a>
-      Diego Maradona
-      Football
-      Argentina
-      Sport
-      Argentina
-      World news
-      Wed, 25 Nov 2020 19:57:06 GMT
-      https://www.theguardian.com/football/video/2020/nov/25/remembering-diego-maradona-football-legend-dies-aged-60-video-obituary
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-        Photograph: Getty Images
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-        Photograph: Getty Images
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-      Hayden Vernon, Nicholas Williams & Nikhita Chulani
-      2020-11-25T19:57:06Z
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-      Fans in Argentina and Naples mourn death of Diego Maradona – video
-      https://www.theguardian.com/football/video/2020/nov/26/fans-in-argentina-and-naples-mourn-death-of-diego-maradona-video
-      <p>Football fans have gathered in large numbers in Diego Maradona's home country of Argentina after the icon of the sport died aged 60. Supporters congregated across Maradona's home city of Buenos Aires to pay tribute to the World Cup-winning captain. The Argentinian was also mourned in Naples where he spent seven seasons playing for Napoli</p><ul><li><a href="https://www.theguardian.com/football/2020/nov/25/diego-maradona-dies-one-of-greatest-footballers-of-all-time-argentina-world-cup-1986">Diego Maradona, one of the greatest footballers of all time, dies aged 60</a></li><li><a href="https://www.theguardian.com/football/2020/nov/25/diego-maradona-obituary">Diego Maradona obituary</a></li><li><a href="https://www.theguardian.com/football/live/2020/nov/25/diego-maradona-dies-aged-60-latest-tributes-and-reaction">Maradona dies aged 60: tributes and reaction – latest updates</a></li></ul> <a href="https://www.theguardian.com/football/video/2020/nov/26/fans-in-argentina-and-naples-mourn-death-of-diego-maradona-video">Continue reading...</a>
-      Diego Maradona
-      Football
-      Argentina
-      Sport
-      World news
-      Italy
-      Thu, 26 Nov 2020 01:53:39 GMT
-      https://www.theguardian.com/football/video/2020/nov/26/fans-in-argentina-and-naples-mourn-death-of-diego-maradona-video
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-        Photograph: Getty Images
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-        Photograph: Getty Images
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-      2020-11-26T01:53:39Z
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-      Diego Simeone's new and improved Atlético Madrid on title trail again | Sid Lowe
-      https://www.theguardian.com/football/blog/2020/nov/24/diego-simeones-new-and-improved-atletico-madrid-on-title-trail-again
-      <p>This is not a repeat of 2013-14: Atlético are playing expansive football and cemented their favourites tag against Barcelona</p><p>Late on Saturday night, not long after his players had beaten Barcelona 1-0, immortalised a moment even they hadn’t experienced before with <a href="https://twitter.com/marcosllorente/status/1330271609085636611’s=20">a celebratory photo</a> taken in <a href="https://www.theguardian.com/football/blog/2020/oct/01/were-robbing-everyone-la-liga-left-to-lament-lost-dressing-rooms">an improvised recovery room</a> beneath the stands and headed home still in their kit, Diego Simeone sat in an empty auditorium and started another Zoom call. A series of journalists appeared on screen, taking it in turns attempting to get him to say Atlético Madrid are going to win the league. Or at least try to. One by one they failed, until somebody asked if he saw any similarities between this team and the one that <a href="https://www.theguardian.com/football/blog/2014/may/19/atletico-madrid-la-liga-title-impossible">took the title</a> six years ago. “No,” he said, “I don’t.”</p><p><em>Next</em>.</p> <a href="https://www.theguardian.com/football/blog/2020/nov/24/diego-simeones-new-and-improved-atletico-madrid-on-title-trail-again">Continue reading...</a>
-      La Liga
-      European club football
-      Football
-      Sport
-      Atlético Madrid
-      Tue, 24 Nov 2020 08:00:15 GMT
-      https://www.theguardian.com/football/blog/2020/nov/24/diego-simeones-new-and-improved-atletico-madrid-on-title-trail-again
-      
-        Photograph: Gabriel Bouys/AFP/Getty Images
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-        Photograph: Gabriel Bouys/AFP/Getty Images
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-      Sid Lowe
-      2020-11-24T08:00:15Z
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-      Ageless Zlatan Ibrahimovic continues to take care of business for Milan | Nicky Bandini
-      https://www.theguardian.com/football/2020/nov/23/ageless-zlatan-ibrahimovic-continues-to-take-care-of-business-for-milan-serie-a
-      <p>Milan’s senior coaching staff had to watch from home but the 39-year-old did their job for them at Napoli</p><p>Stefano Pioli was working from home last week. A common experience for many people in 2020, but not a straightforward adjustment when your job is to manage a football club that sits top of its domestic league. Trickier still when you are trying to prepare for an away game at a ground where your team has not won in more than a decade.</p><p>Instead of leading Milan’s training sessions ahead of their visit to Napoli on Sunday, Pioli plonked himself in front of a monitor and watched a live stream sent back from a drone. He communicated observations to a member of the club’s analytics team stationed beside the pitch. They, in turn, would run the information over to Daniele Bonera – the manager’s third-in-command.</p> <a href="https://www.theguardian.com/football/2020/nov/23/ageless-zlatan-ibrahimovic-continues-to-take-care-of-business-for-milan-serie-a">Continue reading...</a>
-      Serie A
-      Milan
-      Zlatan Ibrahimovic
-      Football
-      European club football
-      Sport
-      Napoli
-      Mon, 23 Nov 2020 14:22:36 GMT
-      https://www.theguardian.com/football/2020/nov/23/ageless-zlatan-ibrahimovic-continues-to-take-care-of-business-for-milan-serie-a
-      
-        Photograph: Salvatore Laporta/IPA/REX/Shutterstock
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-        Photograph: Salvatore Laporta/IPA/REX/Shutterstock
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-      Nicky Bandini
-      2020-11-23T14:22:36Z
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-      Werder Bremen set niche Bundesliga record in Bayern game that got away
-      https://www.theguardian.com/football/blog/2020/nov/23/werder-bremen-set-niche-bundesliga-record-in-bayern-game-that-got-away
-      <p>Florian Kohfeldt’s team might have beaten Bayern. They also might have lost but settled for their now habitual 1-1 scoreline </p><p>Werder Bremen arrived in Munich with the possibility of setting a peculiar record. Having drawn 1-1 in their last four Bundesliga meetings, they had equalled a 40-year-old run by Bayer Leverkusen and had, in theory at least, the opportunity to supplant <em>Die Werkself </em>in the most niche of trivia categories. With Bayern to face, nobody thought it would actually happen, of course.</p><p> <span>Related: </span><a href="https://www.theguardian.com/football/2020/nov/23/fifa-bans-head-of-african-football-for-five-years-after-misappropriation-of-funds">Fifa bans head of African football for five years after misappropriation of funds</a> </p> <a href="https://www.theguardian.com/football/blog/2020/nov/23/werder-bremen-set-niche-bundesliga-record-in-bayern-game-that-got-away">Continue reading...</a>
-      Bundesliga
-      Werder Bremen
-      Bayern Munich
-      European club football
-      Football
-      Sport
-      Mon, 23 Nov 2020 11:43:42 GMT
-      https://www.theguardian.com/football/blog/2020/nov/23/werder-bremen-set-niche-bundesliga-record-in-bayern-game-that-got-away
-      
-        Photograph: Lukas Barth/AP
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-        Photograph: Lukas Barth/AP
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-      Andy Brassell
-      2020-11-23T11:43:42Z
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-      Rebranded Football Australia forecasts $7.3m loss as Covid hits game hard
-      https://www.theguardian.com/sport/2020/nov/25/football-federation-australia-agm-financial-results
-      <ul><li>FA reports $1.814m loss in financial year 2019-20</li><li>Worse outlook for next year as a result of broadcast deal </li></ul><p>Football Federation Australia insists its rebranding will not cost a cent as it prepares for a loss of $7.3m for the 2020-21 financial year. FFA will now be known as Football Australia (FA), with the name change voted in at Wednesday’s Annual General Meeting.</p><p>FA announced it had lost $1.814m in the 2019-20 financial year, with the loss of crowds due to the Covid-19 pandemic and a significant reduction in the A-League broadcast deal stripping the governing body of millions of dollars.</p> <a href="https://www.theguardian.com/sport/2020/nov/25/football-federation-australia-agm-financial-results">Continue reading...</a>
-      Football Australia
-      A-League
-      W-League
-      Football
-      Australia sport
-      Sport
-      Wed, 25 Nov 2020 03:43:33 GMT
-      https://www.theguardian.com/sport/2020/nov/25/football-federation-australia-agm-financial-results
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-        Photograph: Mick Tsikas/AAP
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-        Photograph: Mick Tsikas/AAP
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-      Australian Associated Press
-      2020-11-25T03:43:33Z
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-    
-      Atlético Madrid's Carrasco punishes Ter Stegen to increase Barcelona woe
-      https://www.theguardian.com/football/2020/nov/21/atletico-madrid-barcelona-la-liga-match-report
-      <p>Atlético Madrid punished a mistake by the Barcelona goalkeeper Marc-André Ter Stegen to close out a 1-0 win at the Wanda Metropolitano. The German had a rush of blood in first-half stoppage time when he raced up field to try to shutdown a counterattack, only to be left red-faced as Yannick Carrasco took the ball around him and knocked it into the empty net.</p><p> <span>Related: </span><a href="https://www.theguardian.com/football/2020/nov/21/european-roundup-bayern-munich-real-madrid-juventus">European roundup: Real Madrid and Bayern both held to lacklustre draws</a> </p> <a href="https://www.theguardian.com/football/2020/nov/21/atletico-madrid-barcelona-la-liga-match-report">Continue reading...</a>
-      La Liga
-      Atlético Madrid
-      Barcelona
-      European club football
-      Football
-      Sport
-      Sat, 21 Nov 2020 22:22:25 GMT
-      https://www.theguardian.com/football/2020/nov/21/atletico-madrid-barcelona-la-liga-match-report
-      
-        Photograph: Gabriel Bouys/AFP/Getty Images
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-        Photograph: Gabriel Bouys/AFP/Getty Images
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-      PA Media
-      2020-11-21T22:22:25Z
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-      Germany's low ebb under Löw put into sharp focus by Spain's six-shooters | Sid Lowe
-      https://www.theguardian.com/football/blog/2020/nov/18/germany-joachim-low-sharp-focus-spain-six-nations-league
-      <p>Germany’s worst result since 1931 raises more questions about the coach’s methods and the players he has frozen out </p><p>The fourth official was preparing to raise the board one last time when Luis Enrique drew up close and said something. Even in the quiet of an empty arena the Spain manager couldn’t be heard clearly, but he seemed to be suggesting there was no need to add any more time. Germany had had enough, and long ago. To his left, Joachim Löw sat in the shadow of the bench, not moving, which is how he had sat for most of the night. Above him, Oliver Bierhoff, the national team director, watched in silence.</p><p>The board went up. “OK, two minutes,” someone could be heard saying, almost apologetically and in Spanish. In German, no one said anything. There was just … <em>nothing</em>. Löw rested his hand on his chin, defeated and distant. On the scoreboard, which the few people in this arena photographed for posterity, <a href="https://www.theguardian.com/football/2020/nov/17/ferran-torress-treble-helps-spain-humble-germany-in-nations-league">it said 6-0</a>. The list of Spain’s goalscorers filled the screen. Through the silence, radio broadcasters shouted about a historic night.</p> <a href="https://www.theguardian.com/football/blog/2020/nov/18/germany-joachim-low-sharp-focus-spain-six-nations-league">Continue reading...</a>
-      Germany
-      Spain
-      Nations League
-      Football
-      Sport
-      Wed, 18 Nov 2020 15:46:03 GMT
-      https://www.theguardian.com/football/blog/2020/nov/18/germany-joachim-low-sharp-focus-spain-six-nations-league
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-        Photograph: Action Press/REX/Shutterstock
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-        Photograph: Action Press/REX/Shutterstock
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-      Sid Lowe
-      2020-11-18T15:46:03Z
-    
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-      Tite's results show he is the best man to lead Brazil to the World Cup
-      https://www.theguardian.com/football/2020/nov/19/tites-results-show-he-is-the-best-man-to-lead-brazil-to-the-world-cup
-      <p>There are always murmurs in Brazil about replacing the manager, but his record in the job is extremely impressive</p><p>By <a href="https://twitter.com/TomSandersonBCN">Tom Sanderson</a> for <a href="https://ygfoot.com/">Yellow and Green Football</a></p><p>Injuries and Covid-19 threatened to derail Brazil in their two World Cup qualifiers in the international break, relieving Tite of no fewer than nine players, yet the Seleção finished off 2020 in perfect fashion, beating Uruguay 2-0 in Montevideo on Tuesday night after seeing off Venezuela in São Paulo three days earlier. After four rounds of fixtures in South American qualifying, Brazil are the only team with a 100% record. There is a long way to go – South American sides have to play 18 qualifiers in their 10-team group – but, after scoring 12 goals and conceding just two in four straight wins, things are looking good for Tite.<br><br>Brazil had not lost to Uruguay since 2001, so should have been confident as they made their way across the border. Yet, following a limp 1-0 win over Venezuela on Friday, some fans were suggesting that the national team may be better off with a different manager. Those mutterings have only increased since their defeat to Belgium in the World Cup quarter-finals in 2018, so Tite needed his team to make a statement.</p><p>They did. Although this wasn’t the 4-1 rout of Uruguay that Brazil enjoyed in 2017 – when Edinson Cavani gave the hosts an early lead from the penalty spot before Brazil roared back thanks to a Paulinho hat-trick and delightful scooped lob from Neymar – the performance reaffirmed Tite’s credentials. Some supporters would prefer to see Flamengo manager Rogério Ceni or São Paulo boss Fernando Diniz in charge, but neither of them has a CV to rival Tite, who won the Brazilian league title, the Copa Libertadores and Fifa Club World Cup while in charge of Corinthians.</p> <a href="https://www.theguardian.com/football/2020/nov/19/tites-results-show-he-is-the-best-man-to-lead-brazil-to-the-world-cup">Continue reading...</a>
-      Brazil
-      Football
-      Sport
-      World Cup
-      Uruguay
-      Thu, 19 Nov 2020 12:30:11 GMT
-      https://www.theguardian.com/football/2020/nov/19/tites-results-show-he-is-the-best-man-to-lead-brazil-to-the-world-cup
-      
-        Photograph: Raúl Martínez/AFP/Getty Images
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-        Photograph: Raúl Martínez/AFP/Getty Images
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-      Tom Sanderson
-      2020-11-19T12:30:11Z
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-    
-      Hungary's Golden Squad: the greatest team never to win it all? | John Ashdown
-      https://www.theguardian.com/football/blog/2020/nov/16/hungary-golden-squad-the-greatest-football-team-never-to-win-it-all
-      <p>The Magical Magyars embarked on one of football’s most remarkable streaks, only to fall short in the Miracle of Bern</p><p>The Wankdorf Stadium in Bern was getting a soaking, but the sun was shining on Hungary’s brightest generation. Gusztáv Sebes’s side had not lost since May 1950 – an undefeated international streak that would not be surpassed until 1993 – and here they were, with 63,800 increasingly sodden souls watching on from the open terraces, cruising to victory in the 1954 World Cup final. Their four matches en route had brought a faintly ludicrous 25 goals: South Korea hammered 9-0 and West Germany taken apart 8-3 in the group stage, Brazil swept aside 4-2 in a brutal quarter-final and Uruguay, the holders, edged out by the same score after extra-time in a semi-final for the ages.</p><p>West Germany, the tournament’s surprise package, were lined up to go under the steamroller again in the final and their fear seemed palpable in the opening 10 minutes. Werner Liebrich played the ball straight to József Bozsik 40 yards from his own goal. Bozsik fed Sándor Kocsis, whose shot deflected off a defender and fell to Ferenc Puskás, who crashed the ball home. Two minutes later, Werner Kohlmeyer and goalkeeper Toni Turek appeared overwhelmed by panic as Kocsis pressed, allowing Zoltán Czibor to scoot in from the right and prod the ball into the empty net. Two-nil, eight minutes gone.</p> <a href="https://www.theguardian.com/football/blog/2020/nov/16/hungary-golden-squad-the-greatest-football-team-never-to-win-it-all">Continue reading...</a>
-      Hungary
-      Football
-      Sport
-      Mon, 16 Nov 2020 08:00:02 GMT
-      https://www.theguardian.com/football/blog/2020/nov/16/hungary-golden-squad-the-greatest-football-team-never-to-win-it-all
-      
-        Photograph: PA
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-        Photograph: PA
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-      John Ashdown
-      2020-11-16T08:00:02Z
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-      Australian football study reveals Matildas' alarming lack of squad depth | Samantha Lewis
-      https://www.theguardian.com/football/2020/nov/20/australian-womens-football-study-reveals-matildas-alarming-lack-of-squad-depth
-      <p><strong>Exclusive:</strong> Research by FFA paints a bleak outlook for the national team before the 2023 World Cup</p><p>Earlier this month, when Football Federation Australia denied Wellington Phoenix’s request to relax the W-League’s international player rules in order to field a Kiwi-heavy women’s team, the governing body cited its <a href="https://www.ffa.com.au/xi-principles">XI Principles document</a> and reiterated its desire to “enhance and promote playing and development opportunities for women and girls playing football in Australia.”</p><p>“These are not merely words on paper and FFA will take the necessary steps to continue to promote these principles,” <a href="https://www.smh.com.au/sport/soccer/international-intrigue-why-ffa-shut-down-phoenix-s-w-league-hopes-20201110-p56dab.html">it said in a statement</a>.</p> <a href="https://www.theguardian.com/football/2020/nov/20/australian-womens-football-study-reveals-matildas-alarming-lack-of-squad-depth">Continue reading...</a>
-      Matildas
-      Women's football
-      Football
-      Sport
-      Australia sport
-      Football Australia
-      Thu, 19 Nov 2020 16:30:18 GMT
-      https://www.theguardian.com/football/2020/nov/20/australian-womens-football-study-reveals-matildas-alarming-lack-of-squad-depth
-      
-        Photograph: Saeed Khan/AFP via Getty Images
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-        Photograph: Saeed Khan/AFP via Getty Images
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-      Samantha Lewis
-      2020-11-19T16:30:18Z
-    
-    
-      Next Generation 2020: 60 of the best young talents in world football
-      https://www.theguardian.com/football/ng-interactive/2020/oct/08/next-generation-2020-60-of-the-best-young-talents-in-world-football
-      <p>The ‘Tanzanian Mbappé’, Juan Pablo Ángel’s son and the next Kai Havertz are among our players born in 2003. Check the progress of our <a href="https://www.theguardian.com/football/ng-interactive/2019/oct/10/next-generation-2019-60-of-the-best-young-talents-in-world-football">2019 selection</a> | <a href="https://www.theguardian.com/football/ng-interactive/2018/oct/11/next-generation-2018-60-of-the-best-young-talents-in-world-football">2018</a> | <a href="https://www.theguardian.com/football/ng-interactive/2017/oct/04/next-generation-2017-60-of-the-best-young-talents-in-world-football">2017</a> | <a href="https://www.theguardian.com/football/ng-interactive/2016/oct/05/next-generation-2016-60-of-the-best-young-talents-in-world-football">2016</a> | <a href="https://www.theguardian.com/football/ng-interactive/2015/oct/07/next-generation-2015-50-of-the-best-young-talents-in-world-football">2015 </a></p> <a href="https://www.theguardian.com/football/ng-interactive/2020/oct/08/next-generation-2020-60-of-the-best-young-talents-in-world-football">Continue reading...</a>
-      Football
-      Sport
-      Wed, 07 Oct 2020 23:01:21 GMT
-      https://www.theguardian.com/football/ng-interactive/2020/oct/08/next-generation-2020-60-of-the-best-young-talents-in-world-football
-      
-        Illustration: Guardian Design
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-        Illustration: Guardian Design
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-      Marcus Christenson, Jim Powell and Garry Blight
-      2020-10-07T23:01:21Z
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-      Next Generation 2020: 20 of the best talents at Premier League clubs
-      https://www.theguardian.com/football/ng-interactive/2020/oct/07/next-generation-2020-20-of-the-best-talents-at-premier-league-clubs
-      <p>We pick the best young players at each club born between 1 September 2003 and 31 August 2004, an age band known as first-year scholars. Check the progress of <a href="https://www.theguardian.com/football/ng-interactive/2019/oct/09/next-generation-2019-20-of-the-best-talents-at-premier-league-clubs">class of 2019</a> | <a href="https://www.theguardian.com/football/ng-interactive/2018/oct/10/next-generation-2018-20-of-the-best-talents-at-premier-league-clubs">2018</a> | <a href="https://www.theguardian.com/football/ng-interactive/2017/oct/03/next-generation-2017-20-of-the-best-talents-at-premier-league-clubs">2017</a> | <a href="https://www.theguardian.com/football/ng-interactive/2016/oct/04/next-generation-2016-20-of-the-best-talents-at-premier-league-clubs">2016</a> | <a href="https://www.theguardian.com/football/ng-interactive/2015/oct/06/next-generation-2015-20-of-the-best-talents-at-premier-league-clubs">2015</a></p> <a href="https://www.theguardian.com/football/ng-interactive/2020/oct/07/next-generation-2020-20-of-the-best-talents-at-premier-league-clubs">Continue reading...</a>
-      Football
-      Sport
-      Tue, 06 Oct 2020 23:01:22 GMT
-      https://www.theguardian.com/football/ng-interactive/2020/oct/07/next-generation-2020-20-of-the-best-talents-at-premier-league-clubs
-      
-        Illustration: Guardian Design
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-        Illustration: Guardian Design
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-      Marcus Christenson, Jim Powell and Garry Blight
-      2020-10-06T23:01:22Z
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-      From Ødegaard to Mastour: what happened to Next Generation 2015?
-      https://www.theguardian.com/football/2020/oct/06/from-degaard-to-mastour-what-happened-to-next-generation-2015
-      <p>As we prepare to publish our Next Generation 2020 selection, we say goodbye to the class of 2015. So how did they get on? </p><ul><li>Premier League updates from <a href="https://www.theguardian.com/football/ng-interactive/2015/oct/06/next-generation-2015-20-of-the-best-talents-at-premier-league-clubs">2015</a> | <a href="https://www.theguardian.com/football/ng-interactive/2016/oct/04/next-generation-2016-20-of-the-best-talents-at-premier-league-clubs">2016</a> | <a href="https://www.theguardian.com/football/ng-interactive/2017/oct/03/next-generation-2017-20-of-the-best-talents-at-premier-league-clubs">2017</a> | <a href="https://www.theguardian.com/football/ng-interactive/2018/oct/10/next-generation-2018-20-of-the-best-talents-at-premier-league-clubs">2018</a> | <a href="https://www.theguardian.com/football/ng-interactive/2019/oct/09/next-generation-2019-20-of-the-best-talents-at-premier-league-clubs">2019</a></li><li>Worldwide updates from <a href="https://www.theguardian.com/football/ng-interactive/2015/oct/07/next-generation-2015-50-of-the-best-young-talents-in-world-football">2015</a> | <a href="https://www.theguardian.com/football/ng-interactive/2016/oct/05/next-generation-2016-60-of-the-best-young-talents-in-world-football">2016</a> | <a href="https://www.theguardian.com/football/ng-interactive/2017/oct/04/next-generation-2017-60-of-the-best-young-talents-in-world-football">2017</a> | <a href="https://www.theguardian.com/football/ng-interactive/2018/oct/11/next-generation-2018-60-of-the-best-young-talents-in-world-football">2018</a> | <a href="https://www.theguardian.com/football/ng-interactive/2019/oct/10/next-generation-2019-60-of-the-best-young-talents-in-world-football">2019</a></li></ul><p>This is what the Guardian’s Andy Hunter wrote about his 2015 Liverpool player for our series the Next Generation, where we pick one player from each Premier League club in the first-year scholar age group and follow their developments for five years.</p><p>The 16-year-old has received rave reviews not only from Liverpool and England coaches during a rapid rise up the ranks but also from Steven Gerrard, his boyhood hero, in the former Anfield captain’s new autobiography. Gerrard reveals he asked Pepijn Lijnders, the first-team development coach, to always include Alexander-Arnold in his group when taking his ’B’ Licence course, and handed him the captain’s armband during a comeback game in Liverpool’s Under-18s side last season. “It was my way of telling Trent that I believed he could make it for Liverpool one day,” Gerrard writes.</p> <a href="https://www.theguardian.com/football/2020/oct/06/from-degaard-to-mastour-what-happened-to-next-generation-2015">Continue reading...</a>
-      Football
-      Sport
-      Tue, 06 Oct 2020 14:00:07 GMT
-      https://www.theguardian.com/football/2020/oct/06/from-degaard-to-mastour-what-happened-to-next-generation-2015
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-        Photograph:  Getty Images
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-        Photograph:  Getty Images
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-      Marcus Christenson
-      2020-10-06T14:00:07Z
-    
-    
-      Next Generation 2019: 60 of the best young talents in world football
-      https://www.theguardian.com/football/ng-interactive/2019/oct/10/next-generation-2019-60-of-the-best-young-talents-in-world-football
-      <p>Eidur Gudjohnsen’s son, the new Paul Pogba and Barça’s Ansu Fati are among our 60 most talented players in the world born in 2002. Check the progress of <a href="https://www.theguardian.com/football/ng-interactive/2018/oct/11/next-generation-2018-60-of-the-best-young-talents-in-world-football">our 2018 picks</a> | <a href="https://www.theguardian.com/football/ng-interactive/2017/oct/04/next-generation-2017-60-of-the-best-young-talents-in-world-football">2017</a> | <a href="https://www.theguardian.com/football/ng-interactive/2016/oct/05/next-generation-2016-60-of-the-best-young-talents-in-world-football">2016</a> | <a href="https://www.theguardian.com/football/ng-interactive/2015/oct/07/next-generation-2015-50-of-the-best-young-talents-in-world-football">2015</a> | <a href="https://www.theguardian.com/football/blog/ng-interactive/2014/oct/14/next-generation-2014-40-of-the-best-young-talents-in-world-football">2014 </a></p><p><sup>Photographs by DZfoot, BackpagePix, Club Atlético Belgrano, EFE, EPA, Getty Images, Uwe Gruen/Hoffenheim, AMA/Getty Images, Rex/Shutterstock, Zuma Press/PA, FIFA, Tigres, Damir Krajac/CROPIX, Alamy, Daniel Reyes/Ecuafutbol, Reuters, Atromitos, Imago/PA, fotoBERNAMA, New Straits Times, José Alonso Paredes Sánchez, Agencja Gazeta, David Price/Arsenal FC/Getty, AP, Asian Football Confederation, Danubio FC and Championat Asia</sup><br></p> <a href="https://www.theguardian.com/football/ng-interactive/2019/oct/10/next-generation-2019-60-of-the-best-young-talents-in-world-football">Continue reading...</a>
-      Football
-      Sport
-      Thu, 10 Oct 2019 09:00:14 GMT
-      https://www.theguardian.com/football/ng-interactive/2019/oct/10/next-generation-2019-60-of-the-best-young-talents-in-world-football
-      
-        Illustration: Guardian Design
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-        Illustration: Guardian Design
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-      Marcus Christenson, Jim Powell and Garry Blight
-      2019-10-10T09:00:14Z
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-      Maradona the footballer had no flaws – Maradona the man was a victim | Jorge Valdano
-      https://www.theguardian.com/football/2020/nov/26/maradona-the-footballer-had-no-flaws-maradona-the-man-was-a-victim
-      <p>There is something perverse about a life that fulfils all your dreams and Diego suffered the generosity of fate like no other. The terrible journey from human to myth divided him in two</p><p>Those who screw up their faces sneering as they contemplate the latest incarnation of Maradona, the one who had difficulties walking, struggled to talk, embraced [the Venezuela president Nicolás] Maduro and did whatever he felt like, would be better off abandoning this farewell which embraces the genius and absolves the man. They will not find a single reproach here because the footballer had no flaws and the man was a victim. Of whom? Of me or of you, for example, who at some moment must have eulogised him pitilessly.</p><p>There is something perverse about a life that fulfils all your dreams and Diego suffered the generosity of fate like no other. The terrible, terminal journey from human to myth divided him in two: on the one side Diego; on the other Maradona. Fernando Signorini, his fitness coach, a sensitive, intelligent man who may well have known him better than anyone else, used to say: “I would follow Diego to the end of the earth; I wouldn’t follow Maradona to the corner.”</p> <a href="https://www.theguardian.com/football/2020/nov/26/maradona-the-footballer-had-no-flaws-maradona-the-man-was-a-victim">Continue reading...</a>
-      Diego Maradona
-      Argentina
-      Football
-      Sport
-      Argentina
-      Thu, 26 Nov 2020 17:53:58 GMT
-      https://www.theguardian.com/football/2020/nov/26/maradona-the-footballer-had-no-flaws-maradona-the-man-was-a-victim
-      
-        Photograph: Bob Thomas/Bob Thomas Sports Photography/Getty Images
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-        Photograph: Bob Thomas/Bob Thomas Sports Photography/Getty Images
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-      Jorge Valdano
-      2020-11-26T17:53:58Z
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-    
-      'He took us all to heaven': football fans react to Diego Maradona's death
-      https://www.theguardian.com/football/2020/nov/26/diego-maradona-heaven-football-fans-react-death
-      <p>Fans across the world – from Buenos Aires to Bedford – have been in touch to explain what Maradona means to them</p><p>It is perhaps the strength of feeling for Diego Maradona that sets him apart from other great footballers. Having grown up in poverty and gone on to be revered as a world champion, his story has been an inspiration, while his mesmerising skills gave millions of fans unadulterated joy time and time again. Football fans around the world have been in touch to share their tributes, telling of the lasting impact he had on their lives.</p><p>Nowhere is that feeling stronger than in Maradona’s native Argentina. “Maradona was one of those people who, unintentionally, helped everyone to understand that, even being human and fallible, we can do amazing things. Things that seems impossible to reach or even dream,” says Diego Martínez, a 34-year-old designer. “He also made me realise that we’re not entirely good or bad; we are human. Even though Maradona didn’t seem from this planet sometimes, he was one of our kind, but one of his kind.”</p> <a href="https://www.theguardian.com/football/2020/nov/26/diego-maradona-heaven-football-fans-react-death">Continue reading...</a>
-      Diego Maradona
-      Football
-      Sport
-      Napoli
-      Argentina
-      Thu, 26 Nov 2020 17:16:29 GMT
-      https://www.theguardian.com/football/2020/nov/26/diego-maradona-heaven-football-fans-react-death
-      
-        Photograph: Tomas Cuesta/Getty Images
-      
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-        Photograph: Tomas Cuesta/Getty Images
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-      Molly Blackall
-      2020-11-26T17:16:29Z
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-      The best and worst moments of Diego Maradona’s turbulent career | Scott Murray
-      https://www.theguardian.com/football/blog/2020/nov/26/the-best-and-worst-moments-of-diego-maradona-turbulent-career
-      <p>From dazzling as a 16-year-old to shooting football journalists with an air rifle, the Argentinian’s life was like no other</p> <a href="https://www.theguardian.com/football/blog/2020/nov/26/the-best-and-worst-moments-of-diego-maradona-turbulent-career">Continue reading...</a>
-      Diego Maradona
-      Football
-      Sport
-      Thu, 26 Nov 2020 09:10:03 GMT
-      https://www.theguardian.com/football/blog/2020/nov/26/the-best-and-worst-moments-of-diego-maradona-turbulent-career
-      
-        Photograph: STAFF/AFP/Getty Images
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-        Photograph: STAFF/AFP/Getty Images
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-      Scott Murray
-      2020-11-26T09:10:03Z
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-      The Fiver | Getting their hoops kicked by Sparta Prague
-      https://www.theguardian.com/football/2020/nov/27/the-fiver-celtic-rangers-hoops-kicked-by-sparta-prague
-      <p><a href="https://www.theguardian.com/info/2016/jan/05/the-fiver-email-sign-up">Sign up now! Sign up now! Sign up now? Sign up now</a>!</p><p>On the face of it, shipping a two-goal lead against Benfica for the second time in three weeks isn’t a great look for Pope’s O’Rangers. Especially as the Teddy Bhears would now be through to the knockout stage of Euro Vase if they’d managed to hold on to just one of them. But it wasn’t too long ago that they were getting themselves knocked out by the likes of Progres Niederkorn, so going toe-to-toe with a bona fide European giant is a sign of genuine progress. With things slowly coming together at Ibrox, perma-worried boss Steven Gerrard was able to relax his trademark frown into Light Crease, its lowest setting, for Thursday night’s post-match interview, which there’s no need to quote. The forehead speaks volumes.</p> <a href="https://www.theguardian.com/football/2020/nov/27/the-fiver-celtic-rangers-hoops-kicked-by-sparta-prague">Continue reading...</a>
-      Football
-      Sport
-      Fri, 27 Nov 2020 16:26:03 GMT
-      https://www.theguardian.com/football/2020/nov/27/the-fiver-celtic-rangers-hoops-kicked-by-sparta-prague
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-        Photograph: Martin Divíšek/EPA
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-        Photograph: Martin Divíšek/EPA
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-      Scott Murray
-      2020-11-27T16:26:03Z
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-      A tribute to Diego Maradona – Football Weekly
-      https://www.theguardian.com/football/audio/2020/nov/26/a-tribute-to-diego-maradona-football-weekly-podcast
-      <p><strong><strong><a href="https://twitter.com/maxrushden">Max Rushden</a></strong></strong> is joined by <strong><a href="https://twitter.com/bglendenning">Barry Glendenning</a>, <a href="https://twitter.com/jonawils">Jonathan Wilson</a>, <a href="https://twitter.com/PhilippeAuclair">Philippe Auclair</a>, <a href="https://twitter.com/marc_cart">Marcela Mora y Araujo</a> </strong>and <strong><a href="https://twitter.com/asifkapadia">Asif Kapadia</a> </strong>to celebrate the life of Diego Maradona, the footballing genius who died on Wednesday aged 60</p><p><strong>Rate, review, share on </strong><a href="https://itunes.apple.com/podcast/football-weekly-the-guardian/id188674007?mt=2"><strong>Apple Podcasts</strong></a><strong>, </strong><a href="https://soundcloud.com/guardianfootballweekly"><strong>Soundcloud</strong></a><strong>, </strong><a href="https://audioboom.com/channel/football-weekly"><strong>Audioboom</strong></a><strong>, </strong><a href="https://www.mixcloud.com/guardianfootballweekly/"><strong>Mixcloud</strong></a><strong>, </strong><a href="https://www.acast.com/footballweekly"><strong>Acast</strong></a><strong> and </strong><a href="http://www.stitcher.com/podcast/guardianuk/football-weekly"><strong>Stitcher</strong></a><strong>, and join the conversation on </strong><a href="https://www.facebook.com/GuardianPodcasts/"><strong>Facebook</strong></a><strong>, </strong><a href="https://twitter.com/guardianaudio"><strong>Twitter</strong></a><strong> and </strong><a href="mailto:footballweekly@theguardian.com"><strong>email</strong></a><strong>.</strong></p><p> Diego Maradona went from poverty in Buenos Aires to lifting the World Cup at Mexico ‘86.</p> <a href="https://www.theguardian.com/football/audio/2020/nov/26/a-tribute-to-diego-maradona-football-weekly-podcast">Continue reading...</a>
-      Diego Maradona
-      Football
-      Sport
-      Thu, 26 Nov 2020 14:33:14 GMT
-      https://www.theguardian.com/football/audio/2020/nov/26/a-tribute-to-diego-maradona-football-weekly-podcast
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-        Photograph: Juan Ignacio Roncoroni/EPA
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-        Photograph: Juan Ignacio Roncoroni/EPA
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-      Presented by Max Rushden with Barry Glendenning, Asif Kapadia, Marcela Mora y Araujo, Philippe Auclair and Jonathan Wilson
-      2020-11-26T14:33:14Z
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-      Classic YouTube | Diego Armando Maradona, nerveless kicking and Football Manager kids
-      https://www.theguardian.com/sport/2020/nov/26/diego-armando-maradona-nerveless-kicking-and-football-manager-kids-classic-youtube
-      <p>This week’s roundup includes our tribute to El Diego, plus England rugby highlights and Chelsea v Tottenham</p><p>1) Diego Maradona, one of the greatest footballers of all time, died on Wednesday aged 60. It’s hard to know where to begin, but let’s start with <a href="https://www.youtube.com/watch?v=XegYZ8y3xMY">five of his greatest World Cup goals</a>. No 1 on that list, the ‘goal of the century’, inspired some memorable commentary from <a href="https://www.youtube.com/watch?v=cx6jEh_Zu2M">Barry Davies</a> – “you have to say that’s magnificent” – Argentina’s <a href="https://www.youtube.com/watch?v=1wVho3I0NtU">Victor Hugo Morales</a> going loco and <a href="https://www.youtube.com/watch?v=qnv6m4M_TFM&amp;feature=youtu.be">a concise effort</a> from RTÉ’s Jimmy Magee.</p><p>The assists weren’t bad either: <a href="https://www.youtube.com/watch?v=_c7uMogBM9Q">this one</a>, to Jorge Burruchaga, won the World Cup and four years later he<a href="https://www.youtube.com/watch?v=9LezxSVOrvk"> dissected the Brazilian defence</a>. Here are more of El Diego’s <a href="https://www.youtube.com/watch?v=IIci4TEqAFw">World Cup highlights</a>, and the <a href="https://www.youtube.com/watch?v=cfsLzXYdEQ8&amp;feature=youtu.be">epic post-match celebrations</a> in 1986. Gary Lineker paid <a href="https://twitter.com/btsportfootball/status/1331680485672706048">a fantastic tribute</a> to Maradona on BT Sport, highlighting what made him so unique. And here is the England striker being <a href="https://www.youtube.com/watch?v=1Eo-2byMdqQ">shown round La Bombonera in 2007</a>, plus <a href="https://twitter.com/footballtips/status/1331643784401195008">a 2006 meeting with Diego</a>, who seems genuinely pleased to see Lineker and wasn’t shy about mentioning ‘that’ goal from 1986.</p> <a href="https://www.theguardian.com/sport/2020/nov/26/diego-armando-maradona-nerveless-kicking-and-football-manager-kids-classic-youtube">Continue reading...</a>
-      Sport
-      Thu, 26 Nov 2020 10:00:54 GMT
-      https://www.theguardian.com/sport/2020/nov/26/diego-armando-maradona-nerveless-kicking-and-football-manager-kids-classic-youtube
-      
-        Photograph: David Cannon/Getty Images
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-        Photograph: David Cannon/Getty Images
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-      Guardian sport
-      2020-11-26T10:00:54Z
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-      Playing for England with a teammate from the same non-English club | The Knowledge
-      https://www.theguardian.com/football/2020/nov/25/playing-for-england-with-a-teammate-from-the-same-non-english-club
-      <p>Plus: the best ever penalty shootout comeback, the origins of shirty goal celebrations and more</p><ul><li><a href="mailto:knowledge@theguardian.com">Mail </a><a href="mailto:knowledge@theguardian.com">us</a> your questions or tweet <a href="https://twitter.com/theknowledge_gu">@TheKnowledge_GU</a></li></ul><p><strong>“When Jude Bellingham made his England debut against the Republic of Ireland, he joined his Borussia Dortmund teammate Jadon Sancho on the field. Is this the first time England have had multiple players from the same non-English team?”</strong> wonders Colin Brown.</p><p>Of course not, Colin. Many of you wrote in with examples from Rangers in the early 90s but Dara O’Reilly did this and more, so apologies if you feel irked for missing out on a namecheck.<strong> </strong>“Gary Stevens and Terry Butcher played together for England on 11 occasions while they were both Rangers players, three of those with Trevor Steven, also of Rangers playing, and a further one where Chris Woods, also of Rangers was in goal, for four Rangers players. Mark Walters’ only England cap came as a Rangers player, also with Woods in goal, a friendly against New Zealand in 1991. But, excluding British clubs, we have the following:</p> <a href="https://www.theguardian.com/football/2020/nov/25/playing-for-england-with-a-teammate-from-the-same-non-english-club">Continue reading...</a>
-      Football
-      Sport
-      Wed, 25 Nov 2020 10:00:24 GMT
-      https://www.theguardian.com/football/2020/nov/25/playing-for-england-with-a-teammate-from-the-same-non-english-club
-      
-        Photograph: Mark Leech/Offside/Getty Images
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-      
-        Photograph: Mark Leech/Offside/Getty Images
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-      Guardian sport
-      2020-11-25T10:00:24Z
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-    
-      David Squires on … Being James Milner
-      https://www.theguardian.com/football/ng-interactive/2020/nov/24/david-squires-on-being-james-milner-liverpool-training-ground-door
-      <p>Our cartoonist takes us on a journey through the new training ground door into the mind of Liverpool’s underrated utility man</p><ul><li><a href="https://guardianprintshop.com/collections/david-squires/products/being-james-milner">Buy this cartoon in the Guardian print shop</a></li><li><a href="https://www.theguardian.com/football/series/david-squires-on">Take a look through David’s archive treasure trove</a></li></ul> <a href="https://www.theguardian.com/football/ng-interactive/2020/nov/24/david-squires-on-being-james-milner-liverpool-training-ground-door">Continue reading...</a>
-      Football
-      Sport
-      Liverpool
-      Tue, 24 Nov 2020 11:59:50 GMT
-      https://www.theguardian.com/football/ng-interactive/2020/nov/24/david-squires-on-being-james-milner-liverpool-training-ground-door
-      
-        Illustration: David Squires/The Guardian
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-        Illustration: David Squires/The Guardian
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-      David Squires
-      2020-11-24T11:59:50Z
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-      Women’s Super League: talking points from the weekend’s action | Rachel Brown-Finnis
-      https://www.theguardian.com/football/2020/nov/16/womens-super-league-talking-points-from-the-weekends-action
-      <p>Casey Stoney out-thinks her Manchester City counterpart, Birmingham continue to shine and Bristol City get off the mark</p><p>Following on from last season’s success, the WSL used the men’s international weekend to schedule three huge games, and were rewarded with a scintillating set of high-level thrillers. In the UK, 250,000 people watched the Manchester derby, and I’m excited to see the figures for the US–rights have been bought by NBC.</p> <a href="https://www.theguardian.com/football/2020/nov/16/womens-super-league-talking-points-from-the-weekends-action">Continue reading...</a>
-      Women's Super League
-      Women's football
-      Football
-      Sport
-      Mon, 16 Nov 2020 15:42:09 GMT
-      https://www.theguardian.com/football/2020/nov/16/womens-super-league-talking-points-from-the-weekends-action
-      
-        Composite: Rex/Getty
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-        Composite: Rex/Getty
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-      Rachel Brown-Finnis
-      2020-11-16T15:42:09Z
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-      Golden Goal: Terry Gibson for Manchester United v Arsenal (1987) | Rob Smyth
-      https://www.theguardian.com/football/blog/2020/nov/14/golden-goal-terry-gibson-for-manchester-united-v-arsenal-1987
-      <p>A violent game remembered for a spectacular rampage by Norman Whiteside had a poignant footnote</p><p>Not every golden goal has to glister. One of the most euphoric moments of Terry Gibson’s career was a tap-in from eight yards in a match his team would probably have won anyway. After a year of incessant frustration, it was his first goal for Manchester United – and, as it turned out, his last.</p><p>Gibson was marginalised almost as soon as he was signed by Ron Atkinson, who was subsequently replaced by Sir Alex Ferguson in November 1986. Two months later, Gibson made one and scored in injury-time to secure a violent 2-0 win over the league leaders Arsenal. <a href="https://www.theguardian.com/football/2004/oct/25/match.manchesterunited">Not for the last time</a>, they left Old Trafford bristling with injustice as an unbeaten run – 22 games this time – went up in smoke.</p> <a href="https://www.theguardian.com/football/blog/2020/nov/14/golden-goal-terry-gibson-for-manchester-united-v-arsenal-1987">Continue reading...</a>
-      Football
-      Manchester United
-      Arsenal
-      Sport
-      Sat, 14 Nov 2020 09:10:01 GMT
-      https://www.theguardian.com/football/blog/2020/nov/14/golden-goal-terry-gibson-for-manchester-united-v-arsenal-1987
-      
-        Photograph: Mirrorpix/Getty Images
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-        Photograph: Mirrorpix/Getty Images
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-      Rob Smyth
-      2020-11-14T09:10:01Z
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-      Football quiz: who was Roy Keane ranting about in these quotes?
-      https://www.theguardian.com/football/2020/nov/13/football-quiz-who-was-roy-keane-ranting-about-in-these-quotes
-      <p>Which players and managers took a tongue-lashing from Keane?</p><p class="question__text">"I am sick to death of this goalkeeper!"</p><p class="answer__text">Darren Randolph</p><p class="answer__text">David de Gea</p><p class="answer__text">Jordan Pickford</p><p class="answer__text">Roy Carroll</p><p class="question__text">"He’s had loads of plaudits over the last few months - I know he’s still a young man - but his body language today was shocking, really shocking. Shrugging his shoulders when things weren’t going his way, you roll your sleeves up."</p><p class="answer__text">Paul Pogba</p><p class="answer__text">Mason Greenwood</p><p class="answer__text">Anthony Martial</p><p class="answer__text">Marcus Rashford</p><p class="question__text">"Who is he, Mother Theresa?"</p><p class="answer__text">Niall Quinn</p><p class="answer__text">Alan Shearer</p><p class="answer__text">Gary Neville</p><p class="answer__text">Alex Ferguson</p><p class="answer__text"></p><p class="question__text">"Stuff like that kinda almost insults me. What am I supposed to do? Give up? Not cover every blade of grass? Not do my best for my teammates? Not do my best for my club? To be honest, I get actually offended when people throw quotes like that at me as if I'm supposed to be honoured by it."</p><p class="answer__text">Mick McCarthy</p><p class="answer__text">Alex Ferguson</p><p class="answer__text">Clive Tyldesley</p><p class="answer__text">Ron Atkinson</p><p class="question__text">"I felt I was entitled to say that. He was just a mascot for them. Walking around .... – ‘Look at me, how big I am’ – and he didn’t even own the bloody thing."</p><p class="answer__text">Mick McCarthy</p><p class="answer__text">David Gill</p><p class="answer__text">Alex Ferguson</p><p class="answer__text">David Beckham</p><p class="question__text">"He said, ‘I’ve had enough of you. It’s time we sorted this out.’ So I said, ‘Okay.’ And we had a fight. It felt like 10 minutes. There was a lot of noise – ****’s a big lad. I woke up the next morning. I kind of vaguely remembered the fight ... My hand was really sore and one of my fingers was bent backward ... Throughout the flight, **** wore his sunglasses. He never took them off, and it wasn’t very sunny ... Butty had refereed the fight. He even got a new nickname for it – Mills Lane, after the famous boxing referee. Anyway, **** had grabbed me, I’d head-butted him – we’d been fighting for ages."</p><p class="answer__text">Steve Bruce</p><p class="answer__text">Ruud van Nistelrooy</p><p class="answer__text">Niall Quinn</p><p class="answer__text">Peter Schmeichel</p><p class="question__text">"One of the best pros you could ever come across, so being late for the bus tarnished him; you’d have thought he’d been caught with drugs or something. He was having a go at me."</p><p class="answer__text">Gary Neville</p><p class="answer__text">David Beckham</p><p class="answer__text">Denis Irwin</p><p class="answer__text">Andy Cole</p><p class="question__text">"Reinventing the game, apparently. Sugar lumps at half-time."</p><p class="answer__text">Jürgen Klopp</p><p class="answer__text">Arsène Wenger</p><p class="answer__text">Pep Guardiola</p><p class="answer__text">Julian Nagelsmann</p><p class="question__text">"He was an absolute prick to play against. Niggling, sneaky."</p><p class="answer__text">Paul Ince</p><p class="answer__text">Alf-Inge Haaland</p><p class="answer__text">Vinnie Jones</p><p class="answer__text">Dennis Wise</p><p class="question__text">"People are telling me he’s playing well, I don’t think so. This guy is 30 years of age, an international player, he’s a car crash. He keeps on making these type of mistakes. He was rightfully punished for it.”</p><p class="answer__text">Jordan Henderson</p><p class="answer__text">Kyle Walker</p><p class="answer__text">Joe Hart</p><p class="answer__text">Phil Neville</p><p class="score__min-score">1 and above.</p><p class="score__title">Oh dear. Roy will not be impressed</p><p class="score__min-score">2 and above.</p><p class="score__title">Oh dear. Roy will not be impressed</p><p class="score__min-score">3 and above.</p><p class="score__title">Oh dear. Roy will not be impressed</p><p class="score__min-score">4 and above.</p><p class="score__title">Oh dear. Roy will not be impressed</p><p class="score__min-score">5 and above.</p><p class="score__title">Oh dear. Roy will not be impressed</p><p class="score__min-score">6 and above.</p><p class="score__title">You got a few wrong. Roy will not be pleased</p><p class="score__min-score">7 and above.</p><p class="score__title">You got a few wrong. Roy will not be pleased</p><p class="score__min-score">8 and above.</p><p class="score__title">You got two wrong. Roy will not be pleased</p><p class="score__min-score">9 and above.</p><p class="score__title">You got one wrong. Roy will not be pleased</p><p class="score__min-score">0 and above.</p><p class="score__title">You scored a bit fat zero. Roy will not be impressed</p><p class="score__min-score">10 and above.</p><p class="score__title">You scored full marks. Roy would not expect any less from you.</p> <a href="https://www.theguardian.com/football/2020/nov/13/football-quiz-who-was-roy-keane-ranting-about-in-these-quotes">Continue reading...</a>
-      Roy Keane
-      Football
-      Sport
-      Manchester United
-      Republic of Ireland
-      Celtic
-      Fri, 13 Nov 2020 10:00:14 GMT
-      https://www.theguardian.com/football/2020/nov/13/football-quiz-who-was-roy-keane-ranting-about-in-these-quotes
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-        Photograph: Cathal Noonan/INPHO/REX/Shutterstock
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-        Photograph: Cathal Noonan/INPHO/REX/Shutterstock
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-      Guardian sport
-      2020-11-13T10:00:14Z
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-      Diego Maradona: the achingly human superstar who embodied Argentina | Marcela Mora y Araujo
-      https://www.theguardian.com/football/2020/nov/25/diego-maradona-the-achingly-human-superstar-who-embodied-argentina
-      <p>Maradona was a perfect representation of the human ability to be contradictory, to convey ugly and beautiful at once</p><p><em>“A man of genius is unbearable, unless he possesses at least two things besides: gratitude and purity” – Friedrich Nietzsche, on love, perseverance, and moving beyond good v evil.</em></p><p>Diego Maradona said that when you’ve been to the moon and back, things get difficult. “You become addicted to the moon and it’s not always possible to come back down.”</p> <a href="https://www.theguardian.com/football/2020/nov/25/diego-maradona-the-achingly-human-superstar-who-embodied-argentina">Continue reading...</a>
-      Diego Maradona
-      Argentina
-      Football
-      Sport
-      Argentina
-      Americas
-      World news
-      Wed, 25 Nov 2020 22:34:48 GMT
-      https://www.theguardian.com/football/2020/nov/25/diego-maradona-the-achingly-human-superstar-who-embodied-argentina
-      
-        Photograph: Carlo Fumagalli/AP
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-        Photograph: Carlo Fumagalli/AP
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-      Marcela Mora y Araujo
-      2020-11-25T22:34:48Z
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-      Child genius Diego Maradona became the fulfilment of a prophecy | Jonathan Wilson
-      https://www.theguardian.com/football/blog/2020/nov/25/diego-maradona-argentina-child-genius-who-became-the-fulfilment-of-a-prophecy
-      <p>Blessed with sublime talent developed in the slums of Buenos Aires, <em>cabecita negra</em> went on to become everything that defined Argentina’s football principles</p><p>In the 1920s, as Argentina, a booming immigrant nation, sought a sense of identity, it became apparent that football was one of the few things that bound its disparate population together. No matter what your background, you wanted the team in the blue and white striped shirts to win – and that meant the way the national side played was of political and cultural significance.</p><p>The debate was played out in the pages of El Gráfico, and a consensus emerged that Argentinian football stood in opposition to the game of the British, the quasi-colonial power having largely departed by the beginning of the first world war. On the vast grassy playing fields of the British schools, football was about power and running and energy. The Argentinian, by contrast, learned the game in the <em>potreros</em>, the vacant lots of the slums, on small, hard, crowded pitches where there was no teacher to step in if it got a bit too rough; their game was about being streetwise, tight, technical ability – and cunning.</p> <a href="https://www.theguardian.com/football/blog/2020/nov/25/diego-maradona-argentina-child-genius-who-became-the-fulfilment-of-a-prophecy">Continue reading...</a>
-      Diego Maradona
-      Argentina
-      Football
-      Sport
-      Wed, 25 Nov 2020 17:15:56 GMT
-      https://www.theguardian.com/football/blog/2020/nov/25/diego-maradona-argentina-child-genius-who-became-the-fulfilment-of-a-prophecy
-      
-        Photograph: Alamy
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-        Photograph: Alamy
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-      Jonathan Wilson
-      2020-11-25T17:15:56Z
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-      'Nothing about me was cool': Gareth Southgate on making dreams reality
-      https://www.theguardian.com/football/2020/nov/26/gareth-southgate-on-making-dreams-reality-book-england-manager
-      <p>A new book by England’s manager charting his journey is designed to inspire young people </p><p>Gareth Southgate can still feel the hot flush of teenage embarrassment. It was his first day as a Crystal Palace apprentice and he had made the socially fatal error of turning up in smart clothes – school shirt and trousers, to be precise – only to find that the rest of his year group were in jeans or tracksuits.</p><p>“It felt like a disaster before I’d even started,” the England manager says. “All my peers seemed so much more streetwise and I was just this kid from the suburbs with goofy teeth. Nothing about me was cool and I felt like I’d never fit in.”</p> <a href="https://www.theguardian.com/football/2020/nov/26/gareth-southgate-on-making-dreams-reality-book-england-manager">Continue reading...</a>
-      Gareth Southgate
-      Football
-      Sport
-      England
-      Thu, 26 Nov 2020 08:01:51 GMT
-      https://www.theguardian.com/football/2020/nov/26/gareth-southgate-on-making-dreams-reality-book-england-manager
-      
-        Photograph: Stefan Matzke - sampics/Corbis via Getty Images
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-        Photograph: Stefan Matzke - sampics/Corbis via Getty Images
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-      David Hytner
-      2020-11-26T08:01:51Z
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-      Anton Ferdinand: 'I have kicked myself for years for not speaking out'
-      https://www.theguardian.com/football/2020/nov/20/anton-ferdinand-i-have-kicked-myself-for-years-for-not-speaking-out-john-terry
-      <p>Former defender discusses the pain of the John Terry incident, the abuse his mother suffered and his documentary on racism</p><p>‘I carried the burden of not speaking out for so long, for nine years,” Anton Ferdinand says as he explains how haunted he has been. Ferdinand suffered terribly as the victim in the most controversial incident of <a href="https://www.theguardian.com/football/video/2011/oct/24/john-terry-anton-ferdinand-video" title="">alleged racial abuse</a> in the history of English football. During a Premier League game between Queens Park Rangers and Chelsea in October 2011, John Terry said the words “fucking black cunt” in <a href="https://www.theguardian.com/football/2011/oct/23/john-terry-racist-remarks-anton-ferdinand" title="">a spat with Ferdinand</a> that ended up in court and the subject of a Football Association disciplinary hearing – which <a href="https://www.theguardian.com/football/2012/feb/03/john-terry-stripped-england-captaincy-racism" title="">cost Terry the England captaincy</a>.</p><p>Until now, however, the world has been oblivious to the pain Ferdinand endured. His decision to remain silent burned a hole inside him. “I have kicked myself and beat myself up for years for not speaking out,” he says quietly. This is Ferdinand’s first interview since participating in a searing and deeply moving BBC documentary – <a href="https://www.bbc.co.uk/programmes/m000pzpr" title="">Football, Racism and Me</a> – which will be screened a week on Monday.</p> <a href="https://www.theguardian.com/football/2020/nov/20/anton-ferdinand-i-have-kicked-myself-for-years-for-not-speaking-out-john-terry">Continue reading...</a>
-      Football
-      John Terry
-      Sport
-      Fri, 20 Nov 2020 21:00:34 GMT
-      https://www.theguardian.com/football/2020/nov/20/anton-ferdinand-i-have-kicked-myself-for-years-for-not-speaking-out-john-terry
-      
-        Photograph: Tom Jenkins/The Guardian
-      
-      
-        Photograph: Tom Jenkins/The Guardian
-      
-      Donald McRae
-      2020-11-20T21:00:34Z
-    
-  
-
-
diff --git a/modules/graph-data-science/examples/nlp/queries.cypher b/modules/graph-data-science/examples/nlp/queries.cypher
deleted file mode 100644
index 1da17b85..00000000
--- a/modules/graph-data-science/examples/nlp/queries.cypher
+++ /dev/null
@@ -1,154 +0,0 @@
-// tag::constraint[]
-CREATE CONSTRAINT n10s_unique_uri ON (r:Resource) ASSERT r.uri IS UNIQUE;
-// end::constraint[]
-
-// tag::init[]
-CALL n10s.graphconfig.init({handleVocabUris: "MAP"});
-// end::init[]
-
-// tag::mappings[]
-call n10s.nsprefixes.add('neo','neo4j://voc#');
-CALL n10s.mapping.add("neo4j://voc#subCatOf","SUB_CAT_OF");
-CALL n10s.mapping.add("neo4j://voc#about","ABOUT");
-// end::mappings[]
-
-// tag::software-systems[]
-WITH "https://query.wikidata.org/sparql?query=prefix%20neo%3A%20%3Cneo4j%3A%2F%2Fvoc%23%3E%20%0A%23Cats%0A%23SELECT%20%3Fitem%20%3Flabel%20%0ACONSTRUCT%20%7B%0A%3Fitem%20a%20neo%3ACategory%20%3B%20neo%3AsubCatOf%20%3FparentItem%20.%20%20%0A%20%20%3Fitem%20neo%3Aname%20%3Flabel%20.%0A%20%20%3FparentItem%20a%20neo%3ACategory%3B%20neo%3Aname%20%3FparentLabel%20.%0A%20%20%3Farticle%20a%20neo%3AWikipediaPage%3B%20neo%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%0A%7D%0AWHERE%20%0A%7B%0A%20%20%3Fitem%20(wdt%3AP31%7Cwdt%3AP279)*%20wd%3AQ2429814%20.%0A%20%20%3Fitem%20wdt%3AP31%7Cwdt%3AP279%20%3FparentItem%20.%0A%20%20%3Fitem%20rdfs%3Alabel%20%3Flabel%20.%0A%20%20filter(lang(%3Flabel)%20%3D%20%22en%22)%0A%20%20%3FparentItem%20rdfs%3Alabel%20%3FparentLabel%20.%0A%20%20filter(lang(%3FparentLabel)%20%3D%20%22en%22)%0A%20%20%0A%20%20OPTIONAL%20%7B%0A%20%20%20%20%20%20%3Farticle%20schema%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20schema%3AinLanguage%20%22en%22%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20.%0A%20%20%20%20%7D%0A%20%20%0A%7D" AS softwareSystemsUri
-CALL n10s.rdf.import.fetch(softwareSystemsUri, 'Turtle' , { headerParams: { Accept: "application/x-turtle" } })
-YIELD terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams
-RETURN terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams;
-// end::software-systems[]
-
-// tag::programming-languages[]
-WITH "https://query.wikidata.org/sparql?query=prefix%20neo%3A%20%3Cneo4j%3A%2F%2Fvoc%23%3E%20%0A%23Cats%0A%23SELECT%20%3Fitem%20%3Flabel%20%0ACONSTRUCT%20%7B%0A%3Fitem%20a%20neo%3ACategory%20%3B%20neo%3AsubCatOf%20%3FparentItem%20.%20%20%0A%20%20%3Fitem%20neo%3Aname%20%3Flabel%20.%0A%20%20%3FparentItem%20a%20neo%3ACategory%3B%20neo%3Aname%20%3FparentLabel%20.%0A%20%20%3Farticle%20a%20neo%3AWikipediaPage%3B%20neo%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%0A%7D%0AWHERE%20%0A%7B%0A%20%20%3Fitem%20(wdt%3AP31%7Cwdt%3AP279)*%20wd%3AQ9143%20.%0A%20%20%3Fitem%20wdt%3AP31%7Cwdt%3AP279%20%3FparentItem%20.%0A%20%20%3Fitem%20rdfs%3Alabel%20%3Flabel%20.%0A%20%20filter(lang(%3Flabel)%20%3D%20%22en%22)%0A%20%20%3FparentItem%20rdfs%3Alabel%20%3FparentLabel%20.%0A%20%20filter(lang(%3FparentLabel)%20%3D%20%22en%22)%0A%20%20%0A%20%20OPTIONAL%20%7B%0A%20%20%20%20%20%20%3Farticle%20schema%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20schema%3AinLanguage%20%22en%22%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20.%0A%20%20%20%20%7D%0A%20%20%0A%7D" AS programmingLanguagesUri
-CALL n10s.rdf.import.fetch(programmingLanguagesUri, 'Turtle' , { headerParams: { Accept: "application/x-turtle" } })
-YIELD terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams
-RETURN terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams;
-// end::programming-languages[]
-
-// tag::data-formats[]
-WITH "https://query.wikidata.org/sparql?query=prefix%20neo%3A%20%3Cneo4j%3A%2F%2Fvoc%23%3E%20%0A%23Cats%0A%23SELECT%20%3Fitem%20%3Flabel%20%0ACONSTRUCT%20%7B%0A%3Fitem%20a%20neo%3ACategory%20%3B%20neo%3AsubCatOf%20%3FparentItem%20.%20%20%0A%20%20%3Fitem%20neo%3Aname%20%3Flabel%20.%0A%20%20%3FparentItem%20a%20neo%3ACategory%3B%20neo%3Aname%20%3FparentLabel%20.%0A%20%20%3Farticle%20a%20neo%3AWikipediaPage%3B%20neo%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%0A%7D%0AWHERE%20%0A%7B%0A%20%20%3Fitem%20(wdt%3AP31%7Cwdt%3AP279)*%20wd%3AQ24451526%20.%0A%20%20%3Fitem%20wdt%3AP31%7Cwdt%3AP279%20%3FparentItem%20.%0A%20%20%3Fitem%20rdfs%3Alabel%20%3Flabel%20.%0A%20%20filter(lang(%3Flabel)%20%3D%20%22en%22)%0A%20%20%3FparentItem%20rdfs%3Alabel%20%3FparentLabel%20.%0A%20%20filter(lang(%3FparentLabel)%20%3D%20%22en%22)%0A%20%20%0A%20%20OPTIONAL%20%7B%0A%20%20%20%20%20%20%3Farticle%20schema%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20schema%3AinLanguage%20%22en%22%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20.%0A%20%20%20%20%7D%0A%20%20%0A%7D" AS dataFormatsUri
-CALL n10s.rdf.import.fetch(dataFormatsUri, 'Turtle' , { headerParams: { Accept: "application/x-turtle" } })
-YIELD terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams
-RETURN terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams;
-// end::data-formats[]
-
-// tag::version-control[]
-MATCH path = (c:Category {name: "version control system"})<-[:SUB_CAT_OF*]-(child)
-RETURN path
-LIMIT 25;
-// end::version-control[]
-
-
-// tag::dev-to-articles[]
-LOAD CSV WITH HEADERS FROM 'https://github.com/neo4j-examples/nlp-knowledge-graph/raw/master/import/articles.csv' AS row
-RETURN row
-LIMIT 10;
-// end::dev-to-articles[]
-
-// tag::dev-to-import[]
-CALL apoc.periodic.iterate(
-  "LOAD CSV WITH HEADERS FROM 'https://github.com/neo4j-examples/nlp-knowledge-graph/raw/master/import/articles.csv' AS row
-   RETURN row",
-  "MERGE (a:Article {uri: row.uri})
-   WITH a
-   CALL apoc.load.html(a.uri, {
-     body: 'body div.spec__body p',
-     title: 'h1',
-     time: 'time'
-   })
-   YIELD value
-   UNWIND value.body AS item
-   WITH a,
-        apoc.text.join(collect(item.text), '') AS body,
-        value.title[0].text AS title,
-        value.time[0].attributes.datetime AS date
-   SET a.body = body , a.title = title, a.datetime = datetime(date)",
-  {batchSize: 5, parallel: true}
-)
-YIELD batches, total, timeTaken, committedOperations
-RETURN batches, total, timeTaken, committedOperations;
-// end::dev-to-import[]
-
-// tag::set-key[]
-:params key => ("")
-// end::set-key[]
-
-// tag::nlp-import[]
-CALL apoc.periodic.iterate(
-  "MATCH (a:Article)
-   WHERE not(exists(a.processed))
-   RETURN a",
-  "CALL apoc.nlp.gcp.entities.stream([item in $_batch | item.a], {
-     nodeProperty: 'body',
-     key: $key
-   })
-   YIELD node, value
-   SET node.processed = true
-   WITH node, value
-   UNWIND value.entities AS entity
-   WITH entity, node
-   WHERE not(entity.metadata.wikipedia_url is null)
-   MERGE (page:Resource {uri: entity.metadata.wikipedia_url})
-   SET page:WikipediaPage
-   MERGE (node)-[:HAS_ENTITY]->(page)",
-  {batchMode: "BATCH_SINGLE", batchSize: 10, params: {key: $key}})
-YIELD batches, total, timeTaken, committedOperations
-RETURN batches, total, timeTaken, committedOperations;
-// end::nlp-import[]
-
-// tag::semantic-search[]
-MATCH (c:Category {name: "NoSQL database management system"})
-CALL n10s.inference.nodesInCategory(c, {
-  inCatRel: "ABOUT",
-  subCatRel: "SUB_CAT_OF"
-})
-YIELD node
-MATCH (node)<-[:HAS_ENTITY]-(article)
-RETURN article.uri AS uri, article.title AS title, article.datetime AS date,
-       collect(n10s.rdf.getIRILocalName(node.uri))  as explicitTopics
-ORDER BY date DESC
-LIMIT 5;
-// end::semantic-search[]
-
-// tag::similar-articles-1[]
-MATCH (a:Article {uri: "https://dev.to/qainsights/performance-testing-neo4j-database-using-bolt-protocol-in-apache-jmeter-1oa9"}),
-      path = (a)-[:HAS_ENTITY]->(wiki)-[:ABOUT]->(cat),
-      otherPath = (wiki)<-[:HAS_ENTITY]-(other)
-return path, otherPath;
-// end::similar-articles-1[]
-
-// tag::similar-articles-2[]
-MATCH (a:Article {uri: "https://dev.to/qainsights/performance-testing-neo4j-database-using-bolt-protocol-in-apache-jmeter-1oa9"}),
-      entityPath = (a)-[:HAS_ENTITY]->(wiki)-[:ABOUT]->(cat),
-      path = (cat)-[:SUB_CAT_OF]->(parent)<-[:SUB_CAT_OF]-(otherCat),
-      otherEntityPath = (otherCat)<-[:ABOUT]-(otherWiki)<-[:HAS_ENTITY]-(other)
-RETURN other.title, other.uri,
-       [(other)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS otherCategories,
-       collect([node in nodes(path) | node.name]) AS pathToOther;
-// end::similar-articles-2[]
-
-// tag::custom-ontology-init[]
-CALL n10s.nsprefixes.add('owl','http://www.w3.org/2002/07/owl#');
-CALL n10s.nsprefixes.add('rdfs','http://www.w3.org/2000/01/rdf-schema#');
-CALL n10s.mapping.add("http://www.w3.org/2000/01/rdf-schema#subClassOf","SUB_CAT_OF");
-CALL n10s.mapping.add("http://www.w3.org/2000/01/rdf-schema#label","name");
-CALL n10s.mapping.add("http://www.w3.org/2002/07/owl#Class","Category");
-// end::custom-ontology-init[]
-
-// tag::custom-ontology-preview[]
-CALL n10s.rdf.preview.fetch("http://www.nsmntx.org/2020/08/swStacks","Turtle");
-// end::custom-ontology-preview[]
-
-// tag::custom-ontology-import[]
-CALL n10s.rdf.import.fetch("http://www.nsmntx.org/2020/08/swStacks","Turtle")
-YIELD terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams
-RETURN terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams;
-// end::custom-ontology-import[]
-
-
-// tag::zookeeper[]
-match path = (c:WikipediaPage)-[:ABOUT]->(category)-[:SUB_CAT_OF*]->(:Category {name: "NoSQL database management system"})
-where c.uri contains "Apache_ZooKeeper"
-RETURN path;
-// end::zookeeper[]
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index f5b1a5a8..00000000
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+++ /dev/null
@@ -1 +0,0 @@
-Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/)CITEDCITEDCITEDCITEDCITEDCITEDVENUECITEDCITEDAUTHORAUTHORAUTHORAUTHORCITEDCITEDCITEDCITEDVENUEVENUEVENUEVENUEVENUEVENUEVENUEVENUEVENUEVENUEVENUEVENUEVENUEAUTHORAUTHORVENUEAUTHORAUTHORVENUE Modeling of architectures with UML panel Integrating the ConcernBASE Approach with SADL Schedulability analysis of real time actor systems using Gathering experience in trust-based interactions On the Formal Modelling of Trust in Reputation-… Knowledge-Ba… A performance evaluation framework for nature inspired routing algorit… BeeHive: An efficient fault-tolerant routing algorithm inspired by honey bee The K-Component Architecture Meta-model for Self-Adaptive Software Reflective Analysis and Design for Adapting Object Run-Time Behavior Dimensions of composition models for supporting software ev… A model for developing component-ba… Maximal Safe Set Computation for Idle Speed Control of an Automotive Lecture Notes in Computer Science An Efficient Component Model for the Construction of Adaptive Middl… Getting started with ASPECTJ Nicolas Pessemier Lionel Seinturier Thierry Coupaye Laurence Duchien ArchJava: connecting software architecture to implementa… A Component Model Engineered with Components and Aspe… Modular design of man-machine interfaces with larissa Taming Dynamically Adaptive Systems using models and aspects
\ No newline at end of file
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index 877740ae..00000000
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+++ /dev/null
@@ -1 +0,0 @@
-Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/)CO_AUTHORCO_AUTHORCO_AUTH…CO_AUTHORCO_AUTHORCO_AUTHORCO_AUTHORCO_AUTHORCO_AUTHORCO_AUTHORCO_AUTHORCO_AUTHORCO_AUTH…CO_AUTHORCO_AUTHORCO_AUTHORCO_AUTHORCO_AUTHOR Alaa M. Al-Obaidi Sai Peck Lee Thomas Weigert David Garlan John Knapman Birger Møller-Pedersen Bran Selic Caroline Brun Caroline Hagège John Thornton Andrew Srbic Linda Main Mahsa Chitsaz
\ No newline at end of file
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+++ /dev/null
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-Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/)IN_COUNTRYEROAD Country Place
\ No newline at end of file
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-= Tutorial: Applied Graph Embeddings
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham, updated to GDS 2.x by Clair Sullivan
-:category: graph-data-science
-:tags: graph-data-science, graph-algorithms, graph-embeddings, machine-learning
-:gds-version: 2.0
-:description: This guide provides a hands on walk through of the node2Vec graph embedding algorithm in the Neo4j Data Science Library.
-:page-aliases: ROOT:applied-graph-embeddings.adoc
-:page-type: Tutorial
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/machine-learning/node-embeddings/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-++++
-
-
-
-
-++++
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) and link:/download-center/#algorithms[Graph Data Science Library^] (version 2.0 or later) downloaded and installed to use graph embeddings.
-You will also need to have Python installed to follow the second half of this guide.
-
-[role=expertise {level}]
-{level}
-
-[#graph-embeddings]
-Graph embeddings were introduced in version 1.3 of the link:/graph-data-science-library/[Graph Data Science Library^] (GDSL).
-They can be used to create a fixed size vector representation for nodes in a graph.
-In this guide we'll learn how to use these algorithms to generate embeddings and how to interpret them using visualization techniques.
-
-[NOTE]
-====
-The code examples used in this guide can be found in the https://github.com/neo4j-examples/graph-embeddings[neo4j-examples/applied-graph-embeddings^] GitHub repository.
-For background reading about graph embeddings, see the link:/developer/graph-embeddings[Graph Embeddings] Developer Guide.
-====
-
-
-[#eroads-dataset]
-== European Roads dataset
-
-We're going to use a dataset of European Roads compiled by Lasse Westh-Nielsen and https://lassewesth.blogspot.com/2018/07/the-international-e-road-network-and.html[described in more detail in his blog post^].
-The dataset contains 894 towns, 39 countries, and 1,250 roads connecting them.
-
-To import the dataset, run the following:
-
-[source,cypher]
-----
-CREATE CONSTRAINT places IF NOT EXISTS ON (p:Place) ASSERT p.name IS UNIQUE;
-CREATE CONSTRAINT countries IF NOT EXISTS ON (c:Country) ASSERT c.code IS UNIQUE;
-
-LOAD CSV WITH HEADERS FROM "https://github.com/neo4j-examples/graph-embeddings/raw/main/data/roads.csv"
-AS row
-
-MERGE (origin:Place {name: row.origin_reference_place})
-SET origin.countryCode = row.origin_country_code
-
-MERGE (destination:Place {name: row.destination_reference_place})
-SET destination.countryCode = row.destination_country_code
-
-MERGE (c_origin:Country {code: row.origin_country_code})
-
-MERGE (c_destination:Country {code: row.destination_country_code})
-
-MERGE (origin)-[eroad:EROAD {number: row.road_number}]->(destination)
-SET eroad.distance = toInteger(row.distance), eroad.watercrossing = row.watercrossing
-
-MERGE (origin)-[:IN_COUNTRY]->(c_origin)
-MERGE (destination)-[:IN_COUNTRY]->(c_destination);
-----
-
-We can see the schema in the diagram below:
-
-.European Roads Graph Schema
-image::https://dist.neo4j.com/wp-content/uploads/20200710065009/eroads-new-schema.svg[]
-
-This diagram was generated by running `CALL db.schema.visualization()` in the link:/developer/neo4j-browser/[Neo4j Browser] after importing the data.
-
-In the next section we're going to run graph embeddings over the towns and roads to generate a vector representation for each town.
-
-[#running-graph-embeddings]
-== Running graph embeddings
-
-We're going to use the FastRP algorithm, which is one of 3 embedding algorithms available in the Graph Data Science Library.  FastRP creates embeddings based on random walks of a node's neighborhood.  For more information on the inner workings of FastRP, see link:https://dev.neo4j.com/fastrp_background[this blog post^].
-
-We're going to first run the streaming version of this procedure, which returns a stream of node IDs and embeddings.  The algorithm only requires one mandatory configuration parameter, `embeddingDimension`, which is the size of the vector/list of numbers to create for each node.  Additionally, there a many optional parameters that can be used to further tune the resulting embeddings, which are described in detail in the link:https://neo4j.com/docs/graph-data-science/current/machine-learning/node-embeddings/fastrp/[API documentation^].
-
-In order to create our embeddings, we must first create a graph projection:
-
-[source, cypher]
-----
-CALL gds.graph.project(
-  'places_undir',                    
-  'Place',                             
-  {EROAD: {orientation: 'UNDIRECTED'}}  
-)
-----
-
-In `relationshipProjection`, we specify `orientation: "UNDIRECTED"` so that the direction of the `EROAD` relationship type is ignored on the projected graph that the algorithm runs against.  This is because FastRP uses a random walk approach, so by using an undirected graph we are allowing the random walk to traverse the relationship in both directions.
-
-We can run the algorithm with the following query:
-
-[source, cypher]
-----
-CALL gds.fastRP.stream('places_undir',
-  {
-    embeddingDimension: 10
-  }
-)
-YIELD nodeId, embedding
-RETURN gds.util.asNode(nodeId).name AS place, embedding
-LIMIT 5
-----
-
-If we run the query, it will return the following output:
-
-.Results
-[opts=header, cols="1,5"]
-|===
-| place      | embedding
-| "Larne"    | [0.5786177515983582,-0.4012638330459595,-0.16752511262893677,-0.7087218761444092, 0.37056204676628113,-0.9627646803855896,-0.17660734057426453, 0.5529423356056213, -0.1881837546825409, 0.20178654789924622]
-| "Belfast"  | [0.5153923034667969, -0.22510990500450134, -0.199273020029068, -0.6573874354362488, 0.2203015387058258, -1.0398733615875244, -0.19496142864227295, 0.49318426847457886, -0.024694180116057396, 0.08109953254461288]
-| "Dublin"   | [0.19724464416503906, -0.21975931525230408, 0.019983142614364624, -0.5070462822914124, 0.13303154706954956, -0.8911266326904297, -0.278847873210907, 0.6584466695785522, -0.21137264370918274, -0.22576412558555603]
-| "Wexford"  | [0.2206020951271057, -0.21055060625076294, 0.24700090289115906, -0.7486459016799927, 0.1806430220603943, -0.47783035039901733, -0.3107770085334778, 0.4667920470237732, -0.27482911944389343, -0.6629651784896851]
-| "Rosslare" | [0.24542507529258728, -0.18245811760425568, 0.33527031540870667, -0.8160518407821655, 0.19655174016952515, -0.33142057061195374, -0.2781609892845154, 0.2243683785200119, -0.3783305585384369, -0.8914577960968018]
-|===
-
-[NOTE]
-====
-This procedure is non deterministic, so we'll get different results each time that we run it.
-====
-
-Everything looks fine so far, we've been successful in returning embeddings for each node.
-
-Further exploration will be easier if we store the embeddings in Neo4j, so we're going to do that using the write version of the procedure.  We can store the embeddings by running the following query:
-
-[source, cypher]
-----
-CALL gds.fastRP.write(
-  'places_undir',
-  {
-    embeddingDimension: 256,
-    writeProperty: 'embedding'
-  }
-);
-----
-
-.Results of gds.fastRP.write()
-[opts=header, cols="1,1"]
-|===
-| nodeCount | nodePropertiesWritten | preProcessingMillis | computeMillis | writeMillis | configuration
-| 894       | 894                   | 0                   | 8             | 99          | {"writeConcurrency":4,"nodeSelfInfluence":0,"relationshipWeightProperty":null,"propertyRatio":0.0,"concurrency":4,"normalizationStrength":0.0,"writeProperty":"embedding","iterationWeights":[0.0,1.0,1.0],"embeddingDimension":256,"nodeLabels":["\*"],"sudo":false,"relationshipTypes":["*"],"featureProperties":[],"username":null}
-|===
-
-In the next section we're going to explore these graph embeddings using visualization techniques.
-
-[#visualize-graph-embeddings]
-== Visualizing graph embeddings
-
-We're now going to explore the graph embeddings using the Python programming language, the Neo4j Python driver, and some popular Data Science libraries.
-We'll create a scatterplot of the embedding and we want to see whether it's possible to work out which town a country belongs to by looking at its embedding.
-
-[NOTE]
-====
-The code examples used in this section are available https://github.com/neo4j-examples/applied-graph-embeddings/tree/main/notebooks[in Jupyter notebook form^] in the project repository.  In this notebook you will find the code to import the relevant packages and make the connection to the Neo4j database.  In particular, you will require the Python packages `neo4j`, `pandas`, `sklearn`, and `altair`, which are pip installable.
-====
-
-We're going to use the driver to execute a Cypher query that returns the embedding for towns in the most popular countries, which are Spain, Great Britain, France, Turkey, Italy, Germany, and Greece.
-Restricting the number of countries will make it easier to detect any patterns once we start visualizing the data.
-Once the query has run, we'll convert the results into a Pandas data frame:
-
-
-[source, python]
-----
-query = '''MATCH (p:Place)-[:IN_COUNTRY]->(country)
-           WHERE country.code IN ["E", "GB", "F", "TR", "I", "D", "GR"]
-           RETURN p.name AS place, p.embedding AS embedding, country.code AS country
-'''
-
-df = pd.DataFrame([dict(_) for _ in conn.query(query)])
-df.head()
-----
-
-Now we're ready to start analyzing the data.
-
-At the moment our embeddings are of size 256, but we need them to be of size 2 so that we can visualize them in 2 dimensions.
-The https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding[t-SNE algorithm^] is a dimensionality reduction technique that reduces high dimensionality objects to 2 or 3 dimensions so that they can be better visualized.
-We're going to use it to create x and y coordinates for each embedding.
-
-The following code snippet applies t-SNE to the embeddings and then creates a data frame containing each place, its country, as well as x and y coordinates.
-
-[source, python]
-----
-X_embedded = TSNE(n_components=2, random_state=6).fit_transform(list(df.embedding))
-
-places = df.place
-tsne_df = pd.DataFrame(data = {
-    "place": places,
-    "country": df.country,
-    "x": [value[0] for value in X_embedded],
-    "y": [value[1] for value in X_embedded]
-})
-tsne_df.head()
-----
-
-The content of the data frame looks as follows:
-
-.Results
-[opts=header]
-|===
-| place |	country |	x |	y
-|Larne|	GB|	23.597162|	-3.478853
-|Belfast	|GB|	23.132071|	-4.331254
-|La Coruña|	E|	-6.959006|	7.212301
-|Pontevedra|	E|	-6.563524|	7.505499
-|Huelva	|E|	-11.583806|	11.094340
-|===
-
-[NOTE]
-====
-Since this is not a deterministic embedding, your results will vary from the above.
-====
-
-We can run the following code to create a scatterplot of our embeddings:
-
-[source, python]
-----
-alt.Chart(tsne_df).mark_circle(size=60).encode(
-    x='x',
-    y='y',
-    color='country',
-    tooltip=['place', 'country']
-).properties(width=700, height=400)
-----
-
-// ++++
-// 
    
-
-// 
-// ++++
-
-image::https://guides.neo4j.com/graph-data-science2/visualization_fastrp.png[]
-
-From a quick visual inspection of this chart we can see that the embeddings seem to have clustered by country.
-
-
-[#next-steps]
-== Next Steps
-
-Visualizing embeddings are often only an intermediate step in our analysis.
-
-* Cluster nodes based on the similarity of their embeddings using a k-means clustering algorithm
-* Predict the country of town by using a nearest neighbors algorithm that takes embeddings as input
-* Use the embeddings as features for a machine learning algorithm
-
-
-[#resources]
-== Resources
-
-* link:/developer/graph-embeddings[Graph Embeddings Developer Guide]
-* link:/docs/graph-data-science/1.3-preview/algorithms/node-embeddings/[Node Embeddings Reference Documentation^]
-* https://www.sisu.io/posts/embeddings/[Bringing traditional ML to your Neo4j Graph with node2vec^]
-* https://towardsdatascience.com/computing-node-embedding-with-a-graph-database-neo4j-its-graph-data-science-library-d45db83e54b6[Computing Node Embedding with a Graph Database: Neo4j & its Graph Data Science Library^]
diff --git a/modules/graph-data-science/pages/build-knowledge-graph-nlp-ontologies.adoc b/modules/graph-data-science/pages/build-knowledge-graph-nlp-ontologies.adoc
deleted file mode 100644
index 5bf37b7c..00000000
--- a/modules/graph-data-science/pages/build-knowledge-graph-nlp-ontologies.adoc
+++ /dev/null
@@ -1,591 +0,0 @@
-= Tutorial: Build a Knowledge Graph using NLP and Ontologies
-:level: Beginner
-:page-level: Beginner
-:author: Neo4j
-:category: graph-data-science
-:tags: nlp, knowledge-graph, neosemantics, apoc
-:description: This guide shows how to build and query a Knowledge Graph of entities extracted using APOC NLP procedures and Ontologies extracted using neosemantics.
-:page-type: Tutorial
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/introduction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-You should have a basic understanding of the xref:ROOT:graph-database.adoc#property-graph[property graph model].
-Having Neo4j Desktop link:/download[downloaded^] and installed will allow you to code along with the examples.
-
-[role=expertise {level}]
-{level}
-
-[#introduction]
-== Introduction
-
-In this tutorial we're going to build a Software Knowledge Graph based on:
-
-* Articles taken from dev.to, a developer blogging platform, and the entities extracted (using NLP techniques) from those articles.
-* Software ontologies extracted from Wikidata, the free and open knowledge base that acts as central storage for the structured data of Wikipedia.
-
-Once we've done that we'll learn how to query the Knowledge Graph to find interesting insights that are enabled by combining NLP and Ontologies.
-
-[NOTE]
-====
-The queries and data used in this guide can be found in the https://github.com/neo4j-examples/nlp-knowledge-graph[neo4j-examples/nlp-knowledge-graph^] GitHub repository.
-====
-
-[#connections-talks]
-== Video
-
-Jesús Barrasa and Mark Needham presented a talk based on this tutorial at the https://neo4j.com/video/connections/knowledge-graphs/[Neo4j Connections: Knowledge Graphs^] event on 25th August 2020.
-The video from the talk is available below:
-
-++++
-
-++++
-
-[#tools]
-== Tools
-
-We're going to use a couple of plugin libraries in this tutorial, so you'll need to install those if you want to follow along with the examples.
-
-[#apoc]
-=== APOC
-
-image:apoc-small.jpg[float="right", width="100px"]
-
-https://neo4j.com/labs/apoc/[APOC^] (Awesome Procedures on Neo4j) is Neo4j's standard library.
-It contains more than 450 procedures and functions providing functionality for utilities, conversions, graph updates, and more.
-We're going to use this tool to scrape web pages and apply NLP techniques on text data.
-
-We can install APOC from the plugins section of a database in the Neo4j Desktop:
-
-.Installing APOC
-image::apoc-plugin-install.png[Installing APOC, link="{imagesdir}/apoc-plugin-install.png",role="popup-link"]
-
-We'll also need to install the APOC NLP Dependencies jar from GitHub releases.
-At the time of writing, the latest version of APOC is 4.0.0.18, so we need to download `apoc-nlp-dependencies-4.0.0.18.jar` from the https://github.com/neo4j-contrib/neo4j-apoc-procedures/releases/tag/4.0.0.18[4.0.0.18 release page^].
-Once we've downloaded that file, we need to place it in the `plugins` directory:
-
-.Installing APOC NLP Dependencies
-image::apoc-dependencies.png[Installing APOC NLP Depedencies, link="{imagesdir}/apoc-dependencies.png",role="popup-link"]
-
-[#neosemantics]
-=== neosemantics (n10s)
-
-image:n10s.png[float="right", width="100px"]
-
-https://neo4j.com/labs/neosemantics[neosemantics^] is a plugin that enables the use of RDF and its associated vocabularies like OWL, RDFS, SKOS, and others in Neo4j.
-We're going to use this tool to import ontologies into Neo4j.
-
-[NOTE]
-====
-neosemantics only supports the Neo4j 4.0.x and 3.5.x series. It does not yet support the Neo4j 4.1.x series.
-====
-
-We can install neosemantics by following the instructions in the https://neo4j.com/labs/neosemantics/installation/[project installation guide^].
-
-Both of these tools can be installed in Docker environments, and the project repository contains a https://github.com/neo4j-examples/nlp-knowledge-graph/blob/master/docker-compose.yml[docker-compose.yml^] file that shows how to do this.
-
-[#what-is-knowledge-graph]
-== What is a Knowledge Graph?
-
-There are many different definitions of https://neo4j.com/use-cases/knowledge-graph/[Knowledge Graphs^].
-In this tutorial, the definition of a Knowledge Graph is a graph that contains the following:
-
-Facts :: Instance data. This would include graph data imported from any data source and could be structured (e.g. JSON/XML) or semi structured (e.g. HTML)
-
-Explicit Knowledge :: Explicit description of how instance data relates. This comes from ontologies, taxonomies, or any kind of metadata definition.
-
-[#import-onologies]
-== Importing Wikidata Ontologies
-
-image:1280px-Wikidata-logo-en.svg.png[float="right", width="100px"]
-
-Wikidata is a free and open knowledge base that can be read and edited by both humans and machines.
-It acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wiktionary, Wikisource, and others.
-
-[#wikidata-api]
-=== Wikidata SPARQL API
-
-Wikidata provides a https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service[SPARQL API^] that lets users query the data directly.
-The screenshot below shows an example of a SPARQL query along with the results from running that query:
-
-.Querying a Software Taxonomy in the Wikidata SPARQL API
-image::wikidata-api.png[Querying a Software Taxonomy in the Wikidata SPARQL API]
-
-This query starts from the entity https://www.wikidata.org/wiki/Q2429814[Q2429814^] (Software System) and then transitively finds children of that entity as far as it can.
-If we run the query we get a stream of triples (subject, predicate, object).
-
-https://query.wikidata.org/#prefix%20neo%3A%20%3Cneo4j%3A%2F%2Fvoc%23%3E%20%0A%23Cats%0A%23SELECT%20%3Fitem%20%3Flabel%20%0ACONSTRUCT%20%7B%0A%3Fitem%20a%20neo%3ACategory%20%3B%20neo%3AsubCatOf%20%3FparentItem%20.%20%20%0A%20%20%3Fitem%20neo%3Aname%20%3Flabel%20.%0A%20%20%3FparentItem%20a%20neo%3ACategory%3B%20neo%3Aname%20%3FparentLabel%20.%0A%20%20%3Farticle%20a%20neo%3AWikipediaPage%3B%20neo%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%0A%7D%0AWHERE%20%0A%7B%0A%20%20%3Fitem%20%28wdt%3AP31%7Cwdt%3AP279%29%2a%20wd%3AQ2429814%20.%0A%20%20%3Fitem%20wdt%3AP31%7Cwdt%3AP279%20%3FparentItem%20.%0A%20%20%3Fitem%20rdfs%3Alabel%20%3Flabel%20.%0A%20%20filter%28lang%28%3Flabel%29%20%3D%20%22en%22%29%0A%20%20%3FparentItem%20rdfs%3Alabel%20%3FparentLabel%20.%0A%20%20filter%28lang%28%3FparentLabel%29%20%3D%20%22en%22%29%0A%20%20%0A%20%20OPTIONAL%20%7B%0A%20%20%20%20%20%20%3Farticle%20schema%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20schema%3AinLanguage%20%22en%22%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20.%0A%20%20%20%20%7D%0A%20%20%0A%7D[Try out the SPARQL query^,role=more information]
-
-We're now going to learn how to import Wikidata into Neo4j using neosemantics.
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="constraint"]
-----
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="init"]
-----
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="mappings"]
-----
-
-Now we're going to import the Wikidata taxonomies.
-We can get an importable URL directly from the Wikidata SPARQL API, by clicking on the `Code` button:
-
-.Getting an importable URI for a Wikidata SPARQL query
-image::copy-sparql-api.png[Getting an importable URI for a Wikidata SPARQL query, link="{imagesdir}/copy-sparql-api.png",role="popup-link"]
-
-We then pass that URL to the `n10s.rdf.import.fetch` procedure, which will import the stream of triples into Neo4j.
-
-The examples below contain queries that import taxonomies starting from Software Systems, Programming Languages, and Data Formats.
-
-.Software systems taxonomy
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="software-systems"]
-----
-
-.Results
-[options="header"]
-|===
-| terminationStatus | triplesLoaded | triplesParsed | namespaces | callParams
-| "OK"              | 1630          | 1630          | NULL       | {headerParams: {Accept: "application/x-turtle"}}
-|===
-
-.Programming languages taxonomy
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="programming-languages"]
-----
-
-.Results
-[options="header"]
-|===
-| terminationStatus | triplesLoaded | triplesParsed | namespaces | callParams
-| "OK"              | 9376          | 9376          | NULL       | {headerParams: {Accept: "application/x-turtle"}}
-|===
-
-.Data formats taxonomy
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="data-formats"]
-----
-
-.Results
-[options="header"]
-|===
-| terminationStatus | triplesLoaded | triplesParsed | namespaces | callParams
-| "OK"              | 514           | 514           | NULL       | {headerParams: {Accept: "application/x-turtle"}}
-|===
-
-[#view-taxonomy]
-=== Viewing the taxonomy
-
-Let's have a look at what's been imported.
-We can get an overview of the contents of our database by running the following query:
-
-[source,cypher]
-----
-CALL apoc.meta.stats()
-YIELD labels, relTypes, relTypesCount
-RETURN labels, relTypes, relTypesCount;
-----
-
-.Results
-[options="header"]
-|====
-| labels | relTypes | relTypesCount
-| {Category: 2308, _NsPrefDef: 1, _MapNs: 1, Resource: 3868, _MapDef: 2, WikipediaPage: 1560, _GraphConfig: 1} | {`()-[:SUB_CAT_OF]->(:Resource)`: 3636, `(:WikipediaPage)-[:ABOUT]->()`: 1560, `(:Resource)-[:ABOUT]->()`: 1560, `()-[:ABOUT]->(:Resource)`: 1560, `()-[:_IN]->()`: 2, `()-[:ABOUT]->(:Category)`: 1560, `()-[:SUB_CAT_OF]->(:Category)`: 3636, `()-[:_IN]->(:_MapNs)`: 2, `(:Resource)-[:SUB_CAT_OF]->()`: 3636, `(:_MapDef)-[:_IN]->()`: 2, `()-[:SUB_CAT_OF]->()`: 3636, `()-[:ABOUT]->()`: 1560, `(:Category)-[:SUB_CAT_OF]->()`: 3636} | {SUB_CAT_OF: 7272, _IN: 2, ABOUT: 3120}
-|====
-
-Any labels or relationships types that have a `_prefix` can be ignores as they represent meta data created by the n10s library.
-
-We can see that we've imported over 2,000 `Category` nodes and 1,700 `WikipediaPage` nodes.
-Every node that we create using n10s will have a `Resource` label, which is why we have over 4,000 nodes with this label.
-
-We also have more than 7,000 `SUB_CAT_OF` relationship types connecting the `Category` nodes and 3,000 `ABOUT` relationship types connecting the `WikipediaPage` nodes to the `Category` nodes.
-
-Let's now have a look at some of the actual data that we've imported.
-We can look at the sub categories of the version control node by running the following query:
-
-.Finding sub categories of version control
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="version-control"]
-----
-
-.Version Control Graph
-image::version-control.svg[Version Control Graph, link="{imagesdir}/version-control.svg",role="popup-link"]
-
-So far, so good!
-
-[#importing-devto]
-== Importing dev.to articles
-
-https://dev.to/[dev.to^] is a developer blogging platform and contains articles on a variety of topics, including NoSQL databases, JavaScript frameworks, the latest AWS API, chatbots, and more.
-A screenshot of the home page is shown below:
-
-.The dev.to home page
-image::devto-screenshot.png[The dev.to home page, link="{imagesdir}/devto-screenshot.png",role="popup-link"]
-
-We're going to import some articles from dev.to into Neo4j.
-https://github.com/neo4j-examples/nlp-knowledge-graph/blob/master/import/articles.csv[`articles.csv`^] contains a list of 30 articles of interest.
-We can query this file using Cypher's `LOAD CSV` clause:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="dev-to-articles"]
-----
-
-.Results
-[options="header"]
-|===
-| row
-| {uri: "https://dev.to/lirantal/securing-a-nodejs--rethinkdb--tls-setup-on-docker-containers"}
-| {uri: "https://dev.to/setevoy/neo4j-running-in-kubernetes-e4p"}
-| {uri: "https://dev.to/divyanshutomar/introduction-to-redis-3m2a"}
-| {uri: "https://dev.to/zaiste/15-git-commands-you-may-not-know-4a8j"}
-| {uri: "https://dev.to/alexjitbit/removing-files-from-mercurial-history-1b15"}
-| {uri: "https://dev.to/michelemauro/atlassian-sunsetting-mercurial-support-in-bitbucket-2ga9"}
-| {uri: "https://dev.to/shirou/back-up-prometheus-records-to-s3-via-kinesis-firehose-54l4"}
-| {uri: "https://dev.to/ionic/farewell-phonegap-reflections-on-my-hybrid-app-development-journey-10dh"}
-| {uri: "https://dev.to/rootsami/rancher-kubernetes-on-openstack-using-terraform-1ild"}
-| {uri: "https://dev.to/jignesh_simform/comparing-mongodb--mysql-bfa"}
-|===
-
-We're going to use APOC's https://neo4j.com/labs/apoc/4.1/import/html/[`apoc.load.html`] procedure to scrape the interesting information from each of these URIs.
-Let's first see how to use this procedure on a single article, as shown in the following query:
-
-[source,cypher]
-----
-// <1>
-MERGE (a:Article {uri: "https://dev.to/lirantal/securing-a-nodejs--rethinkdb--tls-setup-on-docker-containers"})
-WITH a
-
-// <2>
-CALL apoc.load.html(a.uri, {
- body: 'body div.spec__body p',
- title: 'h1',
- time: 'time'
-})
-YIELD value
-
-UNWIND value.body AS item
-
-// <3>
-WITH a,
-     apoc.text.join(collect(item.text), '') AS body,
-     value.title[0].text AS title,
-     value.time[0].attributes.datetime AS date
-
-<4>
-SET a.body = body , a.title = title, a.datetime = datetime(date)
-RETURN a;
-----
-<1> Create node with `Article` label and `uri` property if it doesn't already exist
-<2> Scrape data from the URI using the provided CSS selectors
-<3> Post processing of the values returned from scrapping the URI
-<4> Update node with `body`, `title`, and `datetime` properties
-
-.Results
-[options="header"]
-|===
-| a
-| (:Article {processed: TRUE, datetime: 2017-08-21T18:41:06Z, title: "Securing a Node.js + RethinkDB + TLS setup on Docker containers", body: "We use RethinkDB at work across different projects. It isn’t used for any sort of big-data applications, but rather as a NoSQL database, which spices things up with real-time updates, and relational tables support.RethinkDB features an officially supported Node.js driver, as well as a community-maintained driver as well called rethinkdbdash which is promises-based, and provides connection pooling. There is also a database migration tool called rethinkdb-migrate that aids in managing database changes such as schema changes, database seeding, tear up and tear down capabilities.We’re going to use the official RethinkDB docker image from the docker hub and make use of docker-compose.yml to spin it up (later on you can add additional services to this setup).A fair example for docker-compose.yml:The compose file mounts a local tls directory as a mapped volume inside the container. The tls/ directory will contain our cert files, and the compose file is reflecting this.To setup a secure connection we need to facilitate it using certificates so an initial technical step:Important notes:Update the compose file to include a command configuration that starts the RethinkDB process with all the required SSL configurationImportant notes:You’ll notice there isn’t any cluster related configuration but you can add them as well if you need to so they can join the SSL connection: — cluster-tls — cluster-tls-key /tls/key.pem — cluster-tls-cert /tls/cert.pem — cluster-tls-ca /tls/ca.pemThe RethinkDB drivers support an ssl optional object which either sets the certificate using the ca property, or sets the rejectUnauthorized property to accept or reject self-signed certificates when connecting. A snippet for the ssl configuration to pass to the driver:Now that the connection is secured, it only makes sense to connect using a user/password which are not the default.To set it up, update the compose file to also include the — initial-password argument so you can set the default admin user’s password. For example:Of course you need to append this argument to the rest of the command line options in the above compose file.Now, update the Node.js driver settings to use a user and password to connect:Congratulations! You’re now eligible to “Ready for Production stickers.Don’t worry, I already mailed them to your address.", uri: "https://dev.to/lirantal/securing-a-nodejs--rethinkdb--tls-setup-on-docker-containers"})
-|===
-
-Now we're going to import the other articles.
-
-We'll use the https://neo4j.com/labs/apoc/4.1/overview/apoc.periodic/apoc.periodic.iterate/[`apoc.periodic.iterate`^] procedure so that we can parallelise this process.
-This procedure takes in a data driven statement and an operation statement:
-
-* The data driven statement contains a stream of items to process, which will be the stream of URIs.
-* The operation statement defines what to do to each of these items, which will be to call `apoc.load.html` and create nodes with the `Article` label.
-
-The final parameter is for providing config.
-We're going to tell the procedure to process these items in batches of 5 that can be run concurrently.
-
-We can see the call to the procedure in the following example:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="dev-to-import"]
-----
-
-.Results
-[options="header"]
-|====
-| batches | total | timeTaken | committedOperations
-| 7       | 32    | 15        | 32
-|====
-
-We now have two disconnected sub graphs, which we can see in the diagram below:
-
-.Articles and Taxonomies Sub Graphs
-image::post-dev-to-import.png[Articles and Taxonomies Sub Graphs, link="{imagesdir}/post-dev-to-import.png",role="popup-link"]
-
-On the left we have the Wikidata taxonomy graph, which represents the explicit knowledge in our Knowledge Graph.
-And on the right we have the articles graph, which represents the facts in our Knowledge Graph.
-We want to join these two graphs together, which we will do using NLP techniques.
-
-[#entity-extraction]
-== Article Entity Extraction
-
-image:apoc_gcp.jpg[float="right", width="100px"]
-
-In April 2020, the APOC standard library added https://neo4j.com/labs/apoc/4.1/nlp/[procedures that wrap the NLP APIs^] of each of the big cloud providers - AWS, GCP, and Azure.
-These procedures extract text from a node property and then send that text to APIs that extract entities, key phrases, categories, or sentiment.
-
-We're going to use the https://neo4j.com/labs/apoc/4.1/nlp/gcp/#nlp-gcp-entities[GCP Entity Extraction^] procedures on our articles.
-The GCP NLP API returns Wikipedia pages for entities where those pages exist.
-
-Before we do that we'll need to create an API key that has access to the Natural Language API.
-Assuming that we're already created a GCP account, we can generate a key by following the instructions at https://console.cloud.google.com/apis/credentials[console.cloud.google.com/apis/credentials^].
-Once we've created a key, we'll create a parameter that contains it:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="set-key"]
-----
-
-We're going to use the `apoc.nlp.gcp.entities.stream` procedure, which will return a stream of entities found for the text content contained in a node property.
-Before running this procedure against all of the articles, let's run it against one of them to see what data is returned:
-
-[source,cypher]
-----
-MATCH (a:Article {uri: "https://dev.to/lirantal/securing-a-nodejs--rethinkdb--tls-setup-on-docker-containers"})
-CALL apoc.nlp.gcp.entities.stream(a, {
- nodeProperty: 'body',
- key: $key
-})
-YIELD node, value
-SET node.processed = true
-WITH node, value
-UNWIND value.entities AS entity
-RETURN entity
-LIMIT 5;
-----
-
-
-.Results
-[options="header"]
-|===
-| entity
-| {name: "RethinkDB", salience: 0.47283632, metadata: {mid: "/m/0134hdhv", wikipedia_url: "https://en.wikipedia.org/wiki/RethinkDB"}, type: "ORGANIZATION", mentions: [{type: "PROPER", text: {content: "RethinkDB", beginOffset: -1}}, {type: "PROPER", text: {content: "RethinkDB", beginOffset: -1}}, {type: "PROPER", text: {content: "RethinkDB", beginOffset: -1}}, {type: "PROPER", text: {content: "RethinkDB", beginOffset: -1}}, {type: "PROPER", text: {content: "pemThe RethinkDB", beginOffset: -1}}]}
-| {name: "connection", salience: 0.04166339, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "connection", beginOffset: -1}}, {type: "COMMON", text: {content: "connection", beginOffset: -1}}]}
-| {name: "work", salience: 0.028608896, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "work", beginOffset: -1}}]}
-| {name: "projects", salience: 0.028608896, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "projects", beginOffset: -1}}]}
-| {name: "database", salience: 0.01957906, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "database", beginOffset: -1}}]}
-|===
-
-Each row contains a `name` property that describes the entity.
-`salience` is an indicator of the importance or centrality of that entity to the entire document text.
-
-Some entities also contain a Wikipedia URL, which is found via the `metadata.wikipedia_url` key.
-The first entity, RethinkDB, is the only entity in this list that has such a URL.
-We're going to filter the rows returned to only include ones that have a Wikipedia URL and we'll then connect the `Article` nodes to the `WikipediaPage` nodes that have that URL.
-
-Let's have a look at how we're going to do this for one article:
-
-[source,cypher]
-----
-MATCH (a:Article {uri: "https://dev.to/lirantal/securing-a-nodejs--rethinkdb--tls-setup-on-docker-containers"})
-CALL apoc.nlp.gcp.entities.stream(a, {
- nodeProperty: 'body',
- key: $key
-})
-
-<1>
-YIELD node, value
-
-SET node.processed = true
-WITH node, value
-UNWIND value.entities AS entity
-
-// <2>
-WITH entity, node
-WHERE not(entity.metadata.wikipedia_url is null)
-
-// <3>
-MERGE (page:Resource {uri: entity.metadata.wikipedia_url})
-SET page:WikipediaPage
-
-// <4>
-MERGE (node)-[:HAS_ENTITY]->(page)
-----
-<1> `node` is the article and `value` contains the extracted entities
-<2> Only include entities that have a Wikipedia URL
-<3> Find a node that matches the Wikipedia URL. Create one if it doesn't already exist.
-<4> Create a `HAS_ENTITY` relationship between the `Article` node and `WikipediaPage`
-
-We can see how running this query connects the article and taxonomy sub graphs by looking at the following Neo4j Browser visualization:
-
-.One article's entity graph
-image::one-article-entities.svg[One article's entity graph, link="{imagesdir}/one-article-entities.svg",role="popup-link"]
-
-Now we can run the entity extraction technique over the rest of the articles with help from the `apoc.periodic.iterate` procedure again:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="nlp-import"]
-----
-
-.Results
-[options="header"]
-|====
-| batches | total | timeTaken | committedOperations
-| 4       | 31    | 29        | 31
-|====
-
-[#querying-the-knowledge-graph]
-== Querying the Knowledge Graph
-
-It's now time to query the Knowledge Graph.
-
-[#semantic-search]
-=== Semantic Search
-
-The first query that we're going to do is semantic search.
-The `n10s.inference.nodesInCategory` procedure lets us search from a top level category, finding all its transitive sub categories, and then returns nodes attached to any of those categories.
-
-In our graph the nodes connected to category nodes are `WikipediaPage` nodes.
-We'll therefore need to add an extra `MATCH` clause to our query to find the connected articles via the `HAS_ENTITY` relationship type.
-We can see how to do this in the following query:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="semantic-search"]
-----
-
-.Results
-[options="header", cols="2,2,1,1"]
-|====
-| uri                                                                                                        | title                                                                          | date                 | explicitTopics
-| "https://dev.to/arthurolga/newsql-an-implementation-with-google-spanner-2a86"                              | "NewSQL: An Implementation with Google Spanner"                                | 2020-08-10T16:01:25Z | ["NoSQL"]
-| "https://dev.to/goaty92/designing-tinyurl-it-s-more-complicated-than-you-think-2a48"                       | "Designing TinyURL: it's more complicated than you think"                      | 2020-08-10T10:21:05Z | ["Apache_ZooKeeper"]
-| "https://dev.to/nipeshkc7/dynamodb-the-basics-360g"                                                        | "DynamoDB: the basics"                                                         | 2020-06-02T04:09:36Z | ["NoSQL", "Amazon_DynamoDB"]
-| "https://dev.to/subhransu/realtime-chat-app-using-kafka-springboot-reactjs-and-websockets-lc"              | "Realtime Chat app using Kafka, SpringBoot, ReactJS, and WebSockets"           | 2020-04-25T23:17:22Z | ["Apache_ZooKeeper"]
-| "https://dev.to/codaelux/running-dynamodb-offline-4k1b"                                                    | "How to run DynamoDB Offline"                                                  | 2020-03-23T21:48:31Z | ["NoSQL", "Amazon_DynamoDB"]
-|====
-
-Although we've searched for NoSQL, we can see from the results that a couple of articles don't link directly to that category.
-For example, we have a couple of articles about Apache Zookeeper.
-We can see how this category is connected to NoSQL by writing the following query:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="zookeeper"]
-----
-
-.Zookeeper to NoSQL
-image::zookeeper-nosql.png[Zookeeper to NoSQL,role="popup-link"]
-
-So Apache Zookeeper is actually a couple of levels away from the NoSQL category.
-
-
-[#similar-articles]
-=== Similar Articles
-
-Another thing that we can do with our Knowledge Graph is find similar articles based on the entities that articles have in common.
-The simplest version of this query would be to find other articles that share common entities, as shown in the following query:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="similar-articles-1"]
-----
-
-.Finding similar articles
-image::similar-articles-1.png[Finding similar articles, link="{imagesdir}/similar-articles-1.png",role="popup-link"]
-
-The Neo4j performance testing article is about Neo4j, and there are two other Neo4j articles that we could recommend to a reader that liked this article.
-
-We can also use the category taxonomy in our query.
-We can find articles that share a common parent category by writing the following query:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="similar-articles-2"]
-----
-
-.Results
-[options="header"]
-|====
-| other.title                                                                    | other.uri                                                                                                  | otherCategories                                                                                                                                                                                                                        | pathToOther
-| "Couchbase GeoSearch with ASP.NET Core"                                        | "https://dev.to/ahmetkucukoglu/couchbase-geosearch-with-asp-net-core-i04"                                  | ["ASP.NET", "Couchbase Server"]                                                                                                                                                                                                        | [["Neo4j", "proprietary software", "Couchbase Server"], ["Neo4j", "free software", "ASP.NET"], ["Neo4j", "free software", "Couchbase Server"]]
-| "The Ultimate Postgres vs MySQL Blog Post"                                     | "https://dev.to/dmfay/the-ultimate-postgres-vs-mysql-blog-post-1l5f"                                       | ["YAML", "Python", "JavaScript", "NoSQL database management system", "Structured Query Language", "JSON", "Extensible Markup Language", "comma-separated values", "PostgreSQL", "MySQL", "Microsoft SQL Server", "MongoDB", "MariaDB"] | [["Neo4j", "proprietary software", "Microsoft SQL Server"], ["Neo4j", "free software", "PostgreSQL"]]
-| "5 Best courses to learn Apache Kafka in 2020"                                 | "https://dev.to/javinpaul/5-best-courses-to-learn-apache-kafka-in-2020-584h"                               | ["Java", "Scratch", "Scala", "Apache ZooKeeper"]                                                                                                                                                                                       | [["Neo4j", "free software", "Scratch"], ["Neo4j", "free software", "Apache ZooKeeper"]]
-| "Building a Modern Web Application with Neo4j and NestJS"                      | "https://dev.to/adamcowley/building-a-modern-web-application-with-neo4j-and-nestjs-38ih"                   | ["TypeScript", "JavaScript", "Neo4j"]                                                                                                                                                                                                  | [["Neo4j", "free software", "TypeScript"]]
-| "Securing a Node.js + RethinkDB + TLS setup on Docker containers"              | "https://dev.to/lirantal/securing-a-nodejs--rethinkdb--tls-setup-on-docker-containers"                     | ["NoSQL database management system", "RethinkDB"]                                                                                                                                                                                      | [["Neo4j", "free software", "RethinkDB"]]
-|====
-
-Note that in this query we'll also returning the path from the initial article to the other article.
-So for "Couchbase GeoSearch with ASP.NET Core", there is a path that goes from the initial article to the Neo4j category, from there to the proprietary software category, which is also a parent of the Couchbase Server Category, which the "Couchbase GeoSearch with ASP.NET Core" article is connected to.
-
-This shows off another nice feature of Knowledge Graphs - as well as making a recommendation, it's easy to explain why it was made as well.
-
-[#custom-onotology]
-== Adding a custom ontology
-
-We might not consider proprietary software to be a very good measure of similarity between two technology products.
-It would be unlikely that we're looking for similar articles based on this type of similarity.
-
-But a common way that software products are connected is via https://en.wikipedia.org/wiki/Solution_stack[technology stacks^].
-We could therefore create our own ontology containing some of these stacks.
-
-http://www.nsmntx.org/2020/08/swStacks[nsmntx.org/2020/08/swStacks] contains an ontology for the GRANDstack, MEAN Stack, and LAMP Stack.
-Before we import this ontology, let's setup some mappings in n10s:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="custom-ontology-init"]
-----
-
-And now we can preview the import on the ontology by running the following query:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="custom-ontology-preview"]
-----
-
-.Software Stacks Ontology
-image::stacks.svg[Software Stacks Ontology, link="{imagesdir}/stacks.svg",role="popup-link"]
-
-It looks good, so let's import it by running the following query:
-
-[source,cypher]
-----
-include::example$nlp/queries.cypher[tag="custom-ontology-import"]
-----
-
-.Results
-[options="header"]
-|===
-| terminationStatus | triplesLoaded | triplesParsed | namespaces |  callParams
-| "OK"              | 58            | 58            | NULL       | {}
-|===
-
-We can now re-run the similarity query, which will now return the following results:
-
-.Results
-[options="header"]
-|====
-| other.title                                                                    | other.uri                                                                                                  | otherCategories                                                                                                                                                                                                                        | pathToOther
-| "A Beginner’s Guide to GraphQL"                                                                  | "https://dev.to/leonardomso/a-beginners-guide-to-graphql-3kjj"                                                     | ["GraphQL", "JavaScript"]                                                                                                                                                                                                              | [["Neo4j", "GRAND Stack", "GraphQL"]]
-| "Learn how YOU can build a Serverless GraphQL API on top of a Microservice architecture, part I" | "https://dev.to/azure/learn-how-you-can-build-a-serverless-graphql-api-on-top-of-a-microservice-architecture-233g" | ["Node.js", "GraphQL"]                                                                                                                                                                                                                 | [["Neo4j", "GRAND Stack", "GraphQL"]]
-| "The Ultimate Postgres vs MySQL Blog Post"                                                       | "https://dev.to/dmfay/the-ultimate-postgres-vs-mysql-blog-post-1l5f"                                               | ["Structured Query Language", "Extensible Markup Language", "PostgreSQL", "MariaDB", "JSON", "MySQL", "Microsoft SQL Server", "MongoDB", "comma-separated values", "JavaScript", "YAML", "Python", "NoSQL database management system"] | [["Neo4j", "proprietary software", "Microsoft SQL Server"], ["Neo4j", "free software", "PostgreSQL"]]
-| "Couchbase GeoSearch with ASP.NET Core"                                                          | "https://dev.to/ahmetkucukoglu/couchbase-geosearch-with-asp-net-core-i04"                                          | ["ASP.NET", "Couchbase Server"]                                                                                                                                                                                                        | [["Neo4j", "proprietary software", "Couchbase Server"], ["Neo4j", "free software", "ASP.NET"], ["Neo4j", "free software", "Couchbase Server"]]
-| "Building a Modern Web Application with Neo4j and NestJS"                                        | "https://dev.to/adamcowley/building-a-modern-web-application-with-neo4j-and-nestjs-38ih"                           | ["JavaScript", "TypeScript", "Neo4j"]                                                                                                                                                                                                  | [["Neo4j", "free software", "TypeScript"]]
-|====
-
-This time we've now got a couple of extra articles at the top about GraphQL, which is one of the tools in the GRANDstack, of which Neo4j is also a part.
diff --git a/modules/graph-data-science/pages/centrality-graph-algorithms.adoc b/modules/graph-data-science/pages/centrality-graph-algorithms.adoc
deleted file mode 100644
index bf7a2b81..00000000
--- a/modules/graph-data-science/pages/centrality-graph-algorithms.adoc
+++ /dev/null
@@ -1,141 +0,0 @@
-= Centrality Algorithms
-:level: Beginner
-:page-level: Beginner
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, graph-algorithms, centrality
-:description: In this guide, we will learn about centrality graph algorithms.
-:page-aliases: ROOT:centrality-graph-algorithms.adoc
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/algorithms/centrality/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) and the link:/download-center/#algorithms[Graph Data Science Library^] downloaded and installed to use centrality algorithms.
-
-[role=expertise {level}]
-{level}
-
-[#overview-centrality-algorithms]
-== What are centrality algorithms?
-
-image:{img}Centrality-Algo-Icon.png[float="right", width="200px"]
-
-Centrality algorithms are one of the traditional categories of graph algorithms.
-They find the important nodes in a graph, where importance can mean that a node:
-
-* has a lot of direct connections
-* is transitively connected to other important nodes
-* can reach other nodes with few hops
-* sits on the shortest path of lots of pairs of nodes.
-
-++++
-
-++++
-
-
-The Neo4j https://neo4j.com/graph-data-science-library[Graph Data Science Library^] supports many different link:/docs/graph-data-science/current/algorithms/centrality/[centrality algorithms^].
-
-[#degree-centrality]
-== Degree Centrality
-
-The Degree Centrality algorithm counts the number of incoming and outgoing relationships from a node.
-It is used to find popular nodes in a graph, and has the following use cases:
-
-* Degree centrality is an important component of any attempt to determine the most important people on a social network.
-For example, in BrandWatch's https://www.brandwatch.com/blog/react-influential-men-and-women-2017/[most influential men and women on Twitter 2017^] the top 5 people in each category have over 40m followers each.
-
-* https://link.springer.com/chapter/10.1007/978-3-319-23461-8_11[Two Step graph-based semi-supervised Learning for Online Auction Fraud Detection^] describes how weighted degree centrality has been used to help separate fraudsters from legitimate users of an online auction.
-The centrality for fraudsters is significantly higher because they collude with each other to artificially increase prices.
-
-
-link:/docs/graph-data-science/current/algorithms/degree-centrality/[Read Degree Centrality reference documentation^, role="medium button"]
-
-[#closeness-centrality]
-== Closeness Centrality
-
-The Closeness Centrality algorithm is a way of detecting nodes that are able to spread information efficiently through a subgraph.
-It measures the average farness (inverse distance) from a node to all other nodes.
-Nodes with a high closeness score have, on average, the shortest distances to all other nodes.
-Closeness Centrality has the following use cases:
-
-* Closeness centrality is used to research organizational networks, where individuals with high closeness centrality are in a favourable position to control and acquire vital information and resources within the organization.
-One such study is http://www.orgnet.com/MappingTerroristNetworks.pdf["Mapping Networks of Terrorist Cells"^] by Valdis E. Krebs.
-
-* Closeness centrality can also used in networks where information spreads through all shortest paths simultaneously, such as infection spreading through a social network.
-Find more details in http://www.analytictech.com/borgatti/papers/centflow.pdf["Centrality and network flow"] by Stephen P. Borgatti.
-
-* Closeness centrality has been used to estimate the importance of words in a document, based on a graph-based keyphrase extraction process.
-This process is described by Florian Boudin in https://www.aclweb.org/anthology/I/I13/I13-1102.pdf["A Comparison of Centrality Measures for Graph-Based Keyphrase Extraction"].
-
-link:/docs/graph-data-science/current/algorithms/closeness-centrality/[Read Closeness Centrality reference documentation^, role="medium button"]
-
-[#harmonic-centrality]
-== Harmonic Centrality
-
-The Harmonic Centrality algorithm is a variant of Closeness Centrality that gives a more accurate measure of closeness when run on graphs where not all nodes are reachable from each other.
-
-link:/docs/graph-data-science/current/algorithms/harmonic-centrality/[Read Harmonic Centrality reference documentation^, role="medium button"]
-
-[#betweenness-centrality]
-== Betweenness Centrality
-
-Betweenness Centrality is a way of detecting the amount of influence a node has over the flow of information or resources in a graph.
-It can be used to find nodes that serve as bridges from one part of a graph to another.
-Betweenness Centrality has the following use cases:
-
-* Betweenness centrality is used to measure the network flow in package delivery processes or telecommunications networks.
-These networks are characterized by traffic that has a known target and takes the shortest path possible.
-This, and other scenarios, are described in http://www.analytictech.com/borgatti/papers/centflow.pdf["Centrality and network flow"].
-
-* Betweenness centrality is used to identify influencers in legitimate, or criminal, organizations.
-Influencers in organizations are not necessarily in management positions, but instead can be found in brokerage positions of the organizational network.
-The removal of these influencers could destabilize the organization.
-More detail can be found in http://archives.cerium.ca/IMG/pdf/Morselli_and_Roy_2008_.pdf["Brokerage qualifications in ringing operations"].
-
-* Betweenness centrality can be used to help microbloggers spread their reach on Twitter, with a recommendation engine that targets influencers that they should interact with in future.
-This approach is described in ftp://ftp.umiacs.umd.edu/incoming/louiqa/PUB2012/RecMB.pdf["Making Recommendations in a Microblog to Improve the Impact of a Focal User"].
-
-link:/docs/graph-data-science/current/algorithms/betweenness-centrality/[Read Betweenness Centrality reference documentation^, role="medium button"]
-
-[#eigenvector-centrality]
-== Eigenvector Centrality
-The Eigenvector Centrality algorithm measures the transitive (or directional) influence of nodes.
-Relationships to high-scoring nodes contribute more to the score of a node than connections to low-scoring nodes.
-A high score means that a node is connected to other nodes that have high scores.
-
-link:/docs/graph-data-science/current/algorithms/eigenvector-centrality/[Read Eigenvector Centrality reference documentation^, role="medium button"]
-
-[#pagerank]
-== PageRank
-
-PageRank measures the transitive (or directional) influence of nodes and is a variant of the Eigenvector Centrality algorithm.
-Eigenvector Centrality can be used on undirected graphs, whereas the PageRank algorithm is more suited to directed graphs.
-It has the following use cases:
-
-* Personalized PageRank is used by Twitter to present users with recommendations of other accounts that they may wish to follow.
-The algorithm is run over a graph which contains shared interests and common connections.
-Their approach is described in more detail in https://web.stanford.edu/~rezab/papers/wtf_overview.pdf["WTF: The Who to Follow Service at Twitter"^].
-
-* PageRank has been used to rank public spaces or streets, predicting traffic flow and human movement in these areas.
-The algorithm is run over a graph which contains intersections connected by roads, where the PageRank score reflects the tendency of people to park, or end their journey, on each street.
-This is described in more detail in https://arxiv.org/pdf/0804.1630.pdf["Self-organized Natural Roads for Predicting Traffic Flow: A Sensitivity Study"^].
-
-* PageRank can be used in an anomaly or fraud detection system in the healthcare and insurance industries.
-It helps find doctors or providers that are behaving in an unusual manner, and the score can be fed into a machine learning algorithm.
-
-link:/docs/graph-data-science/current/algorithms/page-rank/[Read PageRank reference documentation^, role="medium button"]
-
-[#articlerank]
-== ArticleRank
-
-ArticleRank measures the transitive (or directional) influence of nodes and is a variant of the PageRank algorithm.
-ArticleRank weakens Page Rank's assumption that relationships from nodes that have a low out-degree are more important than relationships from nodes with a higher out-degree.
-
-link:/docs/graph-data-science/current/algorithms/article-rank/[Read ArticleRank reference documentation^, role="medium button"]
diff --git a/modules/graph-data-science/pages/community-detection-graph-algorithms.adoc b/modules/graph-data-science/pages/community-detection-graph-algorithms.adoc
deleted file mode 100644
index 3d07b8b1..00000000
--- a/modules/graph-data-science/pages/community-detection-graph-algorithms.adoc
+++ /dev/null
@@ -1,161 +0,0 @@
-= Community Detection Algorithms
-:level: Beginner
-:page-level: Beginner
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, graph-algorithms, community-detection, clustering
-:description: This guide covers community detection algorithms in the Neo4j Data Science Library, like Louvain, Label Propagation, Weakly Connected Components, and more.
-:page-aliases: ROOT:community-detection-graph-algorithms.adoc
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/algorithms/community/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-{description}
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) and the link:/download-center/#algorithms[Graph Data Science Library^] downloaded and installed to use centrality algorithms.
-
-[role=expertise {level}]
-{level}
-
-[#overview-community-detection-algorithms]
-== What are community detection algorithms?
-
-image:{img}Community-Algo-Icon.png[float="right", width="200px"]
-
-Community detection algorithms are used to evaluate how groups of nodes are clustered or partitioned, as well as their tendency to strengthen or break apart.
-
-// ++++
-// 
-// ++++
-
-The Neo4j https://neo4j.com/graph-data-science-library[Graph Data Science Library^] supports many different link:/docs/graph-data-science/current/algorithms/community/[centrality algorithms^].
-
-[#louvain]
-== Louvain
-
-The Louvain method for community detection is an algorithm for detecting communities in networks.
-It maximizes a modularity score for each community, where the modularity quantifies the quality of an assignment of nodes to communities.
-This means that the algorithm evaluates how much more densely connected the nodes within a community are, compared to how connected they would be in a random network.
-On each iteration the Louvain algorithm recursively merges communities into a single node and executes the modularity clustering on the condensed graphs.
-It has the following use cases:
-
-* Providing recommendations for Reddit users to find similar subreddits, based on the general user behavior.
-For more details, see http://snap.stanford.edu/class/cs224w-2014/projects2014/cs224w-16-final.pdf["Subreddit Recommendations within Reddit Communities"^].
-
-* Extracting topics from online social platforms, such as Twitter and Youtube, based on the co-occurence graph of terms in documents, as a part of Topic Modeling process.
-This process is described in http://www.lbd.dcc.ufmg.br/colecoes/sbsi/2016/047.pdf["Topic Modeling based on Louvain method in Online Social Networks"^].
-
-* Investigating the human brain and finding hierarchical community structures within the brain’s functional network.
-The study mentioned is https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2784301/["Hierarchical Modularity in Human Brain Functional Networks"^].
-
-link:/docs/graph-data-science/current/algorithms/louvain/[Read Louvain reference documentation^, role="medium button"]
-
-[#modularity-optimization]
-== Modularity Optimization
-
-Similar to the link:#louvain[Louvain algorithm^], the Modularity Optimization algorithm tries to detect communities in the graph based on their modularity.
-Modularity is a measure of the structure of a graph, measuring the density of connections within a module or community.
-The algorithm explores for every node if its modularity score might increase if it changes its community to one of its neighboring nodes.
-
-link:/docs/graph-data-science/current/algorithms/modularity-optimization/[Read Modularity Optimization reference documentation, role="medium button"]
-
-[#label-propagation]
-== Label Propagation
-
-The Label Propagation algorithm (LPA) detects communities in a graph using network structure alone as its guide, and doesn't require a pre-defined objective function or prior information about the communities.
-LPA propagates labels throughout the network and forms communities based on this process of label propagation.
-It has the following use cases:
-
-* Assigning polarity of tweets, as a part of semantic analysis which uses seed labels from a classifier trained to detect positive and negative emoticons in combination with Twitter follower graph.
-For more information, see https://dl.acm.org/citation.cfm?id=2140465[Twitter polarity classification with label propagation over lexical links and the follower graph^].
-
-* Estimating potentially dangerous combinations of drugs to co-prescribe to a patient, based on the chemical similarity and side effect profiles.
-The study can be found in https://www.nature.com/articles/srep12339[Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects^].
-
-* Inferring features of utterances in a dialogue, for a machine learning model to track user intention with the help of Wikidata knowledge graph of concepts and their relations.
- For more information, see https://www.uni-ulm.de/fileadmin/website_uni_ulm/iui.iwsds2017/papers/IWSDS2017_paper_12.pdf["Feature Inference Based on Label Propagation on Wikidata Graph for DST"^].
-
-link:/docs/graph-data-science/current/algorithms/label-propagation/[Read Label Propagation reference documentation^, role="medium button"]
-
-
-
-[#weakly-connected-components]
-== Weakly Connected Components
-
-The Weakly Connected Components algorithm finds sets of connected nodes in an _undirected graph_ where each node is reachable from any other node in the same set.
-It is often used early in an analysis to understand a graph’s structure, and also has the following use cases:
-
-* Testing whether a graph is connected is an essential pre-processing step for every graph algorithm.
-Such tests can be performed so quickly, and easily, that you should always verify that your input graph is connected, even when you know it has to be.
-Subtle, difficult-to-detect, bugs often result when your algorithm is run only on one component of a disconnected graph.
-
-* Keeping track of clusters of database records, as part of the de-duplication process - an important task in master data management applications.
-Read more in http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.8405["An efficient domain-independent algorithm for detecting approximately duplicate database records"^].
-
-* Analysing citation networks.
-One study uses WCC to work out how well connected the network is, and then to see whether the connectivity remains if 'hub' or 'authority' nodes are moved from the graph.
-Read more in https://pdfs.semanticscholar.org/a8e0/5f803312032569688005acadaa4d4abf0136.pdf["Characterizing and Mining Citation Graph of Computer Science Literature"^].
-
-link:/docs/graph-data-science/current/algorithms/wcc/[Read Weakly Connected Components reference documentation^, role="medium button"]
-
-[#strongly-connected-components]
-== Strongly Connected Components
-
-The Strongly Connected Components (SCC) algorithm finds sets of connected nodes in a _directed graph_ where each node is reachable in both directions from any other node in the same set.
-It has the following use cases:
-
-* In the analysis of powerful transnational corporations, finding the set of firms in which every member owns directly and/or indirectly owns shares in every other member.
-Although it has benefits, such as reducing transaction costs and increasing trust, this type of structure can weaken market competition.
-Read more in http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0025995&type=printable["The Network of Global Corporate Control"^].
-
-* Computing the connectivity of different network configurations when measuring routing performance in multihop wireless networks.
-Read more in https://dl.acm.org/citation.cfm?id=513803["Routing performance in the presence of unidirectional links in multihop wireless networks"^].
-
-* As the first step in many graph algorithms that work only on strongly connected graph.
-In social networks, a group of people are generally strongly connected (For example, students of a class or any other common place).
-Many people in these groups generally like some common pages, or play common games.
-The SCC algorithms can be used to find such groups, and suggest the commonly liked pages or games to the people in the group who have not yet liked those pages or games.
-
-link:/docs/graph-data-science/current/algorithms/strongly-connected-components/[Read Strongly Connected Components reference documentation, role="medium button"]
-
-[#triangle-counting]
-== Triangle Counting
-
-Triangle counting is a community detection graph algorithm that is used to determine the number of triangles passing through each node in the graph.
-A triangle is a set of three nodes, where each node has a relationship to all other nodes.
-
-The triangle count of a node is useful as a features for classifying a given website as spam, or non-spam, content.
-This is described in http://chato.cl/papers/becchetti_2007_approximate_count_triangles.pdf["Efficient Semi-streaming Algorithms for Local Triangle Counting in Massive Graphs"^].
-
-link:/docs/graph-data-science/current/algorithms/triangle-count/[Read Triangle Counting reference documentation, role="medium button"]
-
-[#local-clustering-coefficient]
-== Local Clustering Coefficient
-
-The Local Clustering Coefficient algorithm computes the local clustering coefficient for each node in the graph.
-The local clustering coefficient of a node describes the likelihood that the neighbors of that node are also connected.
-It has the following use cases:
-
-* Investigating the community structure of Facebook’s social graph, where they found dense neighbourhoods of users in an otherwise sparse global graph.
-Find this study in https://arxiv.org/pdf/1111.4503.pdf["The Anatomy of the Facebook Social Graph"^].
-
-* Clustering coefficient has been proposed to help explore thematic structure of the web, and detect communities of pages with a common topic based on the reciprocal links between them.
-For more information, see http://www.pnas.org/content/99/9/5825[Curvature of co-links uncovers hidden thematic layers in the World Wide Web^].
-
-
-link:/docs/graph-data-science/current/algorithms/local-clustering-coefficient/[Read Local Clustering Coefficient reference documentation, role="medium button"]
-
-[#k1-coloring]
-== K-1 Coloring
-
-The K-1 Coloring algorithm assigns colors to each node in the graph, while trying to use as few colors as possible and make sure that neighbors have different colors.
-
-K-1 Coloring is one of the graph coloring algorithms, which are often used to solve scheduling and assignment problems.
-See more in https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=A1BAC400BD09918971D7EB198B46DA8C?doi=10.1.1.95.4268&rep=rep1&type=pdf[Graph colouring problems and their applications in scheduling^] by Daniel Marx.
-
-link:/docs/graph-data-science/current/algorithms/k1coloring/[Read K-1 Coloring reference documentation, role="medium button"]
diff --git a/modules/graph-data-science/pages/connected-feature-extraction.adoc b/modules/graph-data-science/pages/connected-feature-extraction.adoc
deleted file mode 100644
index 0d5cb75d..00000000
--- a/modules/graph-data-science/pages/connected-feature-extraction.adoc
+++ /dev/null
@@ -1,85 +0,0 @@
-= Connected Feature Extraction
-:level: Beginner
-:page-level: Beginner
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, machine-learning, feature-extraction, connected-feature-extraction
-:description: This guide explains concepts related to connected feature extraction.
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will learn about concepts related to connected feature extraction, a technique that is used to improve the performance of Machine Learning models.
-
-[role=expertise {level}]
-{level}
-
-At a high level, machine learning algorithms take input data and create some sort of output.
-The input data comprises features, where a feature could be an attribute or property.
-
-Examples of features could be the number of rooms or size of the garden in meters squared, if we were building a model to predict house prices.
-
-[#feature-engineering]
-== Feature Engineering
-
-The process of generating and selecting appropriate features is called *feature engineering*.
-
-[quote, Discover Feature Engineering: How to Engineer Features and How to Get Good at It, 'link:https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/[machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/^]']
-____
-Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.
-____
-
-[#feature-extraction]
-== Feature Extraction
-
-Feature extraction is one of the sub problems of feature engineering.
-It describes the process of how we change the shape or format of our raw data so that it can be used in a machine learning pipeline.
-
-It can be thought of as a dimensionality reduction process, where raw variables are reduced to more manageable groups (features), while still accurately describing the original data set.
-
-.Feature Extraction
-image::feature-extraction.png[]
-
-These features are often derived from data stored in tables in a relational database.
-
-[#connected-feature-extraction]
-== Connected Feature Extraction
-
-Connected feature extraction is the process of **changing the shape or format of graph data** so that it is usable in a machine learning pipeline.
-
-.Connected Feature Extraction
-image::connected-feature-extraction.png[]
-
-Connected features can be generated in two main ways:
-
-Running local graph queries ::
-These work best when we know what we're trying to find. For example, counting the number of fraudulent users up to 3 degrees away from a person in a fraud graph
-
-Running global graph algorithms ::
-These are used when we know the general structure that we want, but not the exact pattern. For example, computing the PageRank score of all users as an indicator of potentially fraudulent behavior.
-
-In a talk from the ML4ALL 2019 conference, https://www.linkedin.com/in/amyhodler[Amy Hodler^] explained how connected features can be used to improve machine learning predictions.
-
-++++
-
-++++
-
-You can also read more articles on this topic in Amy's https://neo4j.com/blog/ai-graph-technology-connections-improve-accuracy/[AI & Graph Technology blog series^].
-
-[NOTE]
-====
-For a practical example of how connected features can be used to train a machine learning model, see the xref:graph-data-science:link-prediction/scikit-learn.adoc[Link Prediction with scikit-learn] developer guide.
-====
-
-[#use-cases-connected-features]
-== Use Cases for Connected Features
-
-Connected features are used in many industries and have been particularly helpful for investigating financial crimes like fraud and money laundering.
-In these scenarios, criminals often try to hide activities through multiple layers of obfuscation and network relationships.
-
-Traditional feature extraction methods may be unable to detect such behavior, and this is where graph extracted features work well.
-
-Amy Hodler's https://neo4j.com/whitepapers/artificial-intelligence-graph-technology/[Artificial Intelligence & Graph Technology: Enhancing AI with Context & Connections^] white paper goes into the uses for connected features in more detail.
diff --git a/modules/graph-data-science/pages/graph-algorithms.adoc b/modules/graph-data-science/pages/graph-algorithms.adoc
deleted file mode 100644
index 04e7e319..00000000
--- a/modules/graph-data-science/pages/graph-algorithms.adoc
+++ /dev/null
@@ -1,181 +0,0 @@
-= Neo4j Graph Algorithms
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-platform, graph-data-science, labs, book, resources, path-finding, centrality, community-detection, similarity, link-prediction
-:page-aliases: ROOT:graph-algorithms.adoc
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/algorithms/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#graph-algorithms]
-Graph algorithms provide one of the most potent approaches to analyzing connected data because their mathematical calculations are specifically built to operate on relationships.
-They describe steps to be taken to process a graph to discover its general qualities or specific quantities.
-
-[#neo4j-algorithms-library]
-== Neo4j Graph Data Science Library (GDSL)
-
-Neo4j Graph Data Science is a library that provides efficiently implemented, parallel versions of common graph algorithms for Neo4j 3.x and Neo4j 4.x exposed as link:/developer/cypher/[Cypher] procedures.
-It forms the core part of your link:/developer/graph-data-science[Graph Data Science platform^].
-
-++++
-
-++++
-
-Amy Hodler and Alicia Frame also explain more about the library and share hands on examples in https://www.youtube.com/watch?v=ziG_oPnxB20[this talk^] from the link:/connections/graph-data-science/[Connections: Graph Data Science^] event.
-
-The library contains implementations for the following types of algorithms:
-
-* link:/docs/graph-data-science/current/algorithms/pathfinding/[Path Finding^] - these algorithms help find the shortest path or evaluate the availability and quality of routes
-
-* link:/docs/graph-data-science/current/algorithms/centrality/[Centrality^] - these algorithms determine the importance of distinct nodes in a network
-
-* link:/docs/graph-data-science/current/algorithms/community/[Community Detection^] - these algorithms evaluate how a group is clustered or partitioned, as well as its tendency to strengthen or break apart
-
-* link:/docs/graph-data-science/current/algorithms/similarity/[Similarity^] - these algorithms help calculate the similarity of nodes
-
-* link:/docs/graph-data-science/current/algorithms/linkprediction/[Topological link prediction^] - these algorithms determine the closeness of pairs of nodes
-
-* link:/docs/graph-data-science/current/algorithms/node-embeddings/[Node Embeddings^] - these algorithms compute vector representations of nodes in a graph.
-
-* link:/docs/graph-data-science/current/algorithms/ml-models/node-classification/[Node Classification^] - this algorithm uses machine learning to predict the classification of nodes.
-
-* link:/docs/graph-data-science/current/algorithms/ml-models/linkprediction/[Link prediction^] - these algorithms use machine learning to predict new links between pairs of nodes
-
-link:/graph-data-science-library[Learn more about Neo4j GDSL^, role="medium button"]
-
-[#start-graph-algorithms]
-== Getting Started
-
-image:https://dist.neo4j.com/wp-content/uploads/20200727025317/noun_start_3203586.png[float="right", width="100px"]
-
-There are several ways to get started with graph algorithms:
-
-Sandbox :: No download required. Start using Neo4j Graph Algorithms within seconds through a built-in guide and dataset.
-
-link:/sandbox/?ref=developer-graph-algo[Launch Sandbox^, role="medium button"]
-
-NEuler Graph Data Science Playground :: No-code graph algorithms using this Graph App that provides a UI on top of the Graph Data Science Library.
-
-link:/developer/neuler-no-code-graph-algorithms[Get started with NEuler, role="medium button"]
-
-Free online training :: Learn how to use graph algorithms hands-on in the Data Science and Applied Graph Algorithms courses
-
-link:/graphacademy/online-training/[Try out training^, role="medium button"]
-
-[#tutorials]
-== Tutorials
-
-image:https://dist.neo4j.com/wp-content/uploads/20200727025435/noun_tutorial_3284711-1.png[float="right", width="100px"]
-
-The following guides provide hands on examples of the different algorithms in the Graph Data Science Library.
-
-* xref:graph-data-science:applied-graph-embeddings.adoc[Applied Graph Embeddings]
-
-[#how-to-guides]
-== How-To Guides
-
-image:noun_recipe_3127171.png[float="right", width="100px"]
-
-The recipes show how to use the Graph Data Science Library to solve common problems.
-
-* xref:graph-data-science:link-prediction/scikit-learn.adoc[]
-* xref:graph-data-science:link-prediction/aws-sagemaker-autopilot-automl.adoc[]
-* xref:graph-data-science:link-prediction/graph-data-science-library.adoc[]
-* xref:graph-data-science:node-classification.adoc[]
-
-
-
-[#explanation]
-== Concepts
-
-image:https://dist.neo4j.com/wp-content/uploads/20200721070004/noun_Book_100734.png[float="right", width="100px"]
-
-The following guides provide more details and background for parts of the Graph Data Science Library and related topics.
-
-* xref:graph-data-science:graph-search-algorithms.adoc[]
-* xref:graph-data-science:path-finding-graph-algorithms.adoc[]
-* xref:graph-data-science:centrality-graph-algorithms.adoc[]
-* xref:graph-data-science:community-detection-graph-algorithms.adoc[]
-* xref:graph-data-science:graph-embeddings.adoc[]
-* xref:graph-data-science:link-prediction/index.adoc[]
-* xref:graph-data-science:connected-feature-extraction.adoc[]
-
-
-[#graph-algorithms-resources]
-== Additional Resources
-
-The following are useful resources once you've got a bit of experience with Graph Data Science.
-
-* link:/blog/top-13-resources-graph-theory-algorithms/[Blog: Top 13 Resources for Understanding Graph Theory & Algorithms^]
-* https://medium.com/neo4j/tagged/data-science[Neo4j Analytics Category on Medium^]
-* https://bratanic-tomaz.medium.com/[Tomaz Bratanic's Graph Data Science articles^]
-
-++++
-
-++++
diff --git a/modules/graph-data-science/pages/graph-embeddings.adoc b/modules/graph-data-science/pages/graph-embeddings.adoc
deleted file mode 100644
index c36405b6..00000000
--- a/modules/graph-data-science/pages/graph-embeddings.adoc
+++ /dev/null
@@ -1,94 +0,0 @@
-= Graph Embeddings
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, graph-algorithms, graph-embeddings, machine-learning
-:description: This guide covers graph embeddings in the Neo4j Data Science Library, like node2Vec, GraphSAGE, and Random Projection.
-:page-aliases: ROOT:graph-embeddings.adoc
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/machine-learning/node-embeddings/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will learn about graph embeddings.
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) and link:/download-center/#algorithms[Graph Data Science Library^] (version 2.0 or later) downloaded and installed to use graph embeddings.
-
-[role=expertise {level}]
-{level}
-
-[#graph-embeddings]
-== What are graph embeddings?
-
-
-A graph embedding determines a fixed length vector representation for each entity (usually nodes) in our graph.
-These embeddings are a lower dimensional representation of the graph and preserve the graph's topology.
-
-Node embedding techniques usually consist of the following functions:
-
-.Image from https://arxiv.org/pdf/1709.05584.pdf
-image::https://dist.neo4j.com/wp-content/uploads/20200703083748/node-embeddings-how-they-work.png[Graph Embeddings, width="500px", float="right"]
-
-Similarity function :: measures the similarity between nodes
-Encoder function ::  generates the node embedding
-Decoder function ::  reconstructs pairwise similarity
-Loss function :: checks the quality of the reconstruction
-
-[#use-cases-graph-embeddings]
-== Use cases for graph embeddings
-
-There are several use cases that are well suited for graph embeddings:
-
-* We can visually explore the data by reducing the embeddings to 2 or 3 dimensions with the help of algorithms like https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding[t-distributed stochastic neighbor embedding^] (t-SNE) and https://en.wikipedia.org/wiki/Principal_component_analysis[Principle Component Analysis^] (PCA).
-
-* We could build a kNN similarity graph from the embeddings.
-The similarity graph could then be used to make recommendations as part of a k-Nearest Neighbors query.
-
-* We can use the embeddings as the features to feed into a machine learning model, rather than generating those features by hand.
-In this use case, embeddings can be considered an implementation of https://en.wikipedia.org/wiki/Feature_learning[Representational Learning^].
-
-[#supported-graph-embeddings]
-== Neo4j's graph embeddings
-
-The Neo4j link:/graph-data-science-library[Graph Data Science Library^] supports several graph embedding algorithms.
-
-[opts=header]
-|===
-| Name | Speed | Supports node properties? | Implementation details
-| link:#random-projection[Fast Random Projection] | Fast |  Yes | Linear Algebra
-| link:#node2Vec[node2Vec] |  Intermediate |  No | Neural Network
-| link:#graph-sage[GraphSAGE] | Slow |   Yes | Neural Network
-|===
-
-All the embedding algorithms work on a monopartite undirected input graph.
-
-[#random-projection]
-Fast Random Projection ::
-The Fast Random Projection embedding uses sparse random projections to generate embeddings.
-It is an implementation of the https://arxiv.org/pdf/1908.11512.pdf[FastRP algorithm^].
-It is the fastest of the embedding algorithms and can therefore be useful for obtaining baseline embeddings.
-The embeddings it generates are often equally performant as more complex algorithms that take longer to run.  Additionally, the FastRP algorithm supports the use of node properties as features for generating embeddings, as described link:/docs/graph-data-science/current/machine-learning/node-embeddings/fastrp/#algorithms-embeddings-fastrp-examples-properties[here^].
-
-link:/docs/graph-data-science/current/machine-learning/node-embeddings/fastrp/[Read Fast Random Projection reference documentation^, role="medium button"]
-
-[#node2Vec]
-node2Vec ::
-https://arxiv.org/pdf/1607.00653.pdf[node2Vec^] computes embeddings based on biased random walks of a node's neighborhood.
-The algorithm trains a single-layer feedforward neural network, which is used to predict the likelihood that a node will occur in a walk based on the occurrence of another node.
-node2Vec has parameters that can be tuned to control whether the random walks behave more like breadth first or depth first searches.
-This tuning allows the embedding to either capture homophily (similar embeddings capture network communities) or structural equivalence (similar embeddings capture similar structural roles of nodes).
-
-link:/docs/graph-data-science/current/machine-learning/node-embeddings/node2vec/[Read node2Vec reference documentation^, role="medium button"]
-
-[#graph-sage]
-GraphSAGE ::
-This https://arxiv.org/pdf/1706.02216.pdf[algorithm^] is the only one that supports node properties.
-Training embeddings that include node properties can be useful for including information beyond the topology of the graph, like meta data, attributes, or the results of other graph algorithms.
-GraphSAGE differs from the other algorithms in that it learns a function to calculate an embedding rather than training individual embeddings for each node.
-
-link:/docs/graph-data-science/current/machine-learning/node-embeddings/graph-sage/[Read GraphSAGE reference documentation^, role="medium button"]
diff --git a/modules/graph-data-science/pages/graph-search-algorithms.adoc b/modules/graph-data-science/pages/graph-search-algorithms.adoc
deleted file mode 100644
index 2086ccf5..00000000
--- a/modules/graph-data-science/pages/graph-search-algorithms.adoc
+++ /dev/null
@@ -1,60 +0,0 @@
-= Graph Search Algorithms
-:level: Beginner
-:page-level: Beginner
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, graph-algorithms, graph-search, bfs, dfs
-:description: This guide covers the graph search algorithms in the Neo4j Data Science Library, Breadth First Search (BFS) and Depth First Search (DFS).
-:page-aliases: ROOT:graph-search-algorithms.adoc
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/algorithms/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will learn about graph search algorithms.
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) and the link:/download-center/#algorithms[Graph Data Science Library^] downloaded and installed to use graph search algorithms.
-
-[role=expertise {level}]
-{level}
-
-[#overview-graph-search-algorithms]
-== What are graph search algorithms?
-
-image:https://dist.neo4j.com/wp-content/uploads/20200723030840/noun_Search-graph_528032.png[float="right", width="200px"]
-
-Graph search (or graph traversal) algorithms explore a graph for general discovery or explicit search.
-They will try to visit as much of the graph as they can reach, but there is no expectation that the paths they explore are computationally optimal.
-Graph search algorithms are usually used as a core component of other algorithms.
-
-++++
-
-++++
-
-The Neo4j https://neo4j.com/graph-data-science-library[Graph Data Science Library^] supports both of the graph search algorithms.
-
-[#breadth-first-search]
-== Breadth First Search
-
-Breadth First Search (BFS) is a graph traversal algorithm that starts from a chosen node and explores all of its neighbors at one hop away before visiting all the neighbors at two hops away, and so on.
-
-While it can be used on its own, it is most commonly used as the basis for other more goal-oriented algorithms.
-For example, Shortest Path, Connected Components, and Closeness Centrality all use the BFS algorithm.
-It can also be used to find the shortest path between nodes or for computing the maximum flow in a flow network.
-
-link:/docs/graph-data-science/current/algorithms/bfs/[Read Breadth First Search reference documentation^, role="medium button"]
-
-[#depth-first-search]
-== Depth First Search
-
-Depth First Search (DFS) is a graph traversal algorithm that starts from a chosen node, picks one of its neighbors, and then traverses as far as it can along that path before backtracking.
-
-Like BFS, DFS can be used on its own, but is more commonly used as a component of other algorithms.
-For example, Connected Components and Strongly Connected Components use the DFS algorithm.
-It was originally invented as a strategy for solving mazes and can also be used to generate those mazes.
-
-link:/docs/graph-data-science/current/algorithms/dfs/[Read Depth First Search reference documentation^, role="medium button"]
diff --git a/modules/graph-data-science/pages/index.adoc b/modules/graph-data-science/pages/index.adoc
deleted file mode 100644
index 25c3601a..00000000
--- a/modules/graph-data-science/pages/index.adoc
+++ /dev/null
@@ -1,82 +0,0 @@
-= Neo4j Graph Data Science
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, nlp, graph-algorithms
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/introduction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-[#neo4j-graph-data-science]
-Although Neo4j has traditionally been used for transaction workloads, in recent years it is increasingly being used at the heart of graph analytics platforms.
-This guide introduces the tools available for applying graph analytics to your connected data.
-
-[#what-is-graph-data-science]
-== What is Graph Data Science?
-
-Graph Data Science is a science-driven approach to gain knowledge from the relationships and structures in data, typically to power predictions.
-It describes a toolbox of techniques that help data scientists answer questions and explain outcomes using graph data.
-
-++++
-
-++++
-
-[#applications-of-graph-data-science]
-== Applications of Graph Data Science
-
-Graph Data Science techniques can be used as part of a variety of different applications and use cases.
-
-* Graph queries support domain experts by answering common questions.
-* Graph algorithms help make sense of the global structure of a graph, and the results used for standalone analysis or as features in a machine learning model.
-* Graph embeddings are a core component of similarity graphs that power recommendation systems.
-* Natural Language Processing techniques support content based filtering recommendations and knowledge graph completion.
-
-++++
-
-++++
-
-[#library-graph-data-science]
-== Graph Data Science Library
-
-image:https://dist.neo4j.com/wp-content/uploads/20200407203334/graph-data-science.svg[Graph Data Science Library, width="150px",float="right",margin-left:"2px"]
-
-The Neo4j Graph Data Science Library (GDSL) provides efficiently implemented, parallel versions of common graph algorithms for Neo4j 3.x and Neo4j 4.x exposed as Cypher procedures.
-
-The library contains implementations of classic graph algorithms in the path finding, centrality, and community detection categories.
-It also includes algorithms that are well suited for data science problems, like link prediction and weighted and unweighted similarity.
-
-link:/developer/graph-algorithms[Get started with GDSL, role="medium button"]
-
-[#playground-graph-data-science]
-== NEuler: The Graph Data Science Playground
-
-image:https://dist.neo4j.com/wp-content/uploads/np_swing_206_C595E4.png[Graph Data Science Playground, width="100px",float="right",margin-left:"2px"]
-
-NEuler (Neo4j Euler) is a link:/developer/graph-apps/[graph app^] that helps Neo4j Desktop users get started with the Neo4j Graph Data Science Library.
-
-
-++++
-
-++++
-
-link:/developer/neuler-no-code-graph-algorithms[Get started with NEuler, role="medium button"]
-
-[#nlp]
-== Natural Language Processing (NLP)
-
-Neo4j offers powerful querying capabilities for structured data, but a lot of the world's data exists in text documents.
-NLP techniques can help to extract the latent structure in these documents.
-
-link:/developer/nlp[Learn more about NLP, role="medium button"]
-
-[#graph-data-science-training]
-== Graph Data Science Training
-
-image:https://dist.neo4j.com/wp-content/uploads/20200702062117/noun_training_2987031.png[Graph Data Science Training,float="right",width="100px", margin-left:"2px"]
-
-GraphAcademy has self-paced online training classes to help you get up to speed with Graph Data Science.
-
-Here are our Neo4j Graph Data Science Library courses:
-
-* https://graphacademy.neo4j.com/courses/gds-product-introduction/[Introduction to Neo4j Graph Data Science^]
-* https://graphacademy.neo4j.com/courses/graph-data-science-fundamentals/[Neo4j Graph Data Science Fundamentals^]
diff --git a/modules/graph-data-science/pages/link-prediction/aws-sagemaker-autopilot-automl.adoc b/modules/graph-data-science/pages/link-prediction/aws-sagemaker-autopilot-automl.adoc
deleted file mode 100644
index 68fa7913..00000000
--- a/modules/graph-data-science/pages/link-prediction/aws-sagemaker-autopilot-automl.adoc
+++ /dev/null
@@ -1,350 +0,0 @@
-= Link Prediction with GDSL and AWS SageMaker Autopilot (AutoML)
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, machine-learning, link-prediction, automl
-:description: This guide explains how to solve a link prediction problem using Neo4j GDSL and the AWS SageMaker Autopilot AutoML tool.
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/introduction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will learn how to solve a link prediction problem using the AWS SageMaker Autopilot AutoML tool and the Graph Data Science Library.
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) and the link:/download-center/#algorithms[Graph Data Science Library^] downloaded and installed.
-You will also need to have an AWS account.
-
-[role=expertise {level}]
-{level}
-
-Link Prediction techniques are used to predict future or missing links in graphs.
-In this guide we're going to use these techniques to predict future co-authorships using AWS SageMaker Autopilot and link prediction algorithms from the Graph Data Science Library.
-
-[NOTE]
-====
-The code examples used in this guide can be found in the https://github.com/neo4j-examples/link-prediction[neo4j-examples/link-prediction^] GitHub repository.
-For background reading on link prediction, see the xref:link-prediction/index.adoc[] guide.
-====
-
-[#install-dependencies]
-== Install Dependencies
-
-We're going to use several Python libraries in this guide, so let's get those installed by running the following command:
-
-[source, bash]
-----
-pip install pandas sagemaker
-----
-
-[#citation-graph]
-== Citation Graph
-
-image:noun_citation_2276559.png[float="right", width="100px"]
-
-We'll be using data from the https://aminer.org/citation[DBLP Citation Network^], which includes citation data from various academic sources.
-The dataset doesn’t contain relationships between authors describing their collaborations, but we can infer them based on finding articles authored by multiple people.
-
-.The co-authors graph
-image::co-author-graph.svg[]
-
-[#train-test-features]
-== Train and test datasets
-
-We're going to use the train and test DataFrames that we created in the xref:graph-data-science:link-prediction/scikit-learn.adoc[Link Prediction with scikit-learn] developer guide.
-In that guide we split the citation graph into test and train sub graphs and engineered features using graph algorithms.
-
-We can import those DataFrames from CSV files using the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="load-csv-files"]
-----
-
-And now let's have a look at the features that we're going to be working with:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="train-features"]
-----
-
-.Train DataFrame
-[opts=header, format="csv", cols="5,17,15,17,15,6,5,5,5,10"]
-|===
-include::example$link-prediction/notebooks/data/df_train_under_sample.csv[]
-|===
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="test-features"]
-----
-
-.Test DataFrame
-[opts=header, format="csv", cols="5,17,15,17,15,6,5,5,5,10"]
-|===
-include::example$link-prediction/notebooks/data/df_test_under_sample.csv[]
-|===
-
-[#prerequistes]
-== Setup AWS prerequisites
-
-We'll need to both an AWS role and user that have `AmazonSageMakerFullAccess` permissions.
-We also need to generate an access key and secret for the user.
-The code expects these values to be configured as environment variables:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="imports"]
-
-include::example$link-prediction/py/06_SageMaker.py[tag="prerequisites"]
-----
-
-[#upload-to-s3]
-== Upload dataset to S3
-
-image:s3.png[float="right", width="100px"]
-
-Now we're going to convert the Train and Test DataFrames to CSV files and upload them to S3.
-We need to make sure that the order of the columns is the same in both files, and the train CSV file shouldn't have the `label` field and doesn't need the column headings either.
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="upload-dataset-s3"]
-----
-
-.Output
-|===
-| Train data uploaded to: s3://sagemaker-us-east-1-715633473519/sagemaker/link-prediction-developer-guide-2020-09-22-10-53-59/train/train_data_binary_classifier.csv
-
-Test data uploaded to: s3://sagemaker-us-east-1-715633473519/sagemaker/link-prediction-developer-guide-2020-09-22-10-53-59/test/test_data_binary_classifier.csv
-|===
-
-[NOTE]
-====
-Make sure that the independent variable (`label` in this case) is the last field in the CSV file, otherwise you'll end up training a faulty model.
-====
-
-[#setup-sagemaker-autopilot]
-== Set up SageMaker Autopilot Job
-
-image:sagemaker.png[float="right", width="100px"]
-
-We're now ready to configure our Autopilot job.
-The following inputs are mandatory:
-
-* Amazon S3 location for input dataset and for all output artifacts
-* Name of the column of the dataset you want to predict (`label` in this case)
-* An IAM role
-
-We'll also add config to limit the amount of time to 5 minutes for each training job and we'll create a maximum of 5 candidate models.
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="autopilot-setup"]
-----
-
-[#launch-sagemaker-autopilot]
-== Launch SageMaker Autopilot Job
-
-We're now ready to launch the Autopilot job.
-Autopilot jobs consists of the following high-level steps:
-
-Analyzing Data :: where the dataset is analyzed and Autopilot comes up with a list of ML pipelines that should be tried out on the dataset. The dataset is also split into train and validation sets.
-Feature Engineering :: where Autopilot performs feature transformation on individual features of the dataset as well as at an aggregate level.
-Model Tuning :: where the top performing pipeline is selected along with the optimal hyperparameters for the training algorithm (the last stage of the pipeline).
-
-We can launch our job by calling the `create_auto_ml_job` function:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="autopilot-launch"]
-----
-
-.Output
-|===
-| {'AutoMLJobArn': 'arn:aws:sagemaker:us-east-1:715633473519:automl-job/automl-link-2020-08-20-09-25-03',
- 'ResponseMetadata': {'RequestId': 'c780f695-71c6-4bc3-8401-a77beef5e7e5',
-  'HTTPStatusCode': 200,
-  'HTTPHeaders': {'x-amzn-requestid': 'c780f695-71c6-4bc3-8401-a77beef5e7e5',
-   'content-type': 'application/x-amz-json-1.1',
-   'content-length': '102',
-   'date': 'Thu, 20 Aug 2020 09:25:04 GMT'},
-  'RetryAttempts': 0}}
-|===
-
-The job will take about **25 minutes to run**, but we can track its progress.
-The high-level steps will be displayed in the `AutoMLJobSecondaryStatus` field of the response returned by the `describe_auto_ml_job` function.
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="autopilot-track-progress"]
-----
-
-.Output
-|===
-| InProgress - AnalyzingData
-
-...
-
-InProgress - FeatureEngineering
-
-...
-
-InProgress - ModelTuning
-
-...
-
-Completed - MaxCandidatesReached
-|===
-
-
-Once we see a job status of `Completed` and a secondary status of `MaxCandidatesReached`, our job has completed and we can inspect the results.
-
-[#analyze-candidates]
-== Analyze Candidates
-
-We can list all the candidates by running the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="autopilot-all-candidates"]
-----
-
-.All candidates
-[format="csv", options="header", cols="4,1"]
-|===
-include::example$link-prediction/notebooks/data/download/autopilot_candidates.csv[]
-|===
-
-We can also extract just the best candidate:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="autopilot-best-candidate"]
-----
-
-.Best candidate
-[format="csv", options="header", cols="3,1,1"]
-|===
-include::example$link-prediction/notebooks/data/download/autopilot_best_candidate.csv[]
-|===
-
-[#create-model]
-== Create Model
-
-The next step is to create a model based on one of these candidates using inference pipelines.
-
-[quote, Deploy an Inference Pipeline, 'https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipelines.html[docs.aws.amazon.com/sagemaker/latest/dg/inference-pipelines.html^]']
-____
-An inference pipeline is an Amazon SageMaker model that is composed of a linear sequence of two to five containers that process requests for inferences on data.
-You use an inference pipeline to define and deploy any combination of pretrained Amazon SageMaker built-in algorithms and your own custom algorithms packaged in Docker containers.
-____
-
-We can create a model by running the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="autopilot-create-model"]
-----
-
-.Output
-|===
-| Model ARN corresponding to the best candidate is : arn:aws:sagemaker:us-east-1:715633473519:model/automl-link-pred-model-automl-link-2020-08-20-09-25-03
-|===
-
-[#evaluate-model]
-== Evaluate Model
-
-image:noun_evaluation_2404409.png[float="right", width="100px"]
-
-Now we're going to apply our model to the test set to see how well it fares.
-
-We can use a transform job to do this.
-A transform job uses a trained model to get inferences on a dataset and saves these results to S3.
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="autopilot-create-transform-job"]
-----
-
-.Output
-|===
-| {'AutoMLJobArn': 'arn:aws:sagemaker:us-east-1:715633473519:automl-job/automl-link-2020-09-22-10-53-59',
- 'ResponseMetadata': {'RequestId': 'e3c45bde-62b4-424f-bb2f-98479d7f4428',
-  'HTTPStatusCode': 200,
-  'HTTPHeaders': {'x-amzn-requestid': 'e3c45bde-62b4-424f-bb2f-98479d7f4428',
-   'content-type': 'application/x-amz-json-1.1',
-   'content-length': '102',
-   'date': 'Tue, 22 Sep 2020 10:57:03 GMT'},
-  'RetryAttempts': 0}}
-|===
-
-We can track this job by using the `describe_transform_job` function:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="autopilot-track-transform-job"]
-----
-
-.Output
-|===
-| InProgress
-
-...
-
-Completed
-|===
-
-Once that's completed, we can view the results of the job by running the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="autopilot-transform-job-results"]
-----
-
-This DataFrame contains predictions for the label field of the test DataFrame, and we're now ready to compare those predictions against the actual labels to see how well the model has performed.
-
-We’re going to evaluate the quality of our model by computing its accuracy, precision, and recall.
-The diagram below, taken from the https://neo4j.com/graph-algorithms-book/[O’Reilly Graph Algorithms Book^], explains how each of these metrics are computed.
-
-.Accuracy measures
-image::model-evaluation.png[]
-
-scikit-learn has built in functions that we can use for this.
-The following function will help with this:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="evaluation-imports"]
-
-include::example$link-prediction/py/06_SageMaker.py[tag="evaluation-functions"]
-----
-
-We can evaluate our model by running the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/06_SageMaker.py[tag="test-model"]
-----
-
-.Results
-[opts=header, format="csv"]
-|===
-include::example$link-prediction/notebooks/data/sagemaker-model-eval.csv[]
-|===
-
-We have accuracy, precision, and recall scores of just over 96%, which means the model has done a pretty good job of predicting likely co-authorship.
-
-[#next-steps]
-== Next Steps
-
-We've already got a good model, but can we do better?
-
-Perhaps we could add more features based on the results of other algorithms?
-Or maybe we could increase the run time per job and the number of candidates evaluated by SageMaker to see if it can come up with a better model.
-
-If you have any ideas or questions, please create an issue or PR on the https://github.com/neo4j-examples/link-prediction[neo4j-examples/link-prediction^] GitHub repository.
diff --git a/modules/graph-data-science/pages/link-prediction/graph-data-science-library.adoc b/modules/graph-data-science/pages/link-prediction/graph-data-science-library.adoc
deleted file mode 100644
index 2295613e..00000000
--- a/modules/graph-data-science/pages/link-prediction/graph-data-science-library.adoc
+++ /dev/null
@@ -1,485 +0,0 @@
-= Link Prediction with the Graph Data Science Library
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, machine-learning, link-prediction
-:description: This guide explains how to solve a link prediction problem using Neo4j's Graph Data Science Library.
-:page-type: How-To Guide
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/introduction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will solve a link prediction problem with the Graph Data Science Library.
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later), link:/download-center/#algorithms[Graph Data Science Library^] (version 1.5.0 or later), and APOC downloaded and installed.
-
-[role=expertise {level}]
-{level}
-
-// ++++
-// 
-// ++++
-
-[#what-is-link-prediction]
-== What is link prediction?
-
-Link Prediction is the problem of predicting the existence of a relationship between nodes in a graph.
-In this guide, we will predict co-authorships using the https://neo4j.com/docs/graph-data-science/current/algorithms/linkprediction/[link prediction machine learning model^] that was introduced in version 1.5.0 of the Graph Data Science Library.
-
-[NOTE]
-====
-For background reading about link prediction, see the xref:graph-data-science:link-prediction/index.adoc[Link Prediction Concepts Developer Guide].
-====
-
-
-[#tools-used]
-== Tools Used
-
-This guide uses the following tools and versions:
-
-[opts="header"]
-|===
-| Tool | Version
-| https://neo4j.com/download/[Neo4j^] | 4.2.3
-| https://neo4j.com/docs/graph-data-science/current/installation/[Graph Data Science Library^] | 1.5.0
-| https://github.com/neo4j-contrib/neo4j-apoc-procedures[APOC^] | 4.2.0.1
-|===
-
-
-[#citation-graph]
-== Citation Graph
-
-image:noun_citation_2276559.png[float="right", width="100px"]
-
-In this guide, we’re going to use data from the https://aminer.org/citation[DBLP Citation Network^], which includes citation data from various academic sources.
-The full dataset is very large, but we're going to use a subset that contains data from a few Software Development Conferences.
-
-A screenshot of the available datasets is shown below:
-
-.Citation Networks
-image::citation-graph.png[]
-
-We can import this dataset by running the following queries:
-
-.Create constraints
-[source,cypher]
-----
-CREATE CONSTRAINT ON (a:Article) ASSERT a.index IS UNIQUE;
-CREATE CONSTRAINT ON (a:Author) ASSERT a.name IS UNIQUE;
-CREATE CONSTRAINT ON (v:Venue) ASSERT v.name IS UNIQUE;
-----
-
-.Import authors, venues, and articles
-[source,cypher]
-----
-CALL apoc.periodic.iterate(
-  'UNWIND ["dblp-ref-0.json", "dblp-ref-1.json", "dblp-ref-2.json", "dblp-ref-3.json"] AS file
-   CALL apoc.load.json("https://github.com/mneedham/link-prediction/raw/master/data/" + file)
-   YIELD value
-   RETURN value',
-  'MERGE (a:Article {index:value.id})
-   SET a += apoc.map.clean(value,["id","authors","references", "venue"],[0])
-   WITH a, value.authors as authors, value.references AS citations, value.venue AS venue
-   MERGE (v:Venue {name: venue})
-   MERGE (a)-[:VENUE]->(v)
-   FOREACH(author in authors | 
-     MERGE (b:Author{name:author})
-     MERGE (a)-[:AUTHOR]->(b))
-   FOREACH(citation in citations | 
-     MERGE (cited:Article {index:citation})
-     MERGE (a)-[:CITED]->(cited))', 
-   {batchSize: 1000, iterateList: true}
-);
-----
-
-.Remove articles that are missing a title
-[source,cypher]
-----
-MATCH (a:Article) 
-WHERE not(exists(a.title))
-DETACH DELETE a;
-----
-
-We can see what the imported citation graph looks like in the Neo4j Browser Visualization shown below:
-
-.Diagram showing Citation Network in Neo4j
-image::citation-graph-imported.svg[]
-
-[#co-author-graph]
-=== Building a co-author graph
-
-The dataset doesn’t contain relationships between authors describing their collaborations, but we can infer them based on finding articles authored by multiple people.
-The code below creates a `CO_AUTHOR` relationship between authors that have collaborated on at least one article:
-
-[source,cypher]
-----
-CALL apoc.periodic.iterate(
-  "MATCH (a1)<-[:AUTHOR]-(paper)-[:AUTHOR]->(a2:Author)
-   WITH a1, a2, paper
-   ORDER BY a1, paper.year
-   RETURN a1, a2, collect(paper)[0].year AS year, count(*) AS collaborations",
-  "MERGE (a1)-[coauthor:CO_AUTHOR {year: year}]-(a2)
-   SET coauthor.collaborations = collaborations",
-  {batchSize: 100}
-);
-----
-
-We create only one `CO_AUTHOR` relationship between authors that have collaborated, even if they’ve collaborated on multiple articles.
-We create a couple of properties on these relationships:
-
-* a `year` property that indicates the publication year of the first article on which the authors collaborated
-* a `collaborations` property that indicates how many articles on which the authors have collaborated
-
-.The co-authors graph
-image::co-author-graph.svg[]
-
-[#train-test-datasets]
-== Train and test datasets
-
-image::noun_Data_3403843.png[width="100px", float="right"]
-
-To avoid data leakage, we need to split our graph into training and test sub graphs.
-The GDS Library has a https://neo4j.com/docs/graph-data-science/current/alpha-algorithms/split-relationships/[Split Relationships^] algorithm that takes care of this for us.
-
-Before we use that algorithm, let's create an in-memory graph of our co-authors, by running the following query:
-
-.Create in-memory graph
-[source,cypher]
-----
-CALL gds.graph.create(
-  'linkpred', 
-  'Author', 
-  {
-    CO_AUTHOR: {
-      orientation: 'UNDIRECTED'
-    }
-  }
-);
-----
-
-.Results
-[opts=header,cols="22,22,10,10,15,10"]
-|===
-| nodeProjection                              | relationshipProjection                                                                              | graphName   | nodeCount | relationshipCount | createMillis 
-| {Author: {properties: {}, label: "Author"}} | {CO_AUTHOR: {orientation: "UNDIRECTED", aggregation: "DEFAULT", type: "CO_AUTHOR", properties: {}}} | "linkpred" | 80299     | 310448            | 53           
-|===
-
-And now we will use the Split Relationships algorithm to create both train and test in-memory graphs:
-
-.Create test graph
-[source,cypher]
-----
-CALL gds.alpha.ml.splitRelationships.mutate('linkpred', {
-  relationshipTypes: ['CO_AUTHOR'],
-  remainingRelationshipType: 'CO_AUTHOR_REMAINING',
-  holdoutRelationshipType: 'CO_AUTHOR_TESTGRAPH',
-  holdoutFraction: 0.2
-})
-YIELD createMillis, computeMillis, mutateMillis, relationshipsWritten;
-----
-
-.Results
-[opts=header]
-|===
-| createMillis | computeMillis | mutateMillis | relationshipsWritten 
-| 0            | 97            | 0            | 310448               
-|===
-
-.Create training graph
-[source,cypher]
-----
-CALL gds.alpha.ml.splitRelationships.mutate('linkpred', {
-  relationshipTypes: ['CO_AUTHOR_REMAINING'],
-  remainingRelationshipType: 'CO_AUTHOR_IGNORED_FOR_TRAINING',
-  holdoutRelationshipType: 'CO_AUTHOR_TRAINGRAPH',
-  holdoutFraction: 0.2
-})
-YIELD createMillis, computeMillis, mutateMillis, relationshipsWritten;
-----
-
-.Results
-[opts=header]
-|===
-| createMillis | computeMillis | mutateMillis | relationshipsWritten 
-|  0            | 81            | 0            | 248360
-|===
-
-[#feature-engineering]
-== Feature Engineering
-
-image:noun_engineer_94087.png[float="right", width="100px"]
-
-Now it's time to engineer some features which we’ll use to train our model.
-We are going to train a model that tries to identify missing links in the current graph, which might have happened because of misrecording of the authors who wrote a paper, resulting in missing collaborations.
-This is also referred to as _transductive learning_, and means that when we generate features we will generate them using the whole graph.
-
-
-[#pagerank]
-=== PageRank
-
-The https://neo4j.com/docs/graph-data-science/current/algorithms/page-rank/[PageRank algorithm^] computes a score that indicates the transitive influence of an author. 
-The higher the score, the more influential they are.
-
-We can compute the PageRank for each author and store the result as a node property in the in-memory graph, by running the following query:
-
-[source,cypher]
-----
-CALL gds.pageRank.mutate('linkpred',{
-  maxIterations: 20,
-  dampingFactor: 0.05,
-  relationshipTypes: ["CO_AUTHOR"],
-  mutateProperty: 'pagerank'
-})
-YIELD nodePropertiesWritten, mutateMillis, createMillis, computeMillis;
-----
-
-.Results
-[opts=header]
-|===
-| nodePropertiesWritten | mutateMillis | createMillis | computeMillis 
-| 80299                 | 0            | 0            | 62            
-|===
-
-[#triangles]
-=== Triangle Count
-
-The https://neo4j.com/docs/graph-data-science/current/algorithms/triangle-count/[Triangle Count algorithm^] computes the number of triangles that a node forms. 
-Three nodes A,B,C form a triangle if A is a co-author of B, B is a co-author of C, and C is a co-author of A.
-
-We can compute the number of triangles for each author and store the results as a node property in the in-memory graph, by running the following query:
-
-[source,cypher]
-----
-CALL gds.triangleCount.mutate('linkpred',{
-  relationshipTypes: ["CO_AUTHOR"],
-  mutateProperty: 'triangles'
-})
-YIELD nodePropertiesWritten, mutateMillis, nodeCount, createMillis, computeMillis;
-----
-
-.Results
-[opts=header]
-|===
-| nodePropertiesWritten | mutateMillis | nodeCount | createMillis | computeMillis 
-|80299                 | 0            | 80299     | 0            | 19           
-|===
-
-[#fastrp-node-embedding]
-=== Fast Random Projection
-
-
-The https://neo4j.com/docs/graph-data-science/current/algorithms/fastrp/[Fast Random Projection (FastRP) algorithm^] computes embeddings based on a node's neighborhood.
-This means that two nodes that have similar neighborhoods should be assigned similar embedding vectors.
-
-We can compute the FastRP embedding for each author and store the results as a node property in the in-memory graph, by running the following query:
-
-[source,cypher]
-----
-CALL gds.fastRP.mutate('linkpred', {
-    embeddingDimension: 250,
-    relationshipTypes: ["CO_AUTHOR_REMAINING"],
-    iterationWeights: [0, 0, 1.0, 1.0],
-    normalizationStrength:0.05,
-    mutateProperty: 'fastRP_Embedding'
-})
-YIELD nodePropertiesWritten, mutateMillis, nodeCount, createMillis, computeMillis;
-----
-
-.Results
-[opts=header]
-|===
-| nodePropertiesWritten | mutateMillis | nodeCount | createMillis | computeMillis 
-| 80299                 | 0            | 80299     | 8            | 334           
-|===
-
-There is also a version of the FastRP algorithm that takes node properties into account.
-We can compute a FastRP embedding that uses the `pagerank` and `triangles` scores for each author and store the results as a node property in the in-memory graph, by running the following query:
-
-[source,cypher]
-----
-CALL gds.beta.fastRPExtended.mutate('linkpred', {
-  propertyDimension: 45,
-  embeddingDimension: 250,
-  featureProperties: ["pagerank", "triangles"],
-  relationshipTypes: ["CO_AUTHOR_REMAINING"],
-  iterationWeights: [0, 0, 1.0, 1.0],
-  normalizationStrength:0.05,
-  mutateProperty: 'fastRP_Embedding_Extended'
-})
-YIELD nodePropertiesWritten, mutateMillis, nodeCount, createMillis, computeMillis;
-----
-
-.Results
-[opts=header]
-|===
-| nodePropertiesWritten | mutateMillis | nodeCount | createMillis | computeMillis 
-| 80299                 | 0            | 80299     | 8            | 348        
-|===
-
-[#train-model]
-== Model Training and Evaluation
-
-Now let's build a model based on these features.
-The https://neo4j.com/docs/graph-data-science/current/algorithms/linkprediction/[link prediction procedure^] trains a logistic regression model and evaluates it using the AUCPR metric. 
-
-We'll train out first model using only the `fastRP_Embedding` property.
-We can do this by running the query below:
-
-[source,cypher]
-----
-CALL gds.alpha.ml.linkPrediction.train('linkpred', {
-  trainRelationshipType: 'CO_AUTHOR_TRAINGRAPH',
-  testRelationshipType: 'CO_AUTHOR_TESTGRAPH',
-  modelName: 'model-only-embedding',
-  featureProperties: ['fastRP_Embedding'],
-  validationFolds: 5,
-  classRatio: 1.0,
-  randomSeed: 2,
-  params: [
-    {penalty: 0.25, maxIterations: 1000},
-    {penalty: 0.5, maxIterations: 1000},
-    {penalty: 1.0, maxIterations: 1000},
-    {penalty: 0.0, maxIterations: 1000}
-  ]
-})
-YIELD trainMillis, modelInfo  
-RETURN trainMillis,
-       modelInfo.bestParameters AS winningModel,    
-       modelInfo.metrics.AUCPR.outerTrain AS trainGraphScore,
-       modelInfo.metrics.AUCPR.test AS testGraphScore;
-----
-
-.Results
-[opts=header,cols="60,20,20"]
-|===
-| winningModel                        | trainGraphScore    | testGraphScore     
-| {maxIterations: 1000, penalty: 0.5} | 0.9656900400862477 | 0.9409744114683815 
-|===
-
-This looks good - the model is very good at predicting missing relationships in the graph.
-
-One tweak we could make is to specify the `linkFeatureCombiner` in the `params` used to train the model.
-By default, the `L2` feature combiner is used, which means that the `(feature(nodeA) - feature(nodeB))^2` formula is used to generate features for each pair of nodes.
-If we use `HADAMARD` instead, the `feature(nodeA) * feature(nodeB)` formula is used instead.
-With the `HADAMARD` combiner, we are saying that there's a higher probability of a relationship existing between two nodes that have a score of 100 than between two nodes that have a score of 0.
-
-We can train a model using this combiner by running the following query:
-
-[source,cypher]
-----
-CALL gds.alpha.ml.linkPrediction.train('linkpred', {
-  trainRelationshipType: 'CO_AUTHOR_TRAINGRAPH',
-  testRelationshipType: 'CO_AUTHOR_TESTGRAPH',
-  modelName: 'model-only-embedding-hadamard',
-  featureProperties: ['fastRP_Embedding'],
-  validationFolds: 5,
-  classRatio: 1.0,
-  randomSeed: 2,
-  params: [
-    {penalty: 0.25, maxIterations: 1000, linkFeatureCombiner: 'HADAMARD'},
-    {penalty: 0.5, maxIterations: 1000, linkFeatureCombiner: 'HADAMARD'},
-    {penalty: 1.0, maxIterations: 1000, linkFeatureCombiner: 'HADAMARD'},
-    {penalty: 0.0, maxIterations: 1000, linkFeatureCombiner: 'HADAMARD'}
-  ]
-})
-YIELD modelInfo  
-RETURN modelInfo.bestParameters AS winningModel,    
-       modelInfo.metrics.AUCPR.outerTrain AS trainGraphScore,
-       modelInfo.metrics.AUCPR.test AS testGraphScore;
-----
-
-[NOTE]
-====
-Don't forget that if you are not using the https://neo4j.com/docs/graph-data-science/current/installation/#installation-enterprise-edition[Enterprise Edition] of the Graph Data Science library, you can only have a limited number of models in memory at any given time, so you must drop unused models via `CALL gds.beta.model.drop('model-only-embedding')`
-====
-
-.Results
-[opts=header,cols="60,20,20"]
-|===
-| winningModel                        | trainGraphScore    | testGraphScore     
-| {maxIterations: 1000, linkFeatureCombiner: "HADAMARD", penalty: 0.25} | 0.9999845975769923 | 0.9455924656253945 
-|===
-
-That's slightly better than our previous model. 
-
-Next we're going to train models based on the other features that we generated.
-But first, let's drop all the existing models:
-
-[source,cypher]
-----
-CALL gds.beta.model.list() 
-YIELD modelInfo 
-CALL gds.beta.model.drop(modelInfo.modelName) 
-YIELD modelInfo AS info 
-RETURN info;
-----
-
-We can now train models based on combinations of features, by running the following query:
-
-
-[source,cypher]
-----
-UNWIND [
-  ["fastRP_Embedding_Extended"],
-  ["fastRP_Embedding", "pagerank", "triangles"],
-  ["fastRP_Embedding", "pagerank"],
-  ["fastRP_Embedding", "triangles"],
-  ["fastRP_Embedding"]
-] AS featureProperties
-CALL gds.alpha.ml.linkPrediction.train('linkpred', {
-  trainRelationshipType: 'CO_AUTHOR_TRAINGRAPH',
-  testRelationshipType: 'CO_AUTHOR_TESTGRAPH',
-  modelName: 'model-' + apoc.text.join(featureProperties, "-"),
-  featureProperties: featureProperties,
-  validationFolds: 5,
-  classRatio: 1.0,
-  randomSeed: 2,
-  params: [
-    {penalty: 0.25, maxIterations: 1000, linkFeatureCombiner: 'HADAMARD'},
-    {penalty: 0.5, maxIterations: 1000, linkFeatureCombiner: 'HADAMARD'},
-    {penalty: 1.0, maxIterations: 1000, linkFeatureCombiner: 'HADAMARD'},
-    {penalty: 0.0, maxIterations: 1000, linkFeatureCombiner: 'HADAMARD'}
-  ]
-})
-YIELD modelInfo  
-RETURN modelInfo;
-----
-
-We can return a stream of all the models and their accuracies by running the following query:
-
-[source,cypher]
-----
-CALL gds.beta.model.list() 
-YIELD modelInfo
-RETURN modelInfo.modelName AS modelName,
-       modelInfo.bestParameters AS winningModel,    
-       modelInfo.metrics.AUCPR.outerTrain AS trainGraphScore,
-       modelInfo.metrics.AUCPR.test AS testGraphScore
-ORDER BY testGraphScore DESC;
-----
-
-.Results
-[opts=header,cols="30,30,20,20"]
-|===
-| modelName                        | winningModel                                                          | trainGraphScore    | testGraphScore     
-| "model-fastRP_Embedding-pagerank"           | {maxIterations: 1000, linkFeatureCombiner: "HADAMARD", penalty: 1.0}  | 0.9999889998620444 | 0.9577993987170548 
-| "model-fastRP_Embedding-triangles"          | {maxIterations: 1000, linkFeatureCombiner: "HADAMARD", penalty: 0.0}  | 0.9990012589999202 | 0.9457656238136779 
-| "model-fastRP_Embedding"                    | {maxIterations: 1000, linkFeatureCombiner: "HADAMARD", penalty: 0.25} | 0.9999845975769923 | 0.9455926563716637 
-| "model-fastRP_Embedding-pagerank-triangles" | {maxIterations: 1000, linkFeatureCombiner: "HADAMARD", penalty: 0.0}  | 0.9987023730262333 | 0.9376955436526965 
-| "model-fastRP_Embedding_Extended"           | {maxIterations: 1000, linkFeatureCombiner: "HADAMARD", penalty: 0.0}  | 0.9467628798627221 | 0.8689537717717958 
-|===
-
-Interestingly the best model combines the `fastRP_Embedding` and `pagerank` features, but the model based on the `fastRP_Embedding_Extended` embedding that also includes the `pagerank` features does much worse.
-
-[#next-steps]
-== Next Steps
-
-We've trained a reasonably good model that can predict potential mislabelling in citations.
-A good next step would be to train a model that predicts future collaborations (_inductive learning_).
-For an example of how to do this using scikit-learn, see xref:graph-data-science:link-prediction/scikit-learn.adoc[].
\ No newline at end of file
diff --git a/modules/graph-data-science/pages/link-prediction/index.adoc b/modules/graph-data-science/pages/link-prediction/index.adoc
deleted file mode 100644
index 7006c3c5..00000000
--- a/modules/graph-data-science/pages/link-prediction/index.adoc
+++ /dev/null
@@ -1,145 +0,0 @@
-= Link Prediction
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, machine-learning, link-prediction
-:description: This guide explains concepts related to link prediction, which is used to predict missing or future links in graphs.
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/algorithms/linkprediction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will learn about concepts related to link prediction, which is used to predict missing or future links in graphs.
-
-[role=expertise {level}]
-{level}
-
-[#what-is-link-prediction]
-== What is link prediction?
-
-image:link.png[float="right", width="100px"]
-
-Link prediction has been around for a long time, but was popularised by a paper written by Jon Kleinberg and David Liben-Nowell in 2004, titled link:https://www.cs.cornell.edu/home/kleinber/link-pred.pdf[The Link Prediction Problem for Social Networks^].
-
-Kleinberg and Liben-Nowell approach this problem from the perspective of social networks, asking the question:
-
-[quote, Kleinberg and Liben-Nowell ]
-____
-Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network.
-____
-
-More recently, Dr Jim Webber, Chief Scientist at Neo4j, explains how the evolution of a graph can be determined from its structure in his entertaining GraphConnect San Francisco 2015 talk where he explained World War 2 using graphs.
-
-++++
-
-++++
-
-[#what-is-it-used-for]
-== Use Cases
-
-image:use-case.png[float="right", width="100px"]
-
-Apart from predicting World Wars or friendships in a social network where else might we want to predict links?
-
-We could predict future associations between people in a terrorist network, associations between molecules in a biology network, potential co-authorships in a citation network, interest in an artist or artwork, to name just a few use cases.
-
-In each these examples, predicting a link means that we are predicting some future behaviour.
-For example in a citation network, we’re actually predicting the action of two people collaborating on a paper.
-
-[#algorithms]
-== Link Prediction algorithms
-
-image:algorithm.png[float="right", width="100px"]
-
-Kleinberg and Liben-Nowell describe a set of methods that can be used for link prediction.
-These methods compute a score for a pair of nodes, where the score could be considered a **measure of proximity or “similarity”** between those nodes based on the graph topology.
-The closer two nodes are, the more likely there will be a relationship between them.
-
-A list of the methods described in the paper are shown below:
-
-.Link Prediction Algorithms
-image::link-prediction-algorithms.png[width="500px"]
-
-The Neo4j Data Science Library supports some of the distance metrics described in the paper, as well as other measures of node proximity.
-
-link:https://neo4j.com/docs/graph-data-science/current/algorithms/linkprediction/[Link Prediction algorithms reference documentation^,role=more information]
-
-[#approaches]
-== Link Prediction approaches
-
-While we can use the link prediction algorithms to compute node proximity scores, we need to do something with those scores to predict links.
-There are two approaches described in the literature:
-
-Use the measures directly ::
-We can use the scores from the link prediction algorithms directly.
-With this approach we would **set a threshold value** above which we would predict that a pair of nodes will have a link.
-In the example above we might say that every pair of nodes that has a preferential attachment score above 3 would have a link, and any with 3 or less would not.
-
-Supervised learning ::
-We can take a supervised learning approach where we **use the scores as features** to train a binary classifier. The binary classifier then predicts whether a pair of nodes will have a link.
-
-Guillaume Le Floch describes these two approaches in more detail in his blog post https://hackernoon.com/link-prediction-in-large-scale-networks-f836fcb05c88?gi=b86a42e1c8d4[Link Prediction In Large-Scale Networks^].
-
-
-[#selected-ml-model]
-== Selecting a machine learning model
-
-image:ml-model.png[float="right", width="100px"]
-
-If we're using the supervised learning approach, one of the first things we need to do is decide which machine learning model we're going to use.
-
-Many of the link prediction measures that we’ve covered so far are computed using similar data, and when it comes to training a machine learning model this means there is a https://christophm.github.io/interpretable-ml-book/interaction.html[feature interaction^] issue that we need to deal with.
-Some machine learning models assume that the features they received are independent.
-Providing such a model with features that don’t meet this assumption will lead us to predicts things with low accuracy.
-If we choose one of these models we would need to exclude features with high interaction.
-
-Alternately, we can choose a model where feature interaction isn’t so problematic.
-https://scikit-learn.org/stable/modules/ensemble.html[Ensemble methods^] tend to work well as they don’t make this assumption on their input data.
-This could be a gradient boosting classifier as described in Guillaume Le Floch's blog post or a random forest classifier as http://cs229.stanford.edu/proj2016/report/JulianLu-Application-of-Machine-Learning-to-Link-Prediction-report.pdf[described in a paper^] written by Kyle Julian and Wayne Lu.
-
-[#train-test-data-sets]
-== Train and test data sets
-
-image:leakage.png[float="right", width="100px"]
-
-We also need to decide how we're going to split our train and test datasets.
-We can’t randomly split the data, using a built-in function from our machine learning library of choice, as this could lead to data leakage.
-
-
-Data leakage can occur when data outside of your training data is inadvertently used to create your model.
-This can easily happen when working with graphs because pairs of nodes in our training set may be connected to those in the test set.
-
-When we compute link prediction measures over that training set the **measures computed contain information from the test set** that we’ll later evaluate our model against.
-
-Instead we need to split our graph into training and test sub graphs.
-If our graph has a concept of time our life is easy — we can split the graph at a point in time and the training set will be from before the time, the test set after.
-
-This is still not a perfect solution and we’ll need to try and ensure that the general network structure in the training and test sub graphs is similar.
-
-Once we’ve done that we’ll have pairs of nodes in our train and test set that have relationships between them.
-They will be the **positive examples** in our machine learning model.
-
-Now for the **negative examples**.
-The simplest approach would be to use all pair of nodes that don’t have a relationship. The problem with this approach is that there are significantly more examples of pairs of nodes that don’t have a relationship than there are pairs of nodes that do.
-The maximum number of negative examples is equal to:
-
-[NOTE]
-====
-# negative examples = (# nodes)² - (# relationships) - (# nodes)
-====
-
-i.e. the number of nodes squared, minus the relationships that the graph has, minus self relationships.
-
-If we use all of these negative examples in our training set we will have a massive class imbalance — there are many negative examples and relatively few positive ones.
-
-A model trained using data that’s this imbalanced will achieve very high accuracy by **predicting that any pair of nodes don’t have a relationship** between them, which is not quite what we want!
-
-So we need to try and reduce the number of negative examples.
-An approach described in several link prediction papers is to use pairs of nodes that are a **specific number of hops away from each other**.
-
-This will significantly reduce the number of negative examples, although there will still be a lot more negative examples than positive.
-
-To solve this problem we either need to **down sample** the negative examples or **up sample** the positive examples.
diff --git a/modules/graph-data-science/pages/link-prediction/scikit-learn.adoc b/modules/graph-data-science/pages/link-prediction/scikit-learn.adoc
deleted file mode 100644
index 29537508..00000000
--- a/modules/graph-data-science/pages/link-prediction/scikit-learn.adoc
+++ /dev/null
@@ -1,432 +0,0 @@
-= Link Prediction with GDSL and scikit-learn
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, machine-learning, link-prediction
-:description: This guide explains how to solve a link prediction problem using a scikit-learn binary classifier.
-:page-type: How-To Guide
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/introduction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will learn how to solve a link prediction problem using a scikit-learn binary classifier and the Graph Data Science Library.
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) and the link:/download-center/#algorithms[Graph Data Science Library^] downloaded and installed.
-You will also need to install the Python https://scikit-learn.org/[scikit-learn^] library.
-
-[role=expertise {level}]
-{level}
-
-// ++++
-// 
-// ++++
-
-Link Prediction techniques are used to predict future or missing links in graphs.
-In this guide we're going to use these techniques to predict future co-authorships using scikit-learn and link prediction algorithms from the Graph Data Science Library.
-
-[NOTE]
-====
-The code examples used in this guide can be found in the https://github.com/neo4j-examples/link-prediction[neo4j-examples/link-prediction^] GitHub repository.
-For background reading on link prediction, see the xref:link-prediction/index.adoc[] guide.
-====
-
-[#citation-graph]
-== Citation Graph
-
-image:noun_citation_2276559.png[float="right", width="100px"]
-
-In this guide, we’re going to use data from the https://aminer.org/citation[DBLP Citation Network^], which includes citation data from various academic sources.
-The full dataset is very large, but we're going to use a subset that contains data from a few Software Development Conferences.
-
-A screenshot of the available datasets is shown below:
-
-.Citation Networks
-image::citation-graph.png[]
-
-You can find https://github.com/neo4j-examples/link-prediction/blob/master/notebooks/01_DataLoading.ipynb[instructions for importing the data^] in the project repository.
-The following diagram shows what the data looks like once we’ve imported into Neo4j:
-
-.Diagram showing Citation Network in Neo4j
-image::citation-graph-imported.svg[]
-
-[#co-author-graph]
-=== Building a co-author graph
-
-The dataset doesn’t contain relationships between authors describing their collaborations, but we can infer them based on finding articles authored by multiple people.
-The code below creates a `CO_AUTHOR` relationship between authors that have collaborated on at least one article:
-
-[source,python]
-----
-include::example$link-prediction/py/02_Co-Author_Graph.py[tag="imports"]
-
-include::example$link-prediction/py/02_Co-Author_Graph.py[tag="driver"]
-
-include::example$link-prediction/py/02_Co-Author_Graph.py[tag="data-import"]
-----
-
-We create only one `CO_AUTHOR` relationship between authors that have collaborated, even if they’ve collaborated on multiple articles.
-We create a couple of properties on these relationships:
-
-* a `year` property that indicates the publication year of the first article on which the authors collaborated
-* a `collaborations` property that indicates how many articles on which the authors have collaborated
-
-.The co-authors graph
-image::co-author-graph.svg[]
-
-[#train-test-datasets]
-== Train and test datasets
-
-image::noun_Data_3403843.png[width="100px", float="right"]
-
-To avoid data leakage, we need to split our graph into training and test sub graphs.
-We are lucky that our citation graph contains time information that we can split on.
-We will create training and test graphs by splitting the data on a particular year.
-
-But which year should we split on?
-Let’s have a look at the distribution of the first year that co-authors collaborated:
-
-[source,python]
-----
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="imports"]
-
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="determine-split"]
-----
-
-.Chart showing distribution of year of collaboration
-image::determine-split.png[]
-
-It looks like 2006 would act as a good year on which to split the data, because it will give us a reasonable amount of data for each of our sub graphs.
-We’ll take all the co-authorships from 2005 and earlier as our training graph, and everything from 2006 onwards as the test graph.
-
-Let’s create explicit `CO_AUTHOR_EARLY` and `CO_AUTHOR_LATE` relationships in our graph based on that year.
-The following code will create these relationships for us:
-
-[source,python]
-----
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="sub-graphs"]
-----
-
-This split leaves us with 81,096 relationships in the early graph, and 74,128 in the late one.
-This is a split of 52–48.
-That’s a higher percentage of values than we’d usually have in our test graph, but it should be ok.
-The relationships in these sub graphs will act as the **positive examples** in our train and test sets, but we need some negative examples as well. The negative examples are needed so that our model can learn to distinguish nodes that should have a link between them and nodes that should not.
-
-As is often the case in link prediction problems, there are a lot more negative examples than positive ones.
-Instead of using almost all possible pairs, we’ll use pairs of nodes that are **between 2 and 3 hops away** from each other.
-This will give us a much more manageable amount of data to work with.
-
-We can generate these pairs by running the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="positive-negative-examples"]
-----
-
-Let's combine the `train_existing_links` and `train_missing_links` DataFrames and check how many positive and negative examples we have:
-
-[source,python]
-----
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="count-positive-negative"]
-----
-
-[source, text]
-----
-Negative examples: 973019
-Positive examples: 81096
-----
-
-We have more than 10 times as many negative examples as positive ones.
-So we still have a big class imbalance.
-To solve this issue we can either up sample the positive examples or down sample the negative examples.
-
-[NOTE]
-====
-The advantages and disadvantages of each approach are described in https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets[Resampling strategies for imbalanced datasets^].
-====
-
-We’re going to go with the downsampling approach.
-We can downsample the negative examples using the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="down-sample"]
-----
-
-[source, text]
-----
-Random downsampling:
-1    81096
-0    81096
-Name: label, dtype: int64
-----
-
-We'll now do the same thing for the test data, using the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="test-positive-negative-examples"]
-
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="count-test-positive-negative"]
-
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="down-sample-test"]
-----
-
-[source, text]
-----
-Negative examples: 1265118
-Positive examples: 74128
-
-Random downsampling:
-1    74128
-0    74128
-Name: label, dtype: int64
-----
-
-
-Before we move on, let's have a look at the contents of our train and test DataFrames:
-
-[source,python]
-----
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="train-preview"]
-----
-
-.Train
-[opts=header, cols="2,2,1", format="csv"]
-|===
-include::example$link-prediction/notebooks/data/df_train_under_basic_sample.csv[]
-|===
-
-[source,python]
-----
-include::example$link-prediction/py/03_Train_Test_Split.py[tag="test-preview"]
-----
-
-
-.Test
-[opts=header, cols="2,2,1", format="csv"]
-|===
-include::example$link-prediction/notebooks/data/df_train_under_basic_sample.csv[]
-|===
-
-[#feature-engineering]
-== Feature Engineering
-
-image:noun_engineer_94087.png[float="right", width="100px"]
-
-Now it’s time to engineer some features which we’ll use to train our model.
-We're going to create three types of features:
-
-<<#link-prediction-features,Link prediction measures>> :: features from running the link prediction algorithms
-<<#triangles-clustering-coefficient, Triangles and Clustering Coefficient>> :: features from running the triangles and clustering coefficient algorithms
-<<#community-detection,Community Detection>> :: features from running the Louvain and Label Propagation algorithms
-
-[#link-prediction-features]
-=== Link prediction measures
-
-We’ll start by creating some features using https://neo4j.com/docs/graph-data-science/current/algorithms/linkprediction/[link prediction^] functions.
-We can do this by applying the following function over the test and train DataFrames:
-
-[source,python]
-----
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="graphy-features"]
-
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="apply-graphy-features"]
-----
-
-This function adds three columns to the train and test DataFrames:
-
-* `cn` - common neighbors score
-* `pa` - preferential attachment score
-* `tn` - total neighbors score
-
-For the training DataFrame we compute these metrics based only on the early graph, whereas for the test DataFrame we’ll compute them across the whole graph.
-
-[#triangles-clustering-coefficient]
-=== Triangles and Clustering Coefficient
-
-Now we’re going to add some new features that are generated using the https://neo4j.com/docs/graph-data-science/current/algorithms/triangle-count/[triangles^] and https://neo4j.com/docs/graph-data-science/current/algorithms/local-clustering-coefficient/[clustering coefficient^] algorithms, where:
-
-* Triangle count computer the number of triangles that each node forms.
-* Clustering coefficient computes the likelihood that its neighbors are also connected.
-
-We can compute these metrics for the nodes by running the following code:
-
-.Triangles
-[source,python]
-----
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="train-triangles"]
-
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="test-triangles"]
-----
-
-.Clustering Coefficient
-[source,python]
-----
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="train-coefficient"]
-
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="test-coefficient"]
-----
-
-And we can add the metrics to our test and train DataFrames by running the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="triangles-coefficient-features"]
-
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="apply-triangles-coefficient-features"]
-----
-
-[NOTE]
-====
-These measures are different than the ones we’ve used so far, because rather than being computed based on the pair of nodes, they are node specific measures.
-We can’t simply add these values to our DataFrame as `node1Triangles` or `node1Coeff` because we have no guarantee over the order of nodes in the pair.
-We need to come up with an approach that is agnostic of the order
-We can do this by taking the average of the values, the product of the values, or by computing the minimum and maximum value, as we do here.
-====
-
-[#community-detection]
-=== Community Detection
-
-Finally we'll add features based on community detection algorithms.
-Community detection algorithms evaluate how a group is clustered or partitioned.
-Nodes are considered more similar to nodes that fall in their community than to nodes in other communities.
-
-We'll run two community detection algorithms over the train and test sub graphs - Label Propagation and Louvain.
-First, Label Propagation:
-
-.Label Propagation
-[source,python]
-----
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="train-lpa"]
-
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="test-lpa"]
-----
-
-And now Louvain.
-The Louvain algorithm returns intermediate communities, which are useful for finding fine grained communities that exist in a graph.
-We'll add a property to each node containing the community revealed on the first iteration of the algorithm:
-
-.Louvain
-[source,python]
-----
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="train-louvain"]
-
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="test-louvain"]
-----
-
-And we can check whether a pair of nodes are in the same community and add the result to our test and train DataFrames by running the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="community-features"]
-
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="apply-community-features"]
-----
-
-We've now added all of the features, so let's have a look at the contents of our Train and Test DataFrames:
-
-[source,python]
-----
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="train-after-features"]
-----
-
-.Train DataFrame
-[opts=header, format="csv", cols="5,17,15,17,15,6,5,5,5,10"]
-|===
-include::example$link-prediction/notebooks/data/df_train_under_sample.csv[]
-|===
-
-[source,python]
-----
-include::example$link-prediction/py/04_Model_Feature_Engineering.py[tag="test-after-features"]
-----
-
-.Test DataFrame
-[opts=header, format="csv", cols="5,17,15,17,15,6,5,5,5,10"]
-|===
-include::example$link-prediction/notebooks/data/df_test_under_sample.csv[]
-|===
-
-[#selecting-model]
-== Model Selection
-
-image:noun_Random Forest_1503830.png[float="right", width="100px"]
-
-Now that we've generated all our features, it's time to create our classifier.
-We're going to create a random forest.
-
-This method is well suited as our data set will be comprised of a mix of strong and weak features.
-While the weak features will sometimes be helpful, the random forest method will ensure we don’t create a model that over fits our training data.
-
-We can create this model with the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/05_Train_Evaluate_Model.py[tag="imports"]
-
-include::example$link-prediction/py/05_Train_Evaluate_Model.py[tag="create-classifier"]
-----
-
-[#train-model]
-== Model Training
-
-Now let's build a model based on all these features.
-We can train the random forest model against the train set by running the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/05_Train_Evaluate_Model.py[tag="train-model"]
-----
-
-[#evaluate-model]
-== Model Evaluation
-
-image:noun_evaluation_2404409.png[float="right", width="100px"]
-
-We’re going to evaluate the quality of our model by computing its accuracy, precision, and recall.
-The diagram below, taken from the https://neo4j.com/graph-algorithms-book/[O’Reilly Graph Algorithms Book^], explains how each of these metrics are computed.
-
-.Accuracy measures
-image::model-evaluation.png[]
-
-scikit-learn has built in functions that we can use for this.
-The following function will help with this:
-
-[source,python]
-----
-include::example$link-prediction/py/05_Train_Evaluate_Model.py[tag="evaluation-imports"]
-
-include::example$link-prediction/py/05_Train_Evaluate_Model.py[tag="evaluation-functions"]
-----
-
-We can evaluate our model by running the following code:
-
-[source,python]
-----
-include::example$link-prediction/py/05_Train_Evaluate_Model.py[tag="test-model"]
-----
-
-.Results
-[opts=header, format="csv"]
-|===
-include::example$link-prediction/notebooks/data/model-eval.csv[]
-|===
-
-Our model has done pretty well at predicting whether there is likely to be a co-authorship between a pair of authors.
-It scores above 96% on all of the evaluation metrics.
-
-[#next-steps]
-== Next Steps
-
-We've already got a good model, but can we do better?
-
-Perhaps we could try using a different algorithm, or even the same algorithm with different hyper-parameters.
-Or maybe we can add more features based on the results of other algorithms.
-
-If you have any ideas or questions, please create an issue or PR on the https://github.com/neo4j-examples/link-prediction[neo4j-examples/link-prediction^] GitHub repository.
diff --git a/modules/graph-data-science/pages/neuler-no-code-graph-algorithms.adoc b/modules/graph-data-science/pages/neuler-no-code-graph-algorithms.adoc
deleted file mode 100644
index 14e2594a..00000000
--- a/modules/graph-data-science/pages/neuler-no-code-graph-algorithms.adoc
+++ /dev/null
@@ -1,182 +0,0 @@
-= NEuler: No-code graph algorithms
-:level: Beginner
-:page-level: Beginner
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, graph-algorithms, graph-search, graph-apps
-:description: NEuler is the Graph Data Science Playground, a no-code way of learning how to use Neo4j's graph algorithms.
-:page-ogimage: https://neo4j.com/developer/graph-data-science/_images/youtube-neuler.png
-:page-aliases: ROOT:neuler-no-code-graph-algorithms.adoc
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/introduction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will learn about NEuler: The Graph Data Science Playground, a graph app that helps onboarding with the Graph Data Science Library.
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) downloaded and installed to use NEuler.
-
-[role=expertise]
-{level}
-
-[#overview-neuler]
-== What is NEuler?
-
-image:https://dist.neo4j.com/wp-content/uploads/20200729014057/220px-Leonhard_Euler.jpg[float="right", width="100px"]
-
-NEuler is a no-code UI that helps users onboard with the https://neo4j.com/graph-data-science-library[Neo4j Graph Data Science Library^].
-It supports running each of the graph algorithms in the library, viewing the results, and also provides the Cypher queries to reproduce the results.
-
-++++
-
-++++
-
-We will use the terms 'Neuler' and 'The Graph Data Science Playground' interchangeably in this guide.
-
-[#installing-neuler]
-== Installing NEuler
-
-NEuler is a link:/developer/graph-apps/[Graph Application] (Graph App) that can be installed from the Graph App Gallery that comes installed by default on the xref:ROOT:neo4j-desktop.adoc[Neo4j Desktop].
-
-++++
-
-++++
-
-We can launch the Graph Apps Gallery by clicking on the right hand side of the 'Open' menu underneath a running database:
-
-.Launching the Graph Apps Gallery (Neo4j Desktop 1.3.3)
-image::https://dist.neo4j.com/wp-content/uploads/20200729013741/launch-graph-apps-gallery.png[]
-
-Once we've done that, we'll see the following screen.
-
-.The Graph Apps Gallery
-image::https://dist.neo4j.com/wp-content/uploads/20200729015450/graph-apps-gallery.png[]
-
-We can see the Graph Data Science Playground on the first row and, if we're using Mac or Windows, we can install it automatically by clicking on the 'Install' button.
-
-If we're using a Linux based Operating System we'll need to install it manually.
-We can get the installation link by clicking on the image:get-link.png[width="20px"] button.
-Copy that link onto the clipboard and then paste it into the 'Install' section of the image:graph-apps-button.png[width="20px"] sub menu:
-
-[NOTE]
-====
-The installation link is https://neo.jfrog.io/neo/api/npm/npm/neuler[https://neo.jfrog.io/neo/api/npm/npm/neuler^] in case you don't want to get it via the Graph Apps Gallery!
-====
-
-.Installing NEuler, The Graph Data Science Playground
-image::https://dist.neo4j.com/wp-content/uploads/20200729021819/install-gds.png[]
-
-
-Once we've done that, we're can launch NEuler.
-It will now appear under the menu that we get by clicking on the right side of the 'Open' button under our database, and the first time that we click on it we'll be asked to install the dependencies:
-
-.Launching The Graph Data Science Playground
-image::https://dist.neo4j.com/wp-content/uploads/20200729023043/install-dependencies.png[]
-
-[NOTE]
-====
-The Graph Algorithms Playground won't work unless you install those dependencies, so make sure you click on the 'Install dependencies' button.
-====
-
-Once the dependencies have been installed, The Graph Algorithms Playground will launch:
-
-.The Graph Data Science Playground - Login Screen
-image::https://dist.neo4j.com/wp-content/uploads/20201130033414/gds-login.png[]
-
-The credentials for the active database are pre-populated, but we can connect NEuler to any Neo4j database that has the APOC and GDS dependencies installed.
-If we click on the `Conenct` button, the dependencies will be checked, and we'll see the following screen:
-
-.The Graph Data Science Playground - Select Database Screen
-image::https://dist.neo4j.com/wp-content/uploads/20201130033411/gds-select-database.png[]
-
-By default the 'neo4j' database is selected, but we can change the selection using the dropdown.
-Let's go with the default database and press the `Select database` button.
-We'll now see the following screen:
-
-.The Graph Data Science Playground - Home Screen
-image::https://dist.neo4j.com/wp-content/uploads/20201130034128/gds-home-screen.png[]
-
-We're now ready to start using the playground.
-
-[#sample-dataset]
-== Loading sample datasets
-
-Our database doesn't contain any data at the moment, but luckily NEuler contains some sample datasets that will help us learn how to use the library.
-We're going to use the Game of Thrones graph for the rest of this guide, and we can import this by following the screens in the GIF below:
-
-.The Graph Data Science Playground - Importing the Game of Thrones dataset
-image::https://dist.neo4j.com/wp-content/uploads/20201130034736/gds-sample-dataset.gif[]
-
-It will take about 20 seconds to get the data loaded and once its imported, the home screen will update to look like this:
-
-.The Graph Data Science Playground - Home Screen
-image::https://dist.neo4j.com/wp-content/uploads/20201130035032/gds-home-with-data.png[]
-
-Now that we've got some data loaded, it's time to start using the algorithms.
-
-[#configuring-algorithms]
-== Configuring algorithms
-
-We'll click on the `Run Single Algorithm` link on the top menu, which will take us to the following screen:
-
-.Choosing an algorithm
-image::https://dist.neo4j.com/wp-content/uploads/20201130035327/gds-run-single-algorithm.png[]
-
-On this screen we can select an algorithm from any of the categories, configure the projected graph that the algorithm will be run against, as well as other parameters for more complex algorithms.
-
-The form for configuring the PageRank algorithm, one of the most well known centrality algorithms, is shown in the screenshot below:
-
-.Configuring the PageRank algorithm
-image::https://dist.neo4j.com/wp-content/uploads/20201130035546/gds-select-pagerank.png[]
-
-Once we're happy with the configuration, we can execute the algorithm by pressing the 'Run' button.
-
-[#viewing-results]
-== Exploring results
-
-Results from running the algorithm will be shown on the `Results` tab.
-The output from running the PageRank algorithm against a projected graph of the interactions between Games of Thrones characters in Season 1 of the popular TV show can be seen in the diagram below:
-
-.Running the PageRank algorithm against Season 1 of Game of Thrones
-image::https://dist.neo4j.com/wp-content/uploads/20201130035819/gds-pagerank-results.png[]
-
-We can see that Ned is the most important character in the show in the early days.
-If we want to compute the results for another season, we can do that by changing the 'Relationship Type'.
-
-We can also view the results in chart or graph visualization formats.
-We can see the chart representation of PageRank run against the Game of Thrones graph in the screenshot below:
-
-.A chart showing results of running the PageRank algorithm
-image::https://dist.neo4j.com/wp-content/uploads/20201130040035/gds-chart.png[]
-
-And a graph visualization of the algorithm run in the diagram below:
-
-.A graph visualization of running the PageRank algorithm
-image::https://dist.neo4j.com/wp-content/uploads/20201130040032/gds-graph-viz.png[]
-
-We can change configure the properties that select the caption, node size, and node color by using the dropdown menus above the visualization.
-
-[#seeing-code]
-== Seeing the code
-
-Running algorithms in NEuler is intended as the first step in the process of learning how to use graph algorithms.
-The next step is learning how to run the algorithms using the underlying procedures, and NEuler helps with this by describing the procedure calls in the `Code` tab.
-The procedure call and parameter setup for running the PageRank algorithm is shown in the diagram below:
-
-.The code for running the PageRank algorithm
-image::https://dist.neo4j.com/wp-content/uploads/20201130040148/gds-code-view.png[]
-
-We can use the 'Copy' button on the top right of each window to copy the code fragment to our clipboard.
-Alternatively, we can create a Neo4j Browser guide by clicking on the 'Send to Neo4j Browser' button.
-
-
-[#resources]
-== Resources
-
-* https://medium.com/neo4j/introducing-neuler-the-graph-algorithms-playground-d81042cfcd56[Introducing NEuler — The Graph Algorithms Playground^]
-* https://www.youtube.com/watch?v=zZZFqAX-PH0&feature=emb_title[Desktop Graph Analytics: For The Throne (Neo4j Online Meetup #54)^]
-* https://medium.com/neo4j/the-graph-algorithms-playground-and-graph-data-science-library-69575a0fb329[The Graph Algorithms Playground and Graph Data Science Library^]
diff --git a/modules/graph-data-science/pages/nlp/entity-extraction.adoc b/modules/graph-data-science/pages/nlp/entity-extraction.adoc
deleted file mode 100644
index 5728f452..00000000
--- a/modules/graph-data-science/pages/nlp/entity-extraction.adoc
+++ /dev/null
@@ -1,439 +0,0 @@
-= Entity Extraction with APOC NLP
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-platform, nlp, tools, similarity, apoc, analytics, search, recommendations, labs
-:description: In this guide we'll learn how to do entity extraction using the AWS, GCP, and Azure NLP libraries.
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/introduction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide we'll learn how to do entity extraction using the AWS, GCP, and Azure NLP libraries.
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.2 or later) downloaded and installed.
-
-[role=expertise {level}]
-{level}
-
-[#intro-to-entity-extraction]
-== Intro to Entity Extraction
-
-Entity Extraction takes unstructured text and returns a list of named entities contained within that text.
-
-AWS, GCP, and Azure each provide NLP APIs, which are wrapped by the https://neo4j.com/labs/apoc/4.2/overview/apoc.nlp/[`apoc.nlp`^] procedures in the https://neo4j.com/labs/apoc/4.2/[APOC Library^].
-In this guide we're going to learn how to use these procedures to both build and then query a graph of entities constructed using these procedures.
-
-[#tools-used]
-== Tools Used
-
-This guide uses the following tools and versions:
-
-[opts="header"]
-|===
-| Tool | Version
-| https://neo4j.com/download/[Neo4j^] | 4.2.0
-| https://github.com/neo4j-contrib/neo4j-apoc-procedures[APOC^] + NLP Dependencies | 4.2.0.0
-|===
-
-[#prerequisites]
-== Prerequisites
-
-Before we start using the APOC NLP procedures, there are some prerequisites that we need to setup.
-
-[#install-dependencies]
-=== Install Dependencies
-
-The APOC NLP procedures have dependencies on Kotlin and client libraries that are not included in the APOC Library.
-
-These dependencies are included in the `apoc-nlp-dependencies-.jar`, which can be downloaded from the https://github.com/neo4j-contrib/neo4j-apoc-procedures/releases[releases page^].
-Once that file is downloaded, it should be placed in the plugins directory and the Neo4j Server restarted.
-
-[#setup-api-key]
-=== Setting up API Key
-
-Now we need to setup an API key for the cloud platform that we want to use.
-
-[.tabs]
-.AWS
---
-We can generate an Access Key and Secret by following the instructions at https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html[docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html^].
-Once we've done that, we can populate and execute the following commands to create parameters that contains these details.
-
-.The following define the `apiKey` and `apiSecret` parameters
-[source,cypher]
-----
-:param awsApiKey => ("");
-:param awsApiSecret => ("");
-----
---
-
-.GCP
---
-We can generate an API Key that has access to the Cloud Natural Language API by going to https://console.cloud.google.com/apis/credentials[console.cloud.google.com/apis/credentials^].
-Once we've created a key, we can populate and execute the following command to create a parameter that contains these details.
-
-The following defines the `apiKey` parameter:
-
-[source,cypher]
-----
-:param gcpApiKey => ("")
-----
---
-
-.Azure
---
-We can generate an API key and URL by following the instructions in the  https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/quickstarts/text-analytics-sdk[Quickstart: Use the Text Analytics client library^] article.
-Once we've done that, we should be able to see a page listing our credentials, similar to the screenshot below:
-
-.Azure Text Analytics credentials
-image::https://dist.neo4j.com/wp-content/uploads/20201201060459/aws-keys.png[]
-
-In this case our API URL is `https://neo4j-entity-extraction-developer-guide.cognitiveservices.azure.com/`, and we can use either of the hidden keys.
-
-Let's populate and execute the following commands to create parameters that contains these details.
-
-.The following define the `apiKey` and `apiSecret` parameters
-[source,cypher]
-----
-:param azureApiKey => ("");
-:param azureApiUrl => ("");
-----
---
-
-
-[#import-text-documents]
-== Import text documents
-
-We're going to import some text documents from the https://www.theguardian.com/football/rss[Guardian Football RSS feed^].
-We can use the https://neo4j.com/labs/apoc/4.1/import/xml/[`apoc.load.xml`^] for this, as shown in the following query:
-
-----
-CALL apoc.load.xml("https://www.theguardian.com/football/rss", "rss/channel/item")
-YIELD value
-WITH [child in value._children WHERE child._type = "title" | child._text][0] AS title,
-     [child in value._children WHERE child._type = "link" | child._text][0] AS guid,
-     apoc.text.regreplace(
-       [child in value._children WHERE child._type = "description" | child._text][0],
-       '<[^>]*>',
-       ' '
-     ) AS body,
-     [child in value._children WHERE child._type = "category" | child._text] AS categories
-MERGE (a:Article {id: guid})
-SET a.body = body, a.title = title
-WITH a, categories
-CALL {
-  WITH a, categories
-  UNWIND categories AS category
-  MERGE (c:Category {name: category})
-  MERGE (a)-[:IN_CATEGORY]->(c)
-  RETURN count(*) AS categoryCount
-}
-RETURN a.id AS articleId, a.title AS title, categoryCount;
-----
-
-We can see a Neo4j Browser visualization of the imported graph in the diagram below:
-
-.Guardian football graph
-image::https://dist.neo4j.com/wp-content/uploads/20201130092104/graph-35.svg[]
-
-
-[#build-entity-graph]
-== Building an entity graph
-
-Now we're going to build our entity graph.
-We're going to use a combination of `apoc.periodic.iterate` and the cloud specific streaming procedures to:
-
-* Extract entities for each article, filtering for specific entity types
-* Create a node for each entity
-* Create a relationship from each entity to the article
-
-We can do this with the following query:
-
-[.tabs]
-
-.AWS
---
-[source,cypher]
-----
-CALL apoc.periodic.iterate(
-  "MATCH (a:Article) WHERE not(exists(a.processed)) RETURN a",
-  "CALL apoc.nlp.aws.entities.stream([item in $_batch | item.a], {
-          key: $awsApiKey,
-          secret: $awsApiSecret,
-          nodeProperty: 'body'
-   })
-   YIELD node AS a, value
-   SET a.processed = true
-   WITH a, value
-   UNWIND value.entities AS entity
-   WITH a, entity
-   WHERE entity.type IN ['COMMERCIAL_ITEM', 'PERSON', 'ORGANIZATION', 'LOCATION', 'EVENT']
-
-   CALL apoc.merge.node(
-     ['Entity', apoc.text.capitalize(toLower(entity.type))],
-     {name: entity.text}, {}, {}
-   )
-   YIELD node AS e
-   MERGE (a)-[entityRel:AWS_ENTITY]->(e)
-   SET entityRel.score = entity.score
-   RETURN count(*)",
-  { batchMode: "BATCH_SINGLE",
-    batchSize: 25,
-    params: {awsApiKey: $awsApiKey, awsApiSecret: $awsApiSecret}}
-)
-YIELD batches, total, timeTaken, committedOperations, errorMessages, batch, operations
-RETURN batches, total, timeTaken, committedOperations, errorMessages, batch, operations;
-----
-
-.Results
-[opts="header"]
-|===
-| batches | total | timeTaken | committedOperations |  errorMessages | batch                                           | operations
-| 3       | 59    | 2         | 59                   | {}            | {total: 3, committed: 3, failed: 0, errors: {}} | {total: 59, committed: 59, failed: 0, errors: {}}
-|===
---
-
-.GCP
---
-[source,cypher]
-----
-CALL apoc.periodic.iterate(
-  "MATCH (a:Article) WHERE not(exists(a.processed)) RETURN a",
-  "CALL apoc.nlp.gcp.entities.stream([item in $_batch | item.a], {
-          key: $gcpApiKey,
-          nodeProperty: 'body'
-   })
-   YIELD node AS a, value
-   SET a.processed = true
-   WITH a, value
-   UNWIND value.entities AS entity
-   WITH a, entity
-   WHERE entity.type IN ['PERSON', 'LOCATION', 'ORGANIZATION', 'EVENT']
-
-   CALL apoc.merge.node(
-     ['Entity', apoc.text.capitalize(toLower(entity.type))],
-     {name: entity.name}, {}, {}
-   )
-   YIELD node AS e
-   MERGE (a)-[entityRel:GCP_ENTITY]->(e)
-   SET entityRel.score = entity.score
-   RETURN count(*)",
-  { batchMode: "BATCH_SINGLE",
-    batchSize: 25,
-    params: {gcpApiKey: $gcpApiKey}}
-)
-YIELD batches, total, timeTaken, committedOperations, errorMessages, batch, operations
-RETURN batches, total, timeTaken, committedOperations, errorMessages, batch, operations;
-----
-
-.Results
-[opts="header"]
-|===
-| batches | total | timeTaken | committedOperations |  errorMessages | batch                                           | operations
-| 3       | 59    | 46        | 59                  | {}            | {total: 3, committed: 3, failed: 0, errors: {}} | {total: 59, committed: 59, failed: 0, errors: {}}
-|===
---
-
-.Azure
---
-[source,cypher]
-----
-CALL apoc.periodic.iterate(
-  "MATCH (a:Article) WHERE not(exists(a.processed)) RETURN a",
-  "CALL apoc.nlp.azure.entities.stream([item in $_batch | item.a], {
-          key: $azureApiKey,
-          url: $azureApiUrl,
-          nodeProperty: 'body'
-   })
-   YIELD node AS a, value
-   SET a.processed = true
-   WITH a, value
-   UNWIND value.entities AS entity
-   WITH a, entity
-   WHERE entity.type IN ['Person', 'Organization', 'Location', 'Event']
-
-   CALL apoc.merge.node(
-     ['Entity', apoc.text.capitalize(toLower(entity.type))],
-     {name: entity.name}, {}, {}
-   )
-   YIELD node AS e
-   MERGE (a)-[entityRel:AZURE_ENTITY]->(e)
-   SET entityRel.score = entity.score
-   RETURN count(*)",
-  { batchMode: "BATCH_SINGLE",
-    batchSize: 25,
-    params: {azureApiUrl: $azureApiUrl, azureApiKey: $azureApiKey}}
-)
-YIELD batches, total, timeTaken, committedOperations, errorMessages, batch, operations
-RETURN batches, total, timeTaken, committedOperations, errorMessages, batch, operations;
-----
-
-.Results
-[opts="header"]
-|===
-| batches | total | timeTaken | committedOperations |  errorMessages | batch                                           | operations
-| 3       | 59    | 3         | 59                  | {}            | {total: 3, committed: 3, failed: 0, errors: {}} | {total: 59, committed: 59, failed: 0, errors: {}}
-|===
---
-
-[#querying-entity-graph]
-== Querying the entity graph
-
-Now that we've got the entities, it's time to querying the entity graph.
-Let's start by returning the entities for each article, as shown in the following query:
-
-[.tabs]
-.AWS
---
-[source,cypher]
-----
-MATCH (a:Article)
-RETURN a.title AS title, [(a)-[:AWS_ENTITY]->(entity) | entity.name] AS entities
-LIMIT 5;
-----
-
-.Results
-[opts="header"]
-|===
-| title                                                                                        | entities
-| "Manchester United's Edinson Cavani apologises for 'racist' Instagram post"                  | ["Cavani", "FA", "Edinson Cavani", "Southampton", "Manchester United", "Football Association"]
-| "The need for concussion substitutes – Football Weekly"                                      | ["Faye Carruthers", "Soundcloud", "Mixcloud", "Arsenal", "Barry Glendenning", "Facebook", "Ewan Murray", "Raúl Jiménez", "Podcasts", "Wolves", "David Luiz", "Twitter", "Acast", "Stitcher", "Southampton", "Audioboom", "Manchester United", "Max Rushden", "Lars Sivertsen"]
-| "Lennon the fall guy for Celtic's failure to build on domestic dominance"                    | ["Rangers", "Leicester City", "Brendan Rodgers", "County", "Celtic", "Tony Mowbray", "Ross", "Gordon Strachan", "Neil Lennon", "Martin O’Neill", "Easy Street", "League Cup", "Ronny Deila"]
-| "Napoli find truest tribute to Maradona by mirroring his magic against Roma \| Nicky Bandini" | ["Stadio San Paolo", "Lorenzo Insigne", "European", "Napoli", "Roma", "Lionel Messi", "Naples", "Diego Maradona", "Curva"]
-| "Concussion substitutes trial could begin in Premier League early next year"                 | ["David Luiz    Trials", "David Luiz", "Raúl Jiménez", "Mexico", "Guardian", "Arsenal", "Wolves", "Premier League", "Daniela"]
-|===
-
---
-
-.GCP
---
-[source,cypher]
-----
-MATCH (a:Article)
-RETURN a.title AS title, [(a)-[:GCP_ENTITY]->(entity) | entity.name] AS entities
-LIMIT 5;
-----
-
-.Results
-[opts="header"]
-|===
-| title                                                                                        | entities
-| "Manchester United's Edinson Cavani apologises for 'racist' Instagram post"                  | ["Southampton", "greeting", "club", "Striker", "incident", "Manchester United", "win", "body", "Uruguayan", "FA", "Edinson Cavani", "friend", "Football Association"]
-| "The need for concussion substitutes – Football Weekly"                                      | ["Wolves", "Acast", "Barry Glendenning", "Apple", "Ewan Murray", "victory", "Arsenal", "Soundcloud", "Faye Carruthers", "Max Rushden", "Raúl Jiménez", "Audioboom", "Lars Sivertsen", "David Luiz", "Manchester United"]
-| "Lennon the fall guy for Celtic's failure to build on domestic dominance"                    | ["Brendan Rodgers", "club", "season", "bar", "Easy Street", "Rangers", "Leicester", "Gordon Strachan", "Ronny Deila", "race", "League Cup", "defeat", "City manager", "fans", "Martin O'Neill's", "Celtic", "Tony Mowbray", "pond", "Neil Lennon", "Ross County"]
-| "Napoli find truest tribute to Maradona by mirroring his magic against Roma \| Nicky Bandini" | ["fans", "Lorenzo Insigne", "Stadio San Paolo", "Naples", "Napoli", "family", "Diego Maradona", "Roma", "win", "death", "Lionel Messi", "European", "player"]
-| "Concussion substitutes trial could begin in Premier League early next year"                 | ["club", "clash", "Daniela", "Raúl Jiménez", "operation", "Premier League", "David Luiz", "teams", "Arsenal", "recovery", "Guardian", "striker", "Mexico", "Wolves", "Rule change", "David Luiz Trials"]
-|===
-
---
-
-.Azure
---
-[source,cypher]
-----
-MATCH (a:Article)
-RETURN a.title AS title, [(a)-[:AZURE_ENTITY]->(entity) | entity.name] AS entities
-LIMIT 5;
-----
-
-.Results
-[opts="header"]
-|===
-| title                                                                                        | entities
-| "Manchester United's Edinson Cavani apologises for 'racist' Instagram post"                  | ["Striker", "Edinson Cavani", "Manchester United F.C.", "Uruguay national football team", "The Football Association", "Southampton F.C."]
-| "The need for concussion substitutes – Football Weekly"                                      | ["Arsenal F.C.", "AudioBoom", "Stitcher Radio", "Max Rushden", "Lars Sivertsen", "Manchester United F.C.", "SoundCloud", "Raúl Jiménez", "Acast", "Facebook", "Mixcloud", "Barry Glendenning", "Southampton   Rate", "Ewan Murray", "Twitter", "Southampton F.C.", "David Luiz", "Apple Podcasts", "Faye Carruthers"]
-| "Lennon the fall guy for Celtic's failure to build on domestic dominance"                    | ["Martin O'Neill", "Leicester City F.C.", "Tony Mowbray", "Ronny Deila", "Rangers F.C.", "Gordon Strachan", "League Cup", "Neil Lennon", "Ross County F.C.", "EFL Cup", "Celtic F.C.", "Brendan Rodgers"]
-| "Napoli find truest tribute to Maradona by mirroring his magic against Roma \| Nicky Bandini" | ["Naples", "Lorenzo Insigne", "Diego Maradona", "Rome", "Europe", "Lionel Messi", "Lionel Messi", "Stadio San Paolo", "S.S.C. Napoli", "Stadio San Paolo"]
-| "Concussion substitutes trial could begin in Premier League early next year"                 | ["Premier League", "Mexico", "Daniela", "Guardian", "Raúl Jiménez", "Wolverhampton Wanderers F.C.", "David Luiz", "Arsenal F.C.", "The Guardian"]
-|===
-
---
-
-We can also use the entities that pairs of articles have in common to determine article similarity.
-If we want to find the similar articles to Gary Lineker's video about Maradona, we could write the following query:
-
-
-[.tabs]
-
-.AWS
---
-[source,cypher]
-----
-MATCH (a1:Article {title: "Gary Lineker: Maradona was 'like a messiah' in Argentina – video"})
-MATCH (a1:Article)-[:AWS_ENTITY]-(entity)<-[:AWS_ENTITY]-(a2:Article)
-RETURN a2.title AS otherArticle, collect(entity.name) AS entities
-ORDER BY size(entities) DESC
-LIMIT 5;
-----
-
-.Results
-[opts="header"]
-|===
-| otherArticle  | entities
-| "Remembering Diego Maradona: football legend dies aged 60 – video obituary"                 | ["World Cup", "Diego Maradona", "Buenos Aires", "Napoli", "Maradona", "Argentina", "Barcelona"]
-| "Classic YouTube \| Diego Armando Maradona, nerveless kicking and Football Manager kids"     | ["England", "World Cup", "Diego Maradona", "Argentina", "Maradona", "Lineker", "Gary Lineker"]
-| "Fans in Argentina and Naples mourn death of Diego Maradona – video"                        | ["World Cup", "Diego Maradona", "Buenos Aires", "Napoli", "Argentina", "Maradona"]
-| "Burdened by genius: Maradona reminds us how peaking young brings its problems \| Vic Marks" | ["World Cup", "Diego Maradona", "Mexico", "Maradona", "Argentina"]
-| "A tribute to Diego Maradona – Football Weekly"                                             | ["World Cup", "Diego Maradona", "Buenos Aires", "Mexico", "Twitter"]
-|===
-
---
-
-.GCP
---
-[source,cypher]
-----
-MATCH (a1:Article {title: "Gary Lineker: Maradona was 'like a messiah' in Argentina – video"})
-MATCH (a1:Article)-[:GCP_ENTITY]-(entity)<-[:GCP_ENTITY]-(a2:Article)
-RETURN a2.title AS otherArticle, collect(entity.name) AS entities
-ORDER BY size(entities) DESC
-LIMIT 5;
-----
-
-.Results
-[opts="header"]
-|===
-| otherArticle                                                                              | entities
-| "Remembering Diego Maradona: football legend dies aged 60 – video obituary"               | ["Barcelona", "Argentina", "Napoli", "Buenos Aires"]
-| "Classic YouTube \| Diego Armando Maradona, nerveless kicking and Football Manager kids"   | ["Argentina", "England", "Gary Lineker"]
-| "Diego Maradona's personal doctor denies responsibility for death"                        | ["home", "footballer", "Buenos Aires"]
-| "Ageless Zlatan Ibrahimovic continues to take care of business for Milan \| Nicky Bandini" | ["home", "Napoli"]
-| "'He took us all to heaven': football fans react to Diego Maradona's death"               | ["Argentina", "Buenos Aires"]
-|===
-
---
-
-.Azure
---
-[source,cypher]
-----
-MATCH (a1:Article {title: "Gary Lineker: Maradona was 'like a messiah' in Argentina – video"})
-MATCH (a1:Article)-[:AZURE_ENTITY]-(entity)<-[:AZURE_ENTITY]-(a2:Article)
-RETURN a2.title AS otherArticle, collect(entity.name) AS entities
-ORDER BY size(entities) DESC
-LIMIT 5;
-----
-
-.Results
-[opts="header"]
-|===
-| otherArticle                                                                              | entities
-| "Remembering Diego Maradona: football legend dies aged 60 – video obituary"             | ["FC Barcelona", "Maradona", "S.S.C. Napoli", "Napoli", "Argentina", "Buenos Aires", "Diego Maradona"]
-| "Classic YouTube \| Diego Armando Maradona, nerveless kicking and Football Manager kids" | ["Gary Lineker", "Argentina national football team", "Sheffield Wednesday F.C.", "England", "England national football team", "Argentina", "Diego Maradona"]
-| "A tribute to Diego Maradona – Football Weekly"                                         | ["Sheffield Wednesday F.C.", "Maradona", "Mexico", "Twitter", "Buenos Aires", "Diego Maradona"]
-| "Fans in Argentina and Naples mourn death of Diego Maradona – video"                    | ["Maradona", "S.S.C. Napoli", "Argentina", "Buenos Aires", "Diego Maradona"]
-| "It was Maradona's defiance that most inspired me \| Kenan Malik"                        | ["Argentina national football team", "England", "England national football team", "Diego Maradona"]
-|===
-
---
-
-Some of the entities that these articles have in common don't really make sense (e.g. "home" or "Twitter"), but in general the articles would be good candidates to go in a 'recommended articles' section for the Gary Lineker article.
-
-If we wanted to filter the extracted entities further, we could try including only those entities that have a Wikipedia entry.
-For more details on that approach, see xref:graph-data-science:build-knowledge-graph-nlp-ontologies.adoc[].
diff --git a/modules/graph-data-science/pages/nlp/index.adoc b/modules/graph-data-science/pages/nlp/index.adoc
deleted file mode 100644
index 479ba3b9..00000000
--- a/modules/graph-data-science/pages/nlp/index.adoc
+++ /dev/null
@@ -1,82 +0,0 @@
-= Natural Language Processing (NLP)
-:level: Intermediate
-:page-level: Intermediate
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-platform, nlp, tools, similarity, apoc, graphaware, analytics, search, recommendations, labs
-:description: This guide explains the available tools for doing Natural Language Processing (NLP) with Neo4j.
-:page-aliases: ROOT:nlp.adoc
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/introduction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-Neo4j offers powerful querying capabilities for structured data, but a lot of the world's data exists in text documents.
-NLP techniques can help to extract the latent structure in these documents.
-This structure could be as simple as nodes representing tokens in a sentence or as complicated as nodes representing entities extracted using a named entity recognition algorithm.
-
-[#why-nlp]
-== Why NLP?
-
-Extracting structure from text documents and storing it in a graph enables several different use cases, including:
-
-* Content based recommendations
-* Natural Language search
-* Document similarity
-
-[#nlp-tools]
-== Neo4j NLP Tools and Products
-
-There are several approaches for doing NLP analysis in Neo4j.
-We'll learn about them in this section.
-
-=== APOC NLP
-
-https://neo4j.com/labs/apoc/4.2/[APOC^] is Neo4j's standard library.
-It contains procedures that call the Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure Natural Language APIs, and create a graph based on the results returned.
-
-These procedures support entity extraction, key phrase extraction, sentiment analysis, and document classification.
-
-This library is a good choice for your first graph based NLP project.
-
-.Related Articles
-* https://markhneedham.com/blog/2020/05/05/quick-graph-building-entity-graph-twin4j-apoc-nlp/[QuickGraph #7: An entity graph of TWIN4j using APOC NLP^]
-* https://towardsdatascience.com/nlp-and-graphs-go-hand-in-hand-with-neo4j-and-apoc-e57f59f46845[NLP goes hand in hand with graphs^]
-
-https://neo4j.com/labs/apoc/4.2/nlp/[Learn more about APOC NLP, role="button feature-box_button"]
-
-=== GraphAware Hume
-
-Hume is a graph-powered Insights Engine made by GraphAware, a Neo4j partner.
-It can be used to build a knowledge graph that will help surface previously buried and undetected relevance in your organization.
-
-[NOTE]
-====
-Hume is a commercial product. You'll need to get in contact with GraphAware to learn more and get a demo.
-====
-
-.Related Articles
-* https://graphaware.com/hume,graphaware,covid19/2020/04/01/corona-virus-contact-tracing-smart-quarantine-hume.html[Contact Tracing Using GraphAware Hume (COVID-19)^]
-* https://graphaware.com/nlp/2020/04/15/hume-in-space.html[Hume in Space: Monitoring Satellite Technology Markets with a ML-powered Knowledge Graph^]
-* https://graphaware.com/nlp/2020/10/20/ere-jane-austen.html[Knowledge Graphs with Entity Relations: Is Jane Austen employed by Google?^]
-* https://graphaware.com/hume/2020/11/30/insightful-it-operations-with-hume.html[Insightful IT Operations with Hume^]
-
-https://graphaware.com/products/hume/[Learn more about GraphAware Hume, role="button feature-box_button"]
-
-=== Other approaches
-
-Other approaches to NLP analysis, using Python libraries and Cypher, are described in the following articles:
-
-* https://www.slideshare.net/lyonwj/natural-language-processing-with-graph-databases-and-neo4j[Natural Language Processing with Graph Databases and Neo4j^]
-* https://www.lyonwj.com/2015/06/16/nlp-with-neo4j/[Natural Language Processing With Neo4j - Mining Paradigmatic Word Associations^]
-* https://medium.com/neo4j/using-nlp-in-neo4j-ac40bc92196f[Neo4j: Natural Language Processing (NLP) in Cypher^]
-* https://www.youtube.com/watch?v=mTCqQ2e08Q8[Neo4j Online Meetup #15: Natural Language Understanding with Python and Neo4j^]
-
-
-[#developer-guides]
-== Developer Guides
-
-The following guides show how to solve various NLP related problems:
-
-* xref:graph-data-science:build-knowledge-graph-nlp-ontologies.adoc[Build a Knowledge Graph with NLP and Ontologies]
-* xref:graph-data-science:nlp/entity-extraction.adoc[Entity Extraction with APOC NLP]
diff --git a/modules/graph-data-science/pages/node-classification.adoc b/modules/graph-data-science/pages/node-classification.adoc
deleted file mode 100644
index e34ff8ac..00000000
--- a/modules/graph-data-science/pages/node-classification.adoc
+++ /dev/null
@@ -1,543 +0,0 @@
-= Node Classification with GDSL
-:level: Intermediate
-:page-level: Intermediate
-:author: Clair Sullivan
-:category: graph-data-science
-:tags: graph-data-science, graph-algorithms, machine-learning
-:description: This guide covers machine learning-based node classification using the Neo4j Data Science Library.
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/introduction/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will learn how to perform node classification.
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.2.3 or later) and link:/download-center/#algorithms[Graph Data Science Library^] (version 1.5 or later) downloaded and installed to use graph embeddings.  You should also have https://github.com/neo4j-contrib/neo4j-apoc-procedures[APOC^] (version 4.2.0.1 or later).
-
-[role=expertise {level}]
-{level}
-
-[#node-classification]
-== What is node classification?
-
-Node classification is a supervised machine learning (ML) approach whereby existing nodes with known classes can be used to train a model that will learn the classes for nodes where they are unknown. In order to achieve this, the data must be split into two parts -- a training graph and a testing graph -- prior to predicting the classes for the unknown nodes.  The training process involves splitting the training graph into two parts -- a training and validation set -- that will be used to fine tune the model through subsequent steps.  
-
-Node classification is based on https://en.wikipedia.org/wiki/Logistic_regression[logistic regression].  As in any ML model, care must be taken in choosing both the training and test sets as well as how to ensure that the model is not overfitting the data.  In the case of the GDS Node Classification algorithm, an L2 norm is used as a penalty.
-
-[NOTE]
-====
-The code examples used in this guide can be found in https://github.com/AliciaFrame/ML_with_GDS[this GitHub repository^]. 
-==== 
-
-[#marvel-dataset]
-== Marvel Universe dataset
-
-In this example we will be using a dataset from the comics and movies associated with the Marvel Universe.  This dataset can be found https://gist.github.com/tomasonjo/fbc6d617c3f6476a3a825b5dd22fd29a[here^].  It contains 40,616 characters and 65,870 relationships connecting them.  Additionally, the characters have numerous properties that can be associated with each node.  We will be using this dataset to try and predict, off of a series of characters for training purposes, which characters are X-Men and which are not.
-
-=== Data preparation
-
-We will begin by loading in the data to the database from a series of CSV files available online.  This can be done with the following query:
-
-[source, cypher]
-----
-CALL apoc.schema.assert({Character:['name']},{Comic:['id'], Character:['id'], Event:['id'], Group:['id']});
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/heroes.csv" as row
-CREATE (c:Character)
-SET c += row;
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/groups.csv" as row
-CREATE (c:Group)
-SET c += row;
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/events.csv" as row
-CREATE (c:Event)
-SET c += row;
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/comics.csv" as row
-CREATE (c:Comic)
-SET c += apoc.map.clean(row,[],["null"]);
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/heroToComics.csv" as row
-MATCH (c:Character{id:row.hero})
-MATCH (co:Comic{id:row.comic})
-MERGE (c)-[:APPEARED_IN]->(co);
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/heroToEvent.csv" as row
-MATCH (c:Character{id:row.hero})
-MATCH (e:Event{id:row.event})
-MERGE (c)-[:PART_OF_EVENT]->(e);
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/heroToGroup.csv" as row
-MATCH (c:Character{id:row.hero})
-MATCH (g:Group{id:row.group})
-MERGE (c)-[:PART_OF_GROUP]->(g);
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/heroToHero.csv" as row
-MATCH (s:Character{id:row.source})
-MATCH (t:Character{id:row.target})
-CALL apoc.create.relationship(s,row.type, {}, t) YIELD rel
-RETURN distinct 'done';
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/heroStats.csv" as row
-MATCH (s:Character{id:row.hero})
-CREATE (s)-[:HAS_STATS]->(stats:Stats)
-SET stats += apoc.map.clean(row,['hero'],[]);
-
-LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/Marvel/heroFlight.csv" as row
-MATCH (s:Character{id:row.hero})
-SET s.flight = row.flight;
-
-MATCH (s:Stats)
-WITH keys(s) as keys LIMIT 1
-MATCH (s:Stats)
-UNWIND keys as key
-CALL apoc.create.setProperty(s, key, toInteger(s[key]))
-YIELD node
-RETURN distinct 'done';
-----
-
-This query creates a series of nodes and their labels and properties: `Comic`, `Character`, `Stats` along with a variety of edges such as which comics the characters appeared in which comics, who is an enemy of whom, etc.  You can see a schema of this graph using `CALL db.schema.visualization()` here:
-
-.Marvel Universe Schema
-image::marvel-schema.png[800,600]
-
-(Note that for the simplicity of the image we are not showing the node properties since there are many.)
-
-We next bring in the character traits from the stats to be node properties:
-
-[source, cypher]
-----
-MATCH (c:Character)-[:HAS_STATS]->(s)
-WITH c, s.strength as strength, s.fighting_skills as fighting_skills, s.durability as durability, s.speed as speed, s.intelligence as intelligence, s.energy as energy
-SET c.strength=strength,
-    c.fighting_skills=fighting_skills,
-    c.durability=durability,
-    c.speed=speed,
-    c.intelligence=intelligence,
-    c.energy=energy
-RETURN count(c)
-----
-
-These node properties will eventually be used to build the node classification model via both embeddings as well as the tabular approach.
-
-Next, we set up the co-occurance of characters such that we can identify which characters appear with which other characters and how often (which will be used to identify the edge weighting).  This is done via:
-
-[source, cypher]
-----
-MATCH (c1:Character)-[:APPEARED_IN]->(c:Comic)<-[:APPEARED_IN]-(c2:Character) 
-WITH c1, c2, count(c) as weight
-MERGE (c1)-[:APPEARED_WITH{times:weight}]->(c2)
-MERGE (c2)-[:APPEARED_WITH{times:weight}]->(c1)
-----
-
-=== Feature engineering
-
-Once our data is loaded in, it is time to start the process of engineering the features that will be used to populate our mode.  For example, we might consider a variety of https://neo4j.com/docs/graph-data-science/current/algorithms/centrality/[centrality measures] of the character to be a feature that would be useful to train with.  To obtain this, we first create an in-memory graph as:
-
-[source, cypher]
-----
-CALL gds.graph.create(
-  'marvel-character-graph',
-  {
-    Person: {
-      label: 'Character',
-      properties: { 
-      strength:{property:'strength',defaultValue:0},
-      fighting_skills:{property:'fighting_skills', defaultValue:0},
-      durability:{property:'durability', defaultValue:0},
-      speed:{property:'speed', defaultValue:0},
-      intelligence:{property:'intelligence', defaultValue:0}
-      }
-    }
-  }, {
-    APPEARS_WITH_UNDIRECTED: {
-      type: 'APPEARED_WITH',
-      orientation: 'UNDIRECTED',
-      aggregation: 'SINGLE',
-      properties: ['times']
-    },
-    APPEARS_WITH_DIRECTED: {
-      type: 'APPEARED_WITH',
-      orientation: 'NATURAL',
-      properties: ['times'],
-      aggregation: 'SINGLE'
-    },
-    ALLY_UNDIRECTED: {
-      type: 'ALLY',
-      orientation: 'UNDIRECTED',
-      aggregation: 'SINGLE'
-    },
-    ALLY_DIRECTED: {
-      type: 'ALLY',
-      orientation: 'NATURAL',
-      aggregation: 'SINGLE'
-    },    
-    ENEMY_UNDIRECTED: {
-      type: 'ENEMY',
-      orientation: 'UNDIRECTED',
-      aggregation: 'SINGLE'
-    },
-    ENEMY_DIRECTED: {
-      type: 'ENEMY',
-      orientation: 'NATURAL',
-      aggregation: 'SINGLE'
-    }
-   
-});
-----
-
-and then we use this graph to calculate the https://neo4j.com/docs/graph-data-science/current/algorithms/page-rank/[PageRank^], https://neo4j.com/docs/graph-data-science/current/algorithms/betweenness-centrality/[Betweenness Centrality^], and https://neo4j.com/docs/graph-data-science/current/algorithms/hits/[Hyperlink-Induced Topic Search^] (HITS) of each node and write those values back to the database:
-
-[source, cypher]
-----
-// pageRank
-CALL gds.pageRank.write('marvel-character-graph',{
-     relationshipTypes: ['APPEARS_WITH_DIRECTED'],
-     writeProperty: 'appeared_with_pageRank'
-});
-CALL gds.pageRank.write('marvel-character-graph',{
-     relationshipTypes: ['ALLY_DIRECTED'],
-     writeProperty: 'ally_pageRank'
-});
-CALL gds.pageRank.write('marvel-character-graph',{
-     relationshipTypes: ['ENEMY_DIRECTED'],
-     writeProperty: 'enemy_pageRank'
-});
-
-// betweenness
-CALL gds.betweenness.write('marvel-character-graph',{
-     relationshipTypes: ['APPEARS_WITH_UNDIRECTED'],
-     writeProperty: 'appeared_with_betweenness'
-});
-CALL gds.betweenness.write('marvel-character-graph',{
-     relationshipTypes: ['ALLY_UNDIRECTED'],
-     writeProperty: 'ally_betweenness'
-});
-CALL gds.betweenness.write('marvel-character-graph',{
-     relationshipTypes: ['ENEMY_UNDIRECTED'],
-     writeProperty: 'enemy_betweenness'
-});
-
-//HITS
-CALL gds.alpha.hits.write('marvel-character-graph',{
-     relationshipTypes: ['APPEARS_WITH_DIRECTED'],
-     hitsIterations: 50,
-     authProperty: 'appeared_with_auth',
-     hubProperty: 'appeared_with_hub'
-});
-CALL gds.alpha.hits.write('marvel-character-graph',{
-     relationshipTypes: ['ALLY_DIRECTED'],
-     hitsIterations: 50,
-     authProperty: 'appeared_with_auth',
-     hubProperty: 'appeared_with_hub'
-});
-CALL gds.alpha.hits.write('marvel-character-graph',{
-     relationshipTypes: ['ENEMY_DIRECTED'],
-     hitsIterations: 50,
-     authProperty: 'appeared_with_auth',
-     hubProperty: 'appeared_with_hub'
-});
-----
-
-We will also want these values added to the in-memory graph for the sake of calculating graph embeddings in the next step, which is achieved through the `.mutate()` command:
-
-[source, cypher]
-----
-// pageRank
-CALL gds.pageRank.mutate('marvel-character-graph',{
-     relationshipTypes: ['APPEARS_WITH_DIRECTED'],
-     mutateProperty: 'appeared_with_pageRank'
-});
-CALL gds.pageRank.mutate('marvel-character-graph',{
-     relationshipTypes: ['ALLY_DIRECTED'],
-     mutateProperty: 'ally_pageRank'
-});
-CALL gds.pageRank.mutate('marvel-character-graph',{
-     relationshipTypes: ['ENEMY_DIRECTED'],
-     mutateProperty: 'enemy_pageRank'
-});
-
-// betweenness
-CALL gds.betweenness.mutate('marvel-character-graph',{
-     relationshipTypes: ['APPEARS_WITH_UNDIRECTED'],
-     mutateProperty: 'appeared_with_betweenness'
-});
-CALL gds.betweenness.mutate('marvel-character-graph',{
-     relationshipTypes: ['ALLY_UNDIRECTED'],
-     mutateProperty: 'ally_betweenness'
-});
-CALL gds.betweenness.mutate('marvel-character-graph',{
-     relationshipTypes: ['ENEMY_UNDIRECTED'],
-     mutateProperty: 'enemy_betweenness'
-});
-
-//HITS
-CALL gds.alpha.hits.mutate('marvel-character-graph',{
-     relationshipTypes: ['APPEARS_WITH_DIRECTED'],
-     hitsIterations: 50,
-     authProperty: 'appeared_with_auth',
-     hubProperty: 'appeared_with_hub'
-});
-CALL gds.alpha.hits.mutate('marvel-character-graph',{
-     relationshipTypes: ['ALLY_DIRECTED'],
-     hitsIterations: 50,
-     authProperty: 'appeared_with_auth',
-     hubProperty: 'appeared_with_hub'
-});
-CALL gds.alpha.hits.mutate('marvel-character-graph',{
-     relationshipTypes: ['ENEMY_DIRECTED'],
-     hitsIterations: 50,
-     authProperty: 'appeared_with_auth',
-     hubProperty: 'appeared_with_hub'
-});
-----
-
-Lastly, we will use the https://neo4j.com/docs/graph-data-science/current/algorithms/fastrp/[Fast Random Projection^] (FastRP) embedding algorithm to create embedding vectors for each node, that will be used in one of our node classifications.  Despite the fact that we will only be looking at a subset of this graph, namely X-Men and those who might be X-men or relate to them somehow, but we will compute the embeddings for the whole graph. 
-
-[source, cypher]
-----
-CALL gds.beta.fastRPExtended.write('marvel-character-graph',{
-    relationshipTypes:['APPEARS_WITH_UNDIRECTED'],
-    featureProperties: ['strength','fighting_skills','durability','speed','intelligence','appeared_with_pageRank','ally_pageRank','enemy_pageRank','appeared_with_betweenness','ally_betweenness','enemy_betweenness','appeared_with_hub','appeared_with_auth'], //14 node features
-    relationshipWeightProperty: 'times',
-    propertyDimension: 45,
-    embeddingDimension: 250,
-    iterationWeights: [0, 0, 1.0, 1.0],
-    normalizationStrength:0.05,
-    writeProperty: 'fastRP_Extended_Embedding'
-})
-----
-
-[NOTE]
-====
-For more information on graph embeddings and how they can be used, see link:/developer/graph-data-science/graph-embeddings[Applied Graph Embeddings^].
-====
-
-Finally, we can drop the `marvel-character-graph` to free up some memory via `CALL gds.graph.drop('marvel-character-graph')`.
-
-== Running the node classification algorithm
-
-Prior to the actual running of the node classification we must set up our training and testing graphs.  There are a few things that we need to consider.  First, we want to have roughly an equal number of X-Men to non-X-Men in our graph to prevent class imbalance.  This means that first we will select all of the X-Men and set the property `is_xman` to identify these individuals:
-
-[source, cypher]
-----
-MATCH (c:Character)-[:PART_OF_GROUP]-> (g:Group{name:'X-Men'})
-SET c.is_xman=1, c:Model_Data;
-----
-
-We see here that `c.is_xman` is set to an integer value of 1, which is required by the node classification algorithm to distinguish between the various classes.
-
-Next, we need to identify characters that are not X-Men.  There are many more non-X-Men characters that appear with the X-Men, so we will downsample these through the requirement to have a degree greater than zero while also using a random number to determine whether that character will be put into the non-X-Men set and set their class to the integer value of 0:
-
-[source, cypher]
-----
-MATCH (c:Character)
-WHERE NOT (c)-[:PART_OF_GROUP]->(:Group) WITH c
-WHERE NOT (c)-[:APPEARED_WITH*2..3]-(:Character{is_xman:1}) 
-AND apoc.node.degree(c)>0 WITH c
-WHERE rand() < 0.2
-SET c:Model_Data, c.is_xman=0;
-----
-
-Finally, we will create a set of character that will be used for predictions after the model is trained:
-
-[source, cypher]
-----
-MATCH (c:Character)
-WHERE NOT (c:Model_Data)
-SET c:Holdout_Data;
-----
-
-Once we have done this, we will create an in-memory graph encompassing these characters, their properties, and the class to be predicted.  
-
-[source, cypher]
-----
-CALL gds.graph.create(
-  'marvel_model_data',
-  {
-    Character: {
-      label: 'Model_Data',
-      properties: { 
-        fastRP_embedding:{property:'fastRP_Extended_Embedding', defaultValue:0},
-        strength:{property:'strength', defaultValue:0},
-        durability:{property:'durability', defaultValue:0},
-        intelligence:{property:'intelligence', defaultValue:0},
-        energy:{property:'energy', defaultValue:0},
-        speed:{property:'speed', defaultValue:0},
-        is_xman:{property:'is_xman', defaultValue:0}
-      }
-    },
-    Holdout_Character: {
-      label: 'Holdout_Data',
-      properties: { 
-        fastRP_embedding:{property:'fastRP_Extended_Embedding', defaultValue:0},
-        strength:{property:'strength', defaultValue:0},
-        durability:{property:'durability', defaultValue:0},
-        intelligence:{property:'intelligence', defaultValue:0},
-        energy:{property:'energy', defaultValue:0},
-        speed:{property:'speed', defaultValue:0},
-        is_xman:{property:'is_xman', defaultValue:0}
-      }
-    }
-  }, {
-    APPEARED_WITH: { 
-      type: 'APPEARED_WITH',
-      orientation: 'UNDIRECTED',
-      properties: ['times'],
-      aggregation: 'SINGLE'
-    }
-});
-----
-
-Observe that we have two character labels that are being put into this in-memory graph, namely `Character` and `Holdout_Character`.  This ensures that we are not mixing up the characters that will be used in the validation after the model is fully trained.
-
-First let's train a simple model that only uses some character properties for the training process:
-
-[source, cypher]
-----
-CALL gds.alpha.ml.nodeClassification.train('marvel_model_data', {
-   nodeLabels: ['Character'],
-   modelName: 'xmen-model-properties',
-   featureProperties: ['energy','speed','strength','durability','intelligence'], 
-   targetProperty: 'is_xman', 
-   metrics: ['F1_WEIGHTED','ACCURACY'], 
-   holdoutFraction: 0.2, 
-   validationFolds: 5, 
-   randomSeed: 2,
-   params: [
-       {penalty: 0.0625, maxIterations: 1000},
-       {penalty: 0.125, maxIterations: 1000}, 
-       {penalty: 0.25, maxIterations: 1000}, 
-       {penalty: 0.5, maxIterations: 1000},
-       {penalty: 1.0, maxIterations: 1000},
-       {penalty: 2.0, maxIterations: 1000}, 
-       {penalty: 4.0, maxIterations: 1000}
-       ]
-    }) YIELD modelInfo
-  RETURN
-  modelInfo.bestParameters AS winningModel,
-  modelInfo.metrics.F1_WEIGHTED.outerTrain AS trainGraphScore,
-  modelInfo.metrics.F1_WEIGHTED.test AS testGraphScore
-----
-
-In this statement, we are training a model based on the node properties of `energy`, `speed`, `strength`, `durability`, and `intelligence`.  The `targetProperty` is the thing we are trying to solve for;  in this case we are trying to determine the node property `is_xman` (1 for an X-Man, 0 for everyone else).  The model will be able to return the weighted F1 score and the accuracy, but it is important to note that only the first metric is used for the actual training.  We see that the validation set represents 20% of the test graph with 5-fold cross-validation.  Finally, we set a series of parameters that are used to evaluate the model.  In this case, we have provided a series of penalties using the L2 norm with a given number of training iterations.  The training algorithm will identify the optimal model given these parameters, which is returned in the final portion of the query along with the training and test F1 weighted scores.
-
-When this is run on our dataset, we obtain the following results:
-
-[cols=3*,options=header]
-|===
-|"winningModel"
-|"trainingGraphScore"
-|"testGraphScore"
-
-|{"maxIterations":1000,"penalty":0.0625}
-|0.4696285981767071
-|0.44504748658690024
-|===
-
-These scores are low, but this is not surprising.  We provided a very minimal number of properties on which to train the model, a problem that is compounded by the fact the the graph itself is quite small.  So instead, let's train a new model using the FastRP embeddings.   
-
-[source, cypher]
-----
-CALL gds.alpha.ml.nodeClassification.train('marvel_model_data', {
-   nodeLabels: ['Character'],
-   modelName: 'xmen-model-fastRP',
-   featureProperties: ['fastRP_embedding'], 
-   targetProperty: 'is_xman', 
-   metrics: ['F1_WEIGHTED','ACCURACY'], 
-   holdoutFraction: 0.2, 
-   validationFolds: 5, 
-   randomSeed: 2,
-   params: [
-       {penalty: 0.0625, maxIterations: 1000},
-       {penalty: 0.125, maxIterations: 1000}, 
-       {penalty: 0.25, maxIterations: 1000}, 
-       {penalty: 0.5, maxIterations: 1000},
-       {penalty: 1.0, maxIterations: 1000},
-       {penalty: 2.0, maxIterations: 1000}, 
-       {penalty: 4.0, maxIterations: 1000}
-       ]
-    }) YIELD modelInfo
-  RETURN
-  modelInfo.bestParameters AS winningModel,
-  modelInfo.metrics.F1_WEIGHTED.outerTrain AS trainGraphScore,
-  modelInfo.metrics.F1_WEIGHTED.test AS testGraphScore
-----
-
-[NOTE]
-====
-Don't forget that if you are not using the https://neo4j.com/docs/graph-data-science/current/installation/#installation-enterprise-edition[Enterprise Edition] of the Graph Data Science library, you can only have a limited number of models in memory at any given time, so you must drop unused models via `CALL gds.beta.model.drop('marvel_model_data')`.
-====
-
-This is identical to our procedure before, however, we can see that we have replaced the `featureProperties` to be the FastRP embeddings.  We would expect this model to perform better since the embedding process returns a vector embedding for each node that, in our case, is 250 elements long.  In fact, we obtain the following results with the embeddings:
-
-[cols=3*,options=header]
-|===
-|"winningModel"
-|"trainingGraphScore"
-|"testGraphScore"
-
-|{"maxIterations":1000,"penalty":0.0625}
-|0.9623978269133772
-|0.857142851020408
-|===
-
-== Predictions with the model
-
-Using the FastRP model, let's inspect some predicted nodes.  To do this, we first have to run the prediction algorithm, which we will then write to the nodes themselves:
-
-[source, cypher]
-----
-CALL gds.alpha.ml.nodeClassification.predict.mutate('marvel_model_data', {
-  nodeLabels: ['Holdout_Character'], //filter out the character nodes
-  modelName: 'xmen-model-fastRP',
-  mutateProperty: 'predicted_xman',
-  predictedProbabilityProperty: 'predicted_xman_probability'
-});
-
-CALL gds.graph.writeNodeProperties(
-  'marvel_model_data',
-  ['predicted_xman', 'predicted_xman_probability'],
-  ['Holdout_Character']
-);
-----
-
-We now look at some of the predictions for characters that are labeled as X-Men.  To do this, we run the following query:
-
-[source, cypher]
-----
-MATCH (c:Character) 
-WHERE c.predicted_xman = 1 AND NOT c:Model_Data
-RETURN c.name, c.aliases, c.predicted_xman, c.predicted_xman_probability 
-----
-
-`c.predicted_xman` returns the predicted class (in this case we are looking for characters that were labeled as X-Men by the model).  The returned `c.predicted_xman-probability` gives the probability of each class, presented as a list where the first element is the probability of class 0 (not an X-Man) and the second element is the probability of class 1 (an X-Man).  Our results will be as follows for the first returned character (with long alias list truncated for space):
-
-
-[cols=4*,options=header]
-|===
-|"c.name"
-|"c.aliases"
-|"c.predicted_xman"
-|"c.predicted_xman_probability"
-
-|"Steve Rogers"
-|"Steven Rogers, Brett Hendrick, Buck Jones, etc."
-|1
-|[0.12884971201784812,0.8711502879821517]
-|===
-
-Examining Steve Rogers further, he is not actually an X-Man.  However, in the graph we can see that he has many `:APPEARED_WITH` relationships with actual X-Men, which can be seen via:
-
-[source, cypher]
-----
-MATCH (c:Character {name: 'Steve Rogers'})-[e]->(x:Character)-[:PART_OF_GROUP]->(g:Group {name: 'X-Men'})
-RETURN c.name, e, x.name
-----
-
-If we were to explore other characters returned in this list, we would see that they also have several relationships with true X-Men.  We also will note that there are actual X-Men who were not linked in the original data with the X-Men group that are really X-Men (for example: Beast, Cyclops, and Charles Xavier).
diff --git a/modules/graph-data-science/pages/path-finding-graph-algorithms.adoc b/modules/graph-data-science/pages/path-finding-graph-algorithms.adoc
deleted file mode 100644
index b4071db2..00000000
--- a/modules/graph-data-science/pages/path-finding-graph-algorithms.adoc
+++ /dev/null
@@ -1,141 +0,0 @@
-= Path Finding Algorithms
-:level: Beginner
-:page-level: Beginner
-:author: Mark Needham
-:category: graph-data-science
-:tags: graph-data-science, graph-algorithms, graph-search, path-finding, shortest-path
-:description: This guide covers path finding algorithms in the Neo4j Data Science Library, like Shortest Path, A*, All-Pairs Shortest Path, and more.
-:page-aliases: ROOT:path-finding-graph-algorithms.adoc
-:page-deprecated-title: the Neo4j Graph Data Science Library
-:page-deprecated-redirect: https://neo4j.com/docs/graph-data-science/current/algorithms/pathfinding/
-
-// This page has been deprecated in favour of the Neo4j Graph Data Science Library, maintained by the Neo4j Documentation team. This page will be removed and redirected in the future.
-
-.Goals
-[abstract]
-In this guide, we will learn about path finding graph algorithms.
-
-.Prerequisites
-[abstract]
-Please have link:/download[Neo4j^] (version 4.0 or later) and the link:/download-center/#algorithms[Graph Data Science Library^] downloaded and installed to use path finding algorithms.
-
-[role=expertise {level}]
-{level}
-
-[#overview-path-finding-algorithms]
-== What are path finding algorithms?
-
-image:{img}Pathfinding-Algo-Icon.png[float="right", width="200px"]
-
-Path finding algorithms build on top of link:/developer/graph-search-algorithms[graph search algorithms] and explore routes between nodes, starting at one node and traversing through relationships until the destination has been reached.
-These algorithms find the cheapest path in terms of the number of hops or weight.
-Weights can be anything measured, such as time, distance, capacity, or cost.
-
-++++
-
-++++
-
-The Neo4j https://neo4j.com/graph-data-science-library[Graph Data Science Library^] supports many different link:/docs/graph-data-science/current/algorithms/pathfinding/[path finding algorithms^].
-
-[#shortest-path]
-== Shortest Path
-
-The Shortest Path algorithm calculates the shortest (weighted) path between a pair of nodes.
-Shortest path is considered to be one of the classical graph problems and has been researched as far back as the 19th century.
-It has the following use cases:
-
-* Finding directions between physical locations.
-This is the most common usage, and web mapping tools such as Google Maps use the shortest path algorithm, or a variant of it, to provide driving directions.
-
-* Social networks can use the algorithm to find the degrees of separation between people.
-For example, when you view someone’s profile on LinkedIn, it will indicate how many people separate you in the connections graph, as well as listing your mutual connections.
-
-link:/docs/graph-data-science/current/alpha-algorithms/shortest-path/[Read Shortest Path reference documentation^, role="medium button"]
-
-[#astar]
-== A*
-
-The A* algorithm improves on the Dijkstra shortest path algorithm, by including extra information by way of a heuristic function that determines which paths to explore next.
-This optimization results in shortest paths being found more quickly.
-The A* algorithm can be used to find shortest paths between single pairs of locations, where GPS coordinates are known.
-
-link:/docs/graph-data-science/current/alpha-algorithms/a_star/[Read A* reference documentation^, role="medium button"]
-
-[#yens]
-== Yen's k-Shortest Paths
-
-The Yen's algorithm uses the shortest path algorithm to find the shortest path, 2nd shortest path, 3rd shortest path, and so on up to k-1 deviations of shortest path.
-It has the following use cases:
-
-* Optimizing multiple object tracking by formalizing the motions of targets as flows along the relationships of the spatial graph.
-  Find more in https://cvlab.epfl.ch/files/content/sites/cvlab2/files/publications/publications/2011/BerclazFTF11.pdf[Multiple Object Tracking using K-Shortest Paths Optimization]
-
-* Studying alternative routing on road networks and to recommend top k-paths to the user.
-  Find this study in https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/research/publications/2015/sigspatial_kshortest.pdf[Alternative Routing: k-Shortest Paths with Limited Overlap]
-
-* Part of the https://pdfs.semanticscholar.org/51e7/8aa2041d595b3871a49f4b92be725199e73d.pdf[Finding Diverse High-Quality Plans for Hypothesis Generation] process.
-
-link:/docs/graph-data-science/current/alpha-algorithms/yen-s-k-shortest-path/[Read Yen's k-Shortest Paths reference documentation^, role="medium button"]
-
-[#all-pairs-shortest-path]
-== All-Pairs Shortest Path
-
-The All Pairs Shortest Path calculates the shortest (weighted) path between all pairs of nodes.
-This algorithm has optimizations that make it quicker than calling the Single Source Shortest Path algorithm for every pair of nodes in the graph.
-It has the following use cases:
-
-* Itis used in urban service system problems, such as the location of urban facilities or the distribution or delivery of goods.
-One example of this is determining the traffic load expected on different segments of a transportation grid.
-For more information, see http://web.mit.edu/urban_or_book/www/book/[Urban Operations Research^].
-
-* It is used as part of the REWIRE data center design algorithm that finds a network with maximum bandwidth and minimal latency.
-There are more details about this approach in https://cs.uwaterloo.ca/research/tr/2011/CS-2011-21.pdf["REWIRE: An Optimization-based Framework for Data Center Network Design"^]
-
-link:/docs/graph-data-science/current/alpha-algorithms/all-pairs-shortest-path/[Read All-Pairs Shortest Path reference documentation^, role="medium button"]
-
-[#single-source-shortest-path]
-== Single Source Shortest Path
-
-The Single Source Shortest Path algorithm calculates the shortest (weighted) path from a node to all other nodes in the graph.
-https://en.wikipedia.org/wiki/Open_Shortest_Path_First[Open Shortest Path First^] is a routing protocol for IP networks.
-It uses Single Source Shortest Path to detect changes in topology, such as link failures, and https://routing-bits.com/2009/08/06/ospf-convergence/[come up with a new routing structure in seconds^].
-
-
-link:/docs/graph-data-science/current/alpha-algorithms/single-source-shortest-path/[Read Single Source Shortest Path reference documentation^, role="medium button"]
-
-[#minimum-spanning-tree]
-== Minimum Spanning Tree
-
-The Minimum Weight Spanning Tree starts from a given node, and finds all its reachable nodes and the set of relationships that connect the nodes together with the minimum possible weight.
-It has the following use cases:
-
-* It was applied to analyze airline and sea connections of Papua New Guinea, and minimize the travel cost of exploring the country.
- It could be used to help design low-cost tours that visit many destinations across the country.
- More details can be found in https://www.dwu.ac.pg/en/images/All_Attachements/Research%20Journals/vol_12/2010-V12-1_Fitina_et_al_spanning_trees_for_travel_planning.pdf["An Application of Minimum Spanning Trees to Travel Planning"].
-* Analyzing and visualizing correlations in a network of currencies, based on the correlation between currency returns.
-  This is described in https://www.nbs.sk/_img/Documents/_PUBLIK_NBS_FSR/Biatec/Rok2013/07-2013/05_biatec13-7_resovsky_EN.pdf["Minimum Spanning Tree Application in the Currency Market"].
-* Tracing the history of transmission of infection in an outbreak supported by exhaustive clinical research.
-  For more information, see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC516344/[Use of the Minimum Spanning Tree Model for Molecular Epidemiological Investigation of a Nosocomial Outbreak of Hepatitis C Virus Infection].
-
-
-link:/docs/graph-data-science/current/alpha-algorithms/minimum-weight-spanning-tree/[Read Minimum Spanning Tree reference documentation^, role="medium button"]
-
-[#Random Walk]
-== Random Walk
-
-Random Walk is an algorithm that provides random paths in a graph.
-A random walk means that we start at one node, choose a neighbor to navigate to at random or based on a provided probability distribution, and then do the same from that node, keeping the resulting path in a list.
-It’s similar to how a drunk person traverses a city.
-It has the following use cases:
-
-* It has be shown to relate to Brownian motion and also to the movement and dispersal of animals in the study of https://www.researchgate.net/publication/5425681_Random_walks_in_biology[Random walk models in biology].
-
-* It has been used to analyse ALSI index of the JSE stock exchange and show that the index followed the random walk hypothesis between years 2000 and 2011.
-This means the movement of stock prices was random and the ability of investors to perform relied more on luck than anything else.
-Find this study in https://www.researchgate.net/publication/297750180_The_Random_Walk_Theory_And_Stock_Prices_Evidence_From_Johannesburg_Stock_Exchange[The Random Walk Theory And Stock Prices: Evidence From Johannesburg Stock Exchange]
-
-* It is part of the *node2vec* and *graph2vec* algorithms, that create node embeddings, as well as the  *Walktrap* and *Infomap community detection* algorithms.
-
-* It can be used as part of the training process of machine learning model, as described in David Mack's article https://medium.com/octavian-ai/review-prediction-with-neo4j-and-tensorflow-1cd33996632a[Review prediction with Neo4j and TensorFlow^].
-
-link:/graph-data-science/current/alpha-algorithms/random-walk/[Read Random Walk reference documentation^, role="medium button"]
diff --git a/package-lock.json b/package-lock.json
index 2bd3471e..4524ff72 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -34,12 +34,13 @@
       }
     },
     "node_modules/@antora/cli": {
-      "version": "2.3.3",
-      "resolved": "https://registry.npmjs.org/@antora/cli/-/cli-2.3.3.tgz",
-      "integrity": "sha512-mWxMMWKzTmhmTgAeAVestZf4CRcC4lBD5l521J2fsdGpGx9dQGiwqQZqNcwROJDE+Lg0YfXYYqP1UUKhZwYz6Q==",
+      "version": "2.3.4",
+      "resolved": "https://registry.npmjs.org/@antora/cli/-/cli-2.3.4.tgz",
+      "integrity": "sha512-KItaWFEf/X4LLY2XCidjD00oUp4Ay7y9Hu26+T8dPqV+qnMwOL+MGHhYXsJz+4JaeNJu1Ofwc4onmShpwHQruA==",
+      "license": "MPL-2.0",
       "dependencies": {
-        "@antora/playbook-builder": "2.3.3",
-        "commander": "~5.1"
+        "@antora/playbook-builder": "2.3.4",
+        "commander": "~6.1"
       },
       "bin": {
         "antora": "bin/antora"
@@ -124,9 +125,10 @@
       }
     },
     "node_modules/@antora/playbook-builder": {
-      "version": "2.3.3",
-      "resolved": "https://registry.npmjs.org/@antora/playbook-builder/-/playbook-builder-2.3.3.tgz",
-      "integrity": "sha512-X9S38WgX2diZISNF4QKdF4ZfdND4ySsMlI26Iz4iybxf7xNlUJE1lAk9RM57Ooq543lEUNSnYHpECb1tog2BNw==",
+      "version": "2.3.4",
+      "resolved": "https://registry.npmjs.org/@antora/playbook-builder/-/playbook-builder-2.3.4.tgz",
+      "integrity": "sha512-iPjBndcoZhWqpN608WOkXKUUD94b3JX38igebshiT5/NRINJbeEclpdEX/gPv8D1Z1JcrSVGURZQO1uML76dkg==",
+      "license": "MPL-2.0",
       "dependencies": {
         "@iarna/toml": "~2.2",
         "camelcase-keys": "~6.2",
@@ -171,21 +173,6 @@
         "node": ">=8.11.0"
       }
     },
-    "node_modules/@antora/site-generator-default/node_modules/@antora/playbook-builder": {
-      "version": "2.3.4",
-      "resolved": "https://registry.npmjs.org/@antora/playbook-builder/-/playbook-builder-2.3.4.tgz",
-      "integrity": "sha512-iPjBndcoZhWqpN608WOkXKUUD94b3JX38igebshiT5/NRINJbeEclpdEX/gPv8D1Z1JcrSVGURZQO1uML76dkg==",
-      "dependencies": {
-        "@iarna/toml": "~2.2",
-        "camelcase-keys": "~6.2",
-        "convict": "~6.0",
-        "js-yaml": "~3.14",
-        "json5": "~2.1"
-      },
-      "engines": {
-        "node": ">=8.11.0"
-      }
-    },
     "node_modules/@antora/site-mapper": {
       "version": "2.3.4",
       "resolved": "https://registry.npmjs.org/@antora/site-mapper/-/site-mapper-2.3.4.tgz",
@@ -322,9 +309,10 @@
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     },
     "node_modules/ansi-regex": {
-      "version": "3.0.0",
-      "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-3.0.0.tgz",
-      "integrity": "sha1-7QMXwyIGT3lGbAKWa922Bas32Zg=",
+      "version": "3.0.1",
+      "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-3.0.1.tgz",
+      "integrity": "sha512-+O9Jct8wf++lXxxFc4hc8LsjaSq0HFzzL7cVsw8pRDIPdjKD2mT4ytDZlLuSBZ4cLKZFXIrMGO7DbQCtMJJMKw==",
+      "license": "MIT",
       "engines": {
         "node": ">=4"
       }
@@ -426,6 +414,16 @@
         "node": ">=0.10.0"
       }
     },
+    "node_modules/async": {
+      "version": "2.6.4",
+      "resolved": "https://registry.npmjs.org/async/-/async-2.6.4.tgz",
+      "integrity": "sha512-mzo5dfJYwAn29PeiJ0zvwTo04zj8HDJj0Mn8TD7sno7q12prdbnasKJHhkm2c1LgrhlJ0teaea8860oxi51mGA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "lodash": "^4.17.14"
+      }
+    },
     "node_modules/async-each": {
       "version": "1.0.3",
       "resolved": "https://registry.npmjs.org/async-each/-/async-each-1.0.3.tgz",
@@ -560,15 +558,6 @@
         "node": ">=8.11"
       }
     },
-    "node_modules/bestikk-fs/node_modules/async": {
-      "version": "2.6.3",
-      "resolved": "https://registry.npmjs.org/async/-/async-2.6.3.tgz",
-      "integrity": "sha512-zflvls11DCy+dQWzTW2dzuilv8Z5X/pjfmZOWba6TNIVDm+2UDaJmXSOXlasHKfNBs8oo3M0aT50fDEWfKZjXg==",
-      "dev": true,
-      "dependencies": {
-        "lodash": "^4.17.14"
-      }
-    },
     "node_modules/bestikk-log": {
       "version": "0.1.0",
       "resolved": "https://registry.npmjs.org/bestikk-log/-/bestikk-log-0.1.0.tgz",
@@ -606,23 +595,27 @@
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     "node_modules/body-parser": {
-      "version": "1.19.2",
-      "resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.19.2.tgz",
-      "integrity": "sha512-SAAwOxgoCKMGs9uUAUFHygfLAyaniaoun6I8mFY9pRAJL9+Kec34aU+oIjDhTycub1jozEfEwx1W1IuOYxVSFw==",
+      "version": "1.20.2",
+      "resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.20.2.tgz",
+      "integrity": "sha512-ml9pReCu3M61kGlqoTm2umSXTlRTuGTx0bfYj+uIUKKYycG5NtSbeetV3faSU6R7ajOPw0g/J1PvK4qNy7s5bA==",
+      "license": "MIT",
       "dependencies": {
         "bytes": "3.1.2",
-        "content-type": "~1.0.4",
+        "content-type": "~1.0.5",
         "debug": "2.6.9",
-        "depd": "~1.1.2",
-        "http-errors": "1.8.1",
+        "depd": "2.0.0",
+        "destroy": "1.2.0",
+        "http-errors": "2.0.0",
         "iconv-lite": "0.4.24",
-        "on-finished": "~2.3.0",
-        "qs": "6.9.7",
-        "raw-body": "2.4.3",
-        "type-is": "~1.6.18"
+        "on-finished": "2.4.1",
+        "qs": "6.11.0",
+        "raw-body": "2.5.2",
+        "type-is": "~1.6.18",
+        "unpipe": "1.0.0"
       },
       "engines": {
-        "node": ">= 0.8"
+        "node": ">= 0.8",
+        "npm": "1.2.8000 || >= 1.4.16"
       }
     },
     "node_modules/boolbase": {
@@ -683,11 +676,12 @@
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-      "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.2.tgz",
-      "integrity": "sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==",
+      "version": "3.0.3",
+      "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.3.tgz",
+      "integrity": "sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==",
+      "license": "MIT",
       "dependencies": {
-        "fill-range": "^7.0.1"
+        "fill-range": "^7.1.1"
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+      "license": "MIT",
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@@ -832,12 +827,19 @@
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-      "integrity": "sha512-7O+FbCihrB5WGbFYesctwmTKae6rOiIzmz1icreWJ+0aA7LJfuqhEso2T9ncpcFtzMQtzXf2QGGueWJGTYsqrA==",
+      "version": "1.0.7",
+      "resolved": "https://registry.npmjs.org/call-bind/-/call-bind-1.0.7.tgz",
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+      "license": "MIT",
       "dependencies": {
-        "function-bind": "^1.1.1",
-        "get-intrinsic": "^1.0.2"
+        "es-define-property": "^1.0.0",
+        "es-errors": "^1.3.0",
+        "function-bind": "^1.1.2",
+        "get-intrinsic": "^1.2.4",
+        "set-function-length": "^1.2.1"
+      },
+      "engines": {
+        "node": ">= 0.4"
       },
       "funding": {
         "url": "https://github.com/sponsors/ljharb"
@@ -1199,9 +1201,10 @@
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-      "version": "5.1.0",
-      "resolved": "https://registry.npmjs.org/commander/-/commander-5.1.0.tgz",
-      "integrity": "sha512-P0CysNDQ7rtVw4QIQtm+MRxV66vKFSvlsQvGYXZWR3qFU0jlMKHZZZgw8e+8DSah4UDKMqnknRDQz+xuQXQ/Zg==",
+      "version": "6.1.0",
+      "resolved": "https://registry.npmjs.org/commander/-/commander-6.1.0.tgz",
+      "integrity": "sha512-wl7PNrYWd2y5mp1OK/LhTlv8Ff4kQJQRXXAvF+uU/TPNiVJUxZLRYGj/B0y/lPGAVcSbJqH2Za/cvHmrPMC8mA==",
+      "license": "MIT",
       "engines": {
         "node": ">= 6"
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@@ -1250,9 +1253,10 @@
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+      "resolved": "https://registry.npmjs.org/content-type/-/content-type-1.0.5.tgz",
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+      "license": "MIT",
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@@ -1271,9 +1275,10 @@
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-      "resolved": "https://registry.npmjs.org/convict/-/convict-6.0.0.tgz",
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+      "resolved": "https://registry.npmjs.org/convict/-/convict-6.0.1.tgz",
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@@ -1283,9 +1288,10 @@
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+      "resolved": "https://registry.npmjs.org/cookie/-/cookie-0.6.0.tgz",
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+      "license": "MIT",
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       }
@@ -1397,9 +1403,10 @@
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-      "integrity": "sha1-6zkTMzRYd1y4TNGh+uBiEGu4dUU=",
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+      "license": "MIT",
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@@ -1428,6 +1435,23 @@
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     },
+    "node_modules/define-data-property": {
+      "version": "1.1.4",
+      "resolved": "https://registry.npmjs.org/define-data-property/-/define-data-property-1.1.4.tgz",
+      "integrity": "sha512-rBMvIzlpA8v6E+SJZoo++HAYqsLrkg7MSfIinMPFhmkorw7X+dOXVJQs+QT69zGkzMyfDnIMN2Wid1+NbL3T+A==",
+      "license": "MIT",
+      "dependencies": {
+        "es-define-property": "^1.0.0",
+        "es-errors": "^1.3.0",
+        "gopd": "^1.0.1"
+      },
+      "engines": {
+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
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@@ -1487,17 +1511,23 @@
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+      "resolved": "https://registry.npmjs.org/depd/-/depd-2.0.0.tgz",
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+      "license": "MIT",
       "engines": {
-        "node": ">= 0.6"
+        "node": ">= 0.8"
       }
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-      "version": "1.0.4",
-      "resolved": "https://registry.npmjs.org/destroy/-/destroy-1.0.4.tgz",
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+      "version": "1.2.0",
+      "resolved": "https://registry.npmjs.org/destroy/-/destroy-1.2.0.tgz",
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+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.8",
+        "npm": "1.2.8000 || >= 1.4.16"
+      }
     },
     "node_modules/diff3": {
       "version": "0.0.3",
@@ -1616,12 +1646,14 @@
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-      "integrity": "sha1-WQxhFWsK4vTwJVcyoViyZrxWsh0="
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+      "license": "MIT"
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-      "integrity": "sha1-rT/0yG7C0CkyL1oCw6mmBslbP1k=",
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+      "license": "MIT",
       "engines": {
         "node": ">= 0.8"
       }
@@ -1643,10 +1675,32 @@
         "url": "https://github.com/fb55/entities?sponsor=1"
       }
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+    "node_modules/es-define-property": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/es-define-property/-/es-define-property-1.0.0.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "get-intrinsic": "^1.2.4"
+      },
+      "engines": {
+        "node": ">= 0.4"
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+    },
+    "node_modules/es-errors": {
+      "version": "1.3.0",
+      "resolved": "https://registry.npmjs.org/es-errors/-/es-errors-1.3.0.tgz",
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+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.4"
+      }
+    },
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       "version": "1.0.3",
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+      "license": "MIT"
     },
     "node_modules/escape-string-regexp": {
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+      "license": "MIT",
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         "node": ">= 0.6"
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@@ -1749,37 +1804,39 @@
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@@ -8442,6 +8661,11 @@
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@@ -8589,7 +8813,7 @@
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@@ -8715,15 +8939,6 @@
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@@ -8816,9 +9031,12 @@
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+      }
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@@ -8839,12 +9057,12 @@
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+      "resolved": "https://registry.npmjs.org/raw-body/-/raw-body-2.5.2.tgz",
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-        "http-errors": "1.8.1",
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@@ -9062,9 +9280,9 @@
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-      "resolved": "https://registry.npmjs.org/semver/-/semver-5.7.1.tgz",
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+      "version": "5.7.2",
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@@ -9075,23 +9293,23 @@
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+      "resolved": "https://registry.npmjs.org/send/-/send-0.18.0.tgz",
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         "debug": "2.6.9",
-        "depd": "~1.1.2",
-        "destroy": "~1.0.4",
+        "depd": "2.0.0",
+        "destroy": "1.2.0",
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         "escape-html": "~1.0.3",
         "etag": "~1.8.1",
         "fresh": "0.5.2",
-        "http-errors": "1.8.1",
+        "http-errors": "2.0.0",
         "mime": "1.6.0",
         "ms": "2.1.3",
-        "on-finished": "~2.3.0",
+        "on-finished": "2.4.1",
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-        "statuses": "~1.5.0"
+        "statuses": "2.0.1"
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@@ -9102,14 +9320,27 @@
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+      "version": "1.15.0",
+      "resolved": "https://registry.npmjs.org/serve-static/-/serve-static-1.15.0.tgz",
+      "integrity": "sha512-XGuRDNjXUijsUL0vl6nSD7cwURuzEgglbOaFuZM9g3kwDXOWVTck0jLzjPzGD+TazWbboZYu52/9/XPdUgne9g==",
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         "escape-html": "~1.0.3",
         "parseurl": "~1.3.3",
-        "send": "0.17.2"
+        "send": "0.18.0"
+      }
+    },
+    "set-function-length": {
+      "version": "1.2.2",
+      "resolved": "https://registry.npmjs.org/set-function-length/-/set-function-length-1.2.2.tgz",
+      "integrity": "sha512-pgRc4hJ4/sNjWCSS9AmnS40x3bNMDTknHgL5UaMBTMyJnU90EgWh1Rz+MC9eFu4BuN/UwZjKQuY/1v3rM7HMfg==",
+      "requires": {
+        "define-data-property": "^1.1.4",
+        "es-errors": "^1.3.0",
+        "function-bind": "^1.1.2",
+        "get-intrinsic": "^1.2.4",
+        "gopd": "^1.0.1",
+        "has-property-descriptors": "^1.0.2"
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@@ -9160,6 +9391,17 @@
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       "integrity": "sha1-2kL0l0DAtC2yypcoVxyxkMmO/qM="
     },
+    "side-channel": {
+      "version": "1.0.6",
+      "resolved": "https://registry.npmjs.org/side-channel/-/side-channel-1.0.6.tgz",
+      "integrity": "sha512-fDW/EZ6Q9RiO8eFG8Hj+7u/oW+XrPTIChwCOM2+th2A6OblDtYYIpve9m+KvI9Z4C9qSEXlaGR6bTEYHReuglA==",
+      "requires": {
+        "call-bind": "^1.0.7",
+        "es-errors": "^1.3.0",
+        "get-intrinsic": "^1.2.4",
+        "object-inspect": "^1.13.1"
+      }
+    },
     "signal-exit": {
       "version": "3.0.3",
       "resolved": "https://registry.npmjs.org/signal-exit/-/signal-exit-3.0.3.tgz",
@@ -9171,9 +9413,9 @@
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-      "version": "3.1.0",
-      "resolved": "https://registry.npmjs.org/simple-get/-/simple-get-3.1.0.tgz",
-      "integrity": "sha512-bCR6cP+aTdScaQCnQKbPKtJOKDp/hj9EDLJo3Nw4y1QksqaovlW/bnptB6/c1e+qmNIDHRK+oXFDdEqBT8WzUA==",
+      "version": "3.1.1",
+      "resolved": "https://registry.npmjs.org/simple-get/-/simple-get-3.1.1.tgz",
+      "integrity": "sha512-CQ5LTKGfCpvE1K0n2us+kuMPbk/q0EKl82s4aheV9oXjFEz6W/Y7oQFVJuU6QG77hRT4Ghb5RURteF5vnWjupA==",
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@@ -9352,9 +9594,9 @@
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-      "resolved": "https://registry.npmjs.org/statuses/-/statuses-1.5.0.tgz",
-      "integrity": "sha1-Fhx9rBd2Wf2YEfQ3cfqZOBR4Yow="
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/statuses/-/statuses-2.0.1.tgz",
+      "integrity": "sha512-RwNA9Z/7PrK06rYLIzFMlaF+l73iwpzsqRIFgbMLbTcLD6cOao82TaWefPXQvB2fOC4AjuYSEndS7N/mTCbkdQ=="
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@@ -9749,7 +9991,7 @@
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       "resolved": "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz",
-      "integrity": "sha1-sr9O6FFKrmFltIF4KdIbLvSZBOw="
+      "integrity": "sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ=="
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diff --git a/preview.yml b/preview.yml
index c6f7dfab..7120ab68 100644
--- a/preview.yml
+++ b/preview.yml
@@ -7,6 +7,7 @@ content:
   sources:
   - url: ./
     branches: HEAD
+    edit_url: ~
     exclude:
     - '!**/_includes/*'
     - '!**/readme.adoc'