diff --git a/data/src/Pipfile b/data/src/Pipfile
index 5c2b41e5..ebe4fd46 100644
--- a/data/src/Pipfile
+++ b/data/src/Pipfile
@@ -13,29 +13,29 @@ matplotlib = "*"
 rasterio = "*"
 scikit-learn = "*"
 mapclassify = "*"
-black = "*"
 fiona = "*"
 esridump = "*"
 sqlalchemy = "*"
 psycopg2-binary = "*"
-geoalchemy2 = "*"
 mapbox = "*"
 google-cloud-storage = "*"
 pydantic = "==2.8.2"
-data-diff = {extras = ["postgresql"], version = "*"}
 future = "*"
 slack-sdk = "*"
-pytest = "*"
 networkx = "*"
 libpysal = "*"
 jenkspy = "*"
 pyarrow = "*"
 tqdm = "*"
+geoalchemy2 ="*"
+
+[dev-packages]
+black = "*"
+pytest = "*"
 vulture = "*"
 pylint = "*"
 radon = "*"
-
-[dev-packages]
+ruff = "*"
 
 [requires]
 python_version = "3.11"
diff --git a/data/src/Pipfile.lock b/data/src/Pipfile.lock
index 1d6d561e..a88f8015 100644
--- a/data/src/Pipfile.lock
+++ b/data/src/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "96d3d538b2f2943e62c3ec5ca3d96a8e55831888b586e1d916cc400b1161e330"
+            "sha256": "b0cc0a48b0d260c226c0a714f60c880ea2b05027dcaba59b3eff007abe2ac1c9"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -25,13 +25,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==2.4.0"
         },
-        "agate": {
-            "hashes": [
-                "sha256:1cf329510b3dde07c4ad1740b7587c9c679abc3dcd92bb1107eabc10c2e03c50",
-                "sha256:bc60880c2ee59636a2a80cd8603d63f995be64526abf3cbba12f00767bcd5b3d"
-            ],
-            "version": "==1.9.1"
-        },
         "annotated-types": {
             "hashes": [
                 "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53",
@@ -42,11 +35,11 @@
         },
         "anyio": {
             "hashes": [
-                "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb",
-                "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"
+                "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48",
+                "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==4.6.0"
+            "version": "==4.7.0"
         },
         "argon2-cffi": {
             "hashes": [
@@ -91,20 +84,13 @@
             "markers": "python_version >= '3.8'",
             "version": "==1.3.0"
         },
-        "astroid": {
-            "hashes": [
-                "sha256:5eba185467253501b62a9f113c263524b4f5d55e1b30456370eed4cdbd6438fd",
-                "sha256:e73d0b62dd680a7c07cb2cd0ce3c22570b044dd01bd994bc3a2dd16c6cbba162"
-            ],
-            "markers": "python_full_version >= '3.9.0'",
-            "version": "==3.3.4"
-        },
         "asttokens": {
             "hashes": [
-                "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24",
-                "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"
+                "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7",
+                "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"
             ],
-            "version": "==2.4.1"
+            "markers": "python_version >= '3.8'",
+            "version": "==3.0.0"
         },
         "async-lru": {
             "hashes": [
@@ -130,14 +116,6 @@
             "markers": "python_version >= '3.8'",
             "version": "==2.16.0"
         },
-        "backports.tarfile": {
-            "hashes": [
-                "sha256:77e284d754527b01fb1e6fa8a1afe577858ebe4e9dad8919e34c862cb399bc34",
-                "sha256:d75e02c268746e1b8144c278978b6e98e85de6ad16f8e4b0844a154557eca991"
-            ],
-            "markers": "python_version < '3.12'",
-            "version": "==1.2.0"
-        },
         "beautifulsoup4": {
             "hashes": [
                 "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051",
@@ -146,66 +124,37 @@
             "markers": "python_full_version >= '3.6.0'",
             "version": "==4.12.3"
         },
-        "black": {
-            "hashes": [
-                "sha256:09cdeb74d494ec023ded657f7092ba518e8cf78fa8386155e4a03fdcc44679e6",
-                "sha256:1f13f7f386f86f8121d76599114bb8c17b69d962137fc70efe56137727c7047e",
-                "sha256:2500945420b6784c38b9ee885af039f5e7471ef284ab03fa35ecdde4688cd83f",
-                "sha256:2b59b250fdba5f9a9cd9d0ece6e6d993d91ce877d121d161e4698af3eb9c1018",
-                "sha256:3c4285573d4897a7610054af5a890bde7c65cb466040c5f0c8b732812d7f0e5e",
-                "sha256:505289f17ceda596658ae81b61ebbe2d9b25aa78067035184ed0a9d855d18afd",
-                "sha256:62e8730977f0b77998029da7971fa896ceefa2c4c4933fcd593fa599ecbf97a4",
-                "sha256:649f6d84ccbae73ab767e206772cc2d7a393a001070a4c814a546afd0d423aed",
-                "sha256:6e55d30d44bed36593c3163b9bc63bf58b3b30e4611e4d88a0c3c239930ed5b2",
-                "sha256:707a1ca89221bc8a1a64fb5e15ef39cd755633daa672a9db7498d1c19de66a42",
-                "sha256:72901b4913cbac8972ad911dc4098d5753704d1f3c56e44ae8dce99eecb0e3af",
-                "sha256:73bbf84ed136e45d451a260c6b73ed674652f90a2b3211d6a35e78054563a9bb",
-                "sha256:7c046c1d1eeb7aea9335da62472481d3bbf3fd986e093cffd35f4385c94ae368",
-                "sha256:81c6742da39f33b08e791da38410f32e27d632260e599df7245cccee2064afeb",
-                "sha256:837fd281f1908d0076844bc2b801ad2d369c78c45cf800cad7b61686051041af",
-                "sha256:972085c618ee94f402da1af548a4f218c754ea7e5dc70acb168bfaca4c2542ed",
-                "sha256:9e84e33b37be070ba135176c123ae52a51f82306def9f7d063ee302ecab2cf47",
-                "sha256:b19c9ad992c7883ad84c9b22aaa73562a16b819c1d8db7a1a1a49fb7ec13c7d2",
-                "sha256:d6417535d99c37cee4091a2f24eb2b6d5ec42b144d50f1f2e436d9fe1916fe1a",
-                "sha256:eab4dd44ce80dea27dc69db40dab62d4ca96112f87996bca68cd75639aeb2e4c",
-                "sha256:f490dbd59680d809ca31efdae20e634f3fae27fba3ce0ba3208333b713bc3920",
-                "sha256:fb6e2c0b86bbd43dee042e48059c9ad7830abd5c94b0bc518c0eeec57c3eddc1"
-            ],
-            "index": "pypi",
-            "markers": "python_version >= '3.8'",
-            "version": "==24.8.0"
-        },
         "bleach": {
             "hashes": [
-                "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe",
-                "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"
+                "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e",
+                "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"
             ],
-            "markers": "python_version >= '3.8'",
-            "version": "==6.1.0"
+            "markers": "python_version >= '3.9'",
+            "version": "==6.2.0"
         },
         "boto3": {
             "hashes": [
-                "sha256:d2851aec8e9dc6937977acbe9a5124ecc31b3ad5f50a10cd9ae52636da3f52fa",
-                "sha256:d89c3459db89c5408e83219ab849ffd0146bc4285e75cdc67c6e45d390a12df2"
+                "sha256:5ef7166fe5060637b92af8dc152cd7acecf96b3fc9c5456706a886cadb534391",
+                "sha256:fc8001519c8842e766ad3793bde3fbd0bb39e821a582fc12cf67876b8f3cf7f1"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==1.35.30"
+            "version": "==1.35.78"
         },
         "botocore": {
             "hashes": [
-                "sha256:3bb9f9dde001608671ea74681ac3cec06bbbb10cba8cb8c1387a25e843075ce0",
-                "sha256:ab5350e8a50e48d371fa2d517d65c29a40c43788cb9a15387f93eac5a23df0fd"
+                "sha256:41c37bd7c0326f25122f33ec84fb80fc0a14d7fcc9961431b0e57568e88c9cb5",
+                "sha256:6905036c25449ae8dba5e950e4b908e4b8a6fe6b516bf61e007ecb62fa21f323"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==1.35.30"
+            "version": "==1.35.78"
         },
         "cachecontrol": {
             "hashes": [
-                "sha256:7db1195b41c81f8274a7bbd97c956f44e8348265a1bc7641c37dfebc39f0c938",
-                "sha256:f5bf3f0620c38db2e5122c0726bdebb0d16869de966ea6a2befe92470b740ea0"
+                "sha256:06ef916a1e4eb7dba9948cdfc9c76e749db2e02104a9a1277e8b642591a0f717",
+                "sha256:65e3abd62b06382ce3894df60dde9e0deb92aeb734724f68fa4f3b91e97206b9"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==0.14.0"
+            "markers": "python_version >= '3.8'",
+            "version": "==0.14.1"
         },
         "cachetools": {
             "hashes": [
@@ -293,104 +242,119 @@
                 "sha256:f7f5baafcc48261359e14bcd6d9bff6d4b28d9103847c9e136694cb0501aef87",
                 "sha256:fc48c783f9c87e60831201f2cce7f3b2e4846bf4d8728eabe54d60700b318a0b"
             ],
-            "markers": "platform_python_implementation != 'PyPy'",
+            "markers": "python_version >= '3.8'",
             "version": "==1.17.1"
         },
         "charset-normalizer": {
             "hashes": [
-                "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027",
-                "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087",
-                "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786",
-                "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8",
-                "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09",
-                "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185",
-                "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574",
-                "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e",
-                "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519",
-                "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898",
-                "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269",
-                "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3",
-                "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f",
-                "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6",
-                "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8",
-                "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a",
-                "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73",
-                "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc",
-                "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714",
-                "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2",
-                "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc",
-                "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce",
-                "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d",
-                "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e",
-                "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6",
-                "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269",
-                "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96",
-                "sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d",
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-                "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed",
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-                "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae",
-                "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12",
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@@ -543,104 +455,13 @@
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@@ -650,14 +471,6 @@
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@@ -666,20 +479,6 @@
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@@ -698,10 +497,10 @@
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@@ -737,63 +536,66 @@
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@@ -807,12 +609,12 @@
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@@ -825,19 +627,19 @@
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@@ -849,12 +651,12 @@
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@@ -899,11 +701,11 @@
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@@ -994,19 +796,19 @@
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@@ -1016,22 +818,6 @@
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@@ -1042,11 +828,11 @@
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@@ -1183,17 +923,18 @@
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@@ -1633,28 +1321,6 @@
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@@ -1663,14 +1329,6 @@
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@@ -1741,21 +1399,13 @@
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@@ -1783,20 +1433,20 @@
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@@ -1808,70 +1458,64 @@
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@@ -2058,14 +1682,6 @@
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@@ -2428,15 +2027,6 @@
             "markers": "python_version >= '3.8'",
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@@ -2476,11 +2066,11 @@
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@@ -2488,6 +2078,7 @@
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@@ -2512,15 +2103,6 @@
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@@ -2537,21 +2119,6 @@
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@@ -2733,41 +2300,33 @@
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@@ -3002,14 +2562,6 @@
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@@ -3077,20 +2629,20 @@
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@@ -3110,67 +2662,67 @@
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@@ -3179,14 +2731,6 @@
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@@ -3195,13 +2739,6 @@
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@@ -3212,53 +2749,37 @@
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@@ -3270,11 +2791,11 @@
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@@ -3297,6 +2818,7 @@
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@@ -3309,20 +2831,11 @@
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@@ -3333,10 +2846,11 @@
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@@ -3360,15 +2874,216 @@
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+                "sha256:5a2221696a8224e335c28816a9d331a6c2ae15a2ee34ec857dcf3e45dbfa99ad",
+                "sha256:63f626344343083322233f175aaf372d326de8436f5928c042639a4afbbf1d3f",
+                "sha256:649fff99a20bd06c6f727d2a27f401331dc0cc861fb69cde910fe95b01b5928f",
+                "sha256:680359d932801c76d2e9c9068d05c6b107f2584b2a5b88831c83962eb9984c1b",
+                "sha256:846ea64c97afe3bc677b761787993be4991810ecc7a4a937816dd6bddedc4875",
+                "sha256:b5e39e0fae001df40f95bd8cc36b9165c5e2ea88900167bddf258bacef9bbdc3",
+                "sha256:ccfa1d0cb6200857f1923b602f978386a3a2758a65b52e0950299ea014be6800",
+                "sha256:d37d422772111794b26757c5b55a3eade028aa3fde43121ab7b673d050949d65",
+                "sha256:ddacb691cdcdf77b96f549cf9591701d8db36b2f19519373d60d31746068dbf2",
+                "sha256:e6668650ea4b685440857138e5fe40cde4d652633b1bdffc62933d0db4ed9812",
+                "sha256:f9da3333530dbcecc1be13e69c250ed8dfa67f43c4005fb537bb426e19200d50",
+                "sha256:fe4d6476887de70546212c99ac9bd803d90b42fc4767f058a0baa895013fbb3e"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.9'",
+            "version": "==24.10.0"
+        },
+        "click": {
+            "hashes": [
+                "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
+                "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==8.1.7"
+        },
+        "colorama": {
+            "hashes": [
+                "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44",
+                "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"
+            ],
+            "markers": "python_version >= '3.5'",
+            "version": "==0.4.6"
+        },
+        "dill": {
             "hashes": [
-                "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350",
-                "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"
+                "sha256:468dff3b89520b474c0397703366b7b95eebe6303f108adf9b19da1f702be87a",
+                "sha256:81aa267dddf68cbfe8029c42ca9ec6a4ab3b22371d1c450abc54422577b4512c"
+            ],
+            "markers": "python_version >= '3.11'",
+            "version": "==0.3.9"
+        },
+        "iniconfig": {
+            "hashes": [
+                "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3",
+                "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==2.0.0"
+        },
+        "isort": {
+            "hashes": [
+                "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109",
+                "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"
+            ],
+            "markers": "python_full_version >= '3.8.0'",
+            "version": "==5.13.2"
+        },
+        "mando": {
+            "hashes": [
+                "sha256:18baa999b4b613faefb00eac4efadcf14f510b59b924b66e08289aa1de8c3500",
+                "sha256:26ef1d70928b6057ee3ca12583d73c63e05c49de8972d620c278a7b206581a8a"
+            ],
+            "version": "==0.7.1"
+        },
+        "mccabe": {
+            "hashes": [
+                "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325",
+                "sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==0.7.0"
+        },
+        "mypy-extensions": {
+            "hashes": [
+                "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d",
+                "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"
+            ],
+            "markers": "python_version >= '3.5'",
+            "version": "==1.0.0"
+        },
+        "packaging": {
+            "hashes": [
+                "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759",
+                "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==3.20.2"
+            "version": "==24.2"
+        },
+        "pathspec": {
+            "hashes": [
+                "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08",
+                "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==0.12.1"
+        },
+        "platformdirs": {
+            "hashes": [
+                "sha256:357fb2acbc885b0419afd3ce3ed34564c13c9b95c89360cd9563f73aa5e2b907",
+                "sha256:73e575e1408ab8103900836b97580d5307456908a03e92031bab39e4554cc3fb"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==4.3.6"
+        },
+        "pluggy": {
+            "hashes": [
+                "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1",
+                "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==1.5.0"
+        },
+        "pylint": {
+            "hashes": [
+                "sha256:77f068c287d49b8683cd7c6e624243c74f92890f767f106ffa1ddf3c0a54cb7a",
+                "sha256:9ec054ec992cd05ad30a6df1676229739a73f8feeabf3912c995d17601052b01"
+            ],
+            "index": "pypi",
+            "markers": "python_full_version >= '3.9.0'",
+            "version": "==3.3.2"
+        },
+        "pytest": {
+            "hashes": [
+                "sha256:50e16d954148559c9a74109af1eaf0c945ba2d8f30f0a3d3335edde19788b6f6",
+                "sha256:965370d062bce11e73868e0335abac31b4d3de0e82f4007408d242b4f8610761"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.8'",
+            "version": "==8.3.4"
+        },
+        "radon": {
+            "hashes": [
+                "sha256:632cc032364a6f8bb1010a2f6a12d0f14bc7e5ede76585ef29dc0cecf4cd8859",
+                "sha256:d1ac0053943a893878940fedc8b19ace70386fc9c9bf0a09229a44125ebf45b5"
+            ],
+            "index": "pypi",
+            "version": "==6.0.1"
+        },
+        "ruff": {
+            "hashes": [
+                "sha256:1ca4e3a87496dc07d2427b7dd7ffa88a1e597c28dad65ae6433ecb9f2e4f022f",
+                "sha256:2aae99ec70abf43372612a838d97bfe77d45146254568d94926e8ed5bbb409ea",
+                "sha256:32096b41aaf7a5cc095fa45b4167b890e4c8d3fd217603f3634c92a541de7248",
+                "sha256:5fe716592ae8a376c2673fdfc1f5c0c193a6d0411f90a496863c99cd9e2ae25d",
+                "sha256:60f578c11feb1d3d257b2fb043ddb47501ab4816e7e221fbb0077f0d5d4e7b6f",
+                "sha256:705832cd7d85605cb7858d8a13d75993c8f3ef1397b0831289109e953d833d29",
+                "sha256:729850feed82ef2440aa27946ab39c18cb4a8889c1128a6d589ffa028ddcfc22",
+                "sha256:81c148825277e737493242b44c5388a300584d73d5774defa9245aaef55448b0",
+                "sha256:ac42caaa0411d6a7d9594363294416e0e48fc1279e1b0e948391695db2b3d5b1",
+                "sha256:b402ddee3d777683de60ff76da801fa7e5e8a71038f57ee53e903afbcefdaa58",
+                "sha256:b84f4f414dda8ac7f75075c1fa0b905ac0ff25361f42e6d5da681a465e0f78e5",
+                "sha256:c49ab4da37e7c457105aadfd2725e24305ff9bc908487a9bf8d548c6dad8bb3d",
+                "sha256:cbd5cf9b0ae8f30eebc7b360171bd50f59ab29d39f06a670b3e4501a36ba5897",
+                "sha256:d261d7850c8367704874847d95febc698a950bf061c9475d4a8b7689adc4f7fa",
+                "sha256:e769083da9439508833cfc7c23e351e1809e67f47c50248250ce1ac52c21fb93",
+                "sha256:ec016beb69ac16be416c435828be702ee694c0d722505f9c1f35e1b9c0cc1bf5",
+                "sha256:f05cdf8d050b30e2ba55c9b09330b51f9f97d36d4673213679b965d25a785f3c",
+                "sha256:fb88e2a506b70cfbc2de6fae6681c4f944f7dd5f2fe87233a7233d888bad73e8"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.7'",
+            "version": "==0.8.2"
+        },
+        "six": {
+            "hashes": [
+                "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274",
+                "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==1.17.0"
+        },
+        "tomlkit": {
+            "hashes": [
+                "sha256:7a974427f6e119197f670fbbbeae7bef749a6c14e793db934baefc1b5f03efde",
+                "sha256:fff5fe59a87295b278abd31bec92c15d9bc4a06885ab12bcea52c71119392e79"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==0.13.2"
+        },
+        "vulture": {
+            "hashes": [
+                "sha256:cb8277902a1138deeab796ec5bef7076a6e0248ca3607a3f3dee0b6d9e9b8415",
+                "sha256:d9a90dba89607489548a49d557f8bac8112bd25d3cbc8aeef23e860811bd5ed9"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.8'",
+            "version": "==2.14"
         }
-    },
-    "develop": {}
+    }
 }
diff --git a/data/src/main.py b/data/src/main.py
index e0131edd..66a30287 100644
--- a/data/src/main.py
+++ b/data/src/main.py
@@ -1,155 +1,107 @@
 import sys
-
-from new_etl.data_utils.access_process import access_process
-from new_etl.data_utils.contig_neighbors import contig_neighbors
-from new_etl.data_utils.dev_probability import dev_probability
-from new_etl.data_utils.negligent_devs import negligent_devs
-from new_etl.data_utils.opa_properties import opa_properties
-from new_etl.data_utils.priority_level import priority_level
-from new_etl.data_utils.vacant_properties import vacant_properties
-from new_etl.data_utils.pwd_parcels import pwd_parcels
-from new_etl.data_utils.city_owned_properties import city_owned_properties
-from new_etl.data_utils.phs_properties import phs_properties
-from new_etl.data_utils.li_violations import li_violations
-from new_etl.data_utils.li_complaints import li_complaints
-from new_etl.data_utils.rco_geoms import rco_geoms
-from new_etl.data_utils.council_dists import council_dists
-from new_etl.data_utils.tree_canopy import tree_canopy
-from new_etl.data_utils.nbhoods import nbhoods
-from new_etl.data_utils.gun_crimes import gun_crimes
-from new_etl.data_utils.drug_crimes import drug_crimes
-from new_etl.data_utils.delinquencies import delinquencies
-from new_etl.data_utils.unsafe_buildings import unsafe_buildings
-from new_etl.data_utils.imm_dang_buildings import imm_dang_buildings
-from new_etl.data_utils.tactical_urbanism import tactical_urbanism
-from new_etl.data_utils.conservatorship import conservatorship
-from new_etl.data_utils.owner_type import owner_type
-from new_etl.data_utils.community_gardens import community_gardens
-from new_etl.data_utils.park_priority import park_priority
-from new_etl.data_utils.ppr_properties import ppr_properties
-
 import pandas as pd
+import traceback
 
+from config.psql import conn
+from config.config import tiles_file_id_prefix
+
+from new_etl.classes.slack_reporters import send_dataframe_profile_to_slack, send_pg_stats_to_slack, send_error_to_slack
+from new_etl.classes.data_diff import DiffReport
+from new_etl.data_utils import *
+from new_etl.database import to_postgis_with_schema
 
 # Ensure the directory containing awkde is in the Python path
 awkde_path = "/usr/src/app"
 if awkde_path not in sys.path:
     sys.path.append(awkde_path)
 
-services = [
-    # vacant designation
-    vacant_properties,  # needs to run early so that other utils can make use of the `vacant` designation
-    # geometries/areas
-    pwd_parcels,
-    council_dists,
-    nbhoods,
-    rco_geoms,
-    # ownership
-    city_owned_properties,
-    phs_properties,
-    community_gardens,
-    ppr_properties,
-    owner_type,
-    # quality of life
-    li_violations,
-    li_complaints,
-    tree_canopy,
-    gun_crimes,
-    drug_crimes,
-    delinquencies,
-    unsafe_buildings,
-    imm_dang_buildings,
-    # development
-    contig_neighbors,
-    dev_probability,
-    negligent_devs,
-    # access/interventions
-    tactical_urbanism,
-    conservatorship,
-    park_priority,
-]
-
-dataset = opa_properties()
-
-print("Initial Dataset:")
-print("Shape:", dataset.gdf.shape)
-print("Head:\n", dataset.gdf.head())
-print("NA Counts:\n", dataset.gdf.isna().sum())
-
-for service in services:
-    dataset = service(dataset)
-    print(f"After {service.__name__}:")
-    print("Dataset type:", type(dataset.gdf).__name__)
-    print("Shape:", dataset.gdf.shape)
-    print("Head:\n", dataset.gdf.head())
-    print("NA Counts:\n", dataset.gdf.isna().sum())
-
-before_drop = dataset.gdf.shape[0]
-dataset.gdf = dataset.gdf.drop_duplicates(subset="opa_id")
-after_drop = dataset.gdf.shape[0]
-print(
-    f"Duplicate dataset rows dropped after initial services: {before_drop - after_drop}"
-)
-
-# Add Priority Level
-dataset = priority_level(dataset)
-
-# Print the distribution of "priority_level"
-distribution = dataset.gdf["priority_level"].value_counts()
-print("Distribution of priority level:")
-print(distribution)
-
-# Add Access Process
-dataset = access_process(dataset)
-
-# Print the distribution of "access_process"
-distribution = dataset.gdf["access_process"].value_counts()
-print("Distribution of access process:")
-print(distribution)
-
-before_drop = dataset.gdf.shape[0]
-dataset.gdf = dataset.gdf.drop_duplicates(subset="opa_id")
-after_drop = dataset.gdf.shape[0]
-print(f"Duplicate final dataset rows droppeds: {before_drop - after_drop}")
-
-# Convert problematic columns to numeric
-numeric_columns = [
-    "market_value",
-    "sale_price",
-    "total_assessment",
-    "total_due",
-    "num_years_owed",
-    "permit_count",
-]
-for col in numeric_columns:
-    dataset.gdf[col] = pd.to_numeric(dataset.gdf[col], errors="coerce")
-
-dataset.gdf["most_recent_year_owed"] = dataset.gdf["most_recent_year_owed"].astype(str)
-
-print("Column data types before exporting to Parquet:")
-print(dataset.gdf.dtypes)
-
-# Quick dataset profiling
-print("\nQuick dataset profile:")
-
-# 1) Number of NA values per column
-print("\nNumber of NA values per column:")
-print(dataset.gdf.isna().sum())
-
-# 2) Mean, median, and std of numeric columns
-print("\nMean, Median, and Standard Deviation of numeric columns:")
-numeric_columns = dataset.gdf.select_dtypes(include=["float", "int"]).columns
-
-for column in numeric_columns:
-    mean = dataset.gdf[column].mean()
-    median = dataset.gdf[column].median()
-    std = dataset.gdf[column].std()
-    print(f"{column}:\n  Mean: {mean:.2f}\n  Median: {median:.2f}\n  Std: {std:.2f}")
-
-# 3) Number of unique values in string columns
-print("\nNumber of unique values in string columns:")
-string_columns = dataset.gdf.select_dtypes(include=["object", "string"]).columns
-unique_values = dataset.gdf[string_columns].nunique()
-print(unique_values)
-
-dataset.gdf.to_parquet("tmp/test_output.parquet")
+
+try:
+
+    print("Starting ETL process.")
+
+    services = [
+        vacant_properties,  # Run early for other utils to use the `vacant` designation
+        pwd_parcels,
+        council_dists,
+        nbhoods,
+        rco_geoms,
+        city_owned_properties,
+        phs_properties,
+        community_gardens,
+        ppr_properties,
+        owner_type,
+        li_violations,
+        li_complaints,
+        tree_canopy,
+        gun_crimes,
+        drug_crimes,
+        delinquencies,
+        unsafe_buildings,
+        imm_dang_buildings,
+        contig_neighbors,
+        dev_probability,
+        negligent_devs,
+        tactical_urbanism,
+        conservatorship,
+        park_priority,
+    ]
+
+    print("Loading OPA properties dataset.")
+    dataset = opa_properties()
+
+    for service in services:
+        print(f"Running service: {service.__name__}")
+        dataset = service(dataset)
+
+    print("Applying final dataset transformations.")
+    dataset = priority_level(dataset)
+    dataset = access_process(dataset)
+
+    # Drop duplicates
+    before_drop = dataset.gdf.shape[0]
+    dataset.gdf = dataset.gdf.drop_duplicates(subset="opa_id")
+    print(f"Duplicate rows dropped: {before_drop - dataset.gdf.shape[0]}")
+
+    # Convert columns to numeric where necessary
+    numeric_columns = [
+        "market_value",
+        "sale_price",
+        "total_assessment",
+        "total_due",
+        "num_years_owed",
+        "permit_count",
+    ]
+    dataset.gdf[numeric_columns] = dataset.gdf[numeric_columns].apply(pd.to_numeric, errors="coerce")
+    dataset.gdf["most_recent_year_owed"] = dataset.gdf["most_recent_year_owed"].astype(str)
+
+    # Dataset profiling
+    send_dataframe_profile_to_slack(dataset.gdf, "all_properties_end")
+
+    # Save dataset to PostgreSQL
+    to_postgis_with_schema(dataset.gdf, "all_properties_end", conn)
+
+    # Generate and send diff report
+    diff_report = DiffReport()
+    diff_report.run()
+
+    send_pg_stats_to_slack(conn)  # Send PostgreSQL stats to Slack
+
+    # Save local Parquet file
+    parquet_path = "tmp/test_output.parquet"
+    dataset.gdf.to_parquet(parquet_path)
+    print(f"Dataset saved to Parquet: {parquet_path}")
+
+    # Publish only vacant properties
+    dataset.gdf = dataset.gdf[dataset.gdf["vacant"]]
+    dataset.build_and_publish(tiles_file_id_prefix)
+
+    # Finalize
+    conn.commit()
+    conn.close()
+    print("ETL process completed successfully.")
+
+except Exception as e:
+    error_message = f"Error in backend job: {str(e)}\n\n{traceback.format_exc()}"
+    send_error_to_slack(error_message)
+    raise  # Optionally re-raise the exception
diff --git a/data/src/new_etl/classes/data_diff.py b/data/src/new_etl/classes/data_diff.py
new file mode 100644
index 00000000..3dc40bfb
--- /dev/null
+++ b/data/src/new_etl/classes/data_diff.py
@@ -0,0 +1,152 @@
+from slack_sdk import WebClient
+import pandas as pd
+from sqlalchemy import text
+import os
+
+from config.psql import conn
+
+
+class DiffReport:
+    def __init__(
+        self, conn=conn, table_name="all_properties_end", unique_id_col="opa_id"
+    ):
+        """
+        Initialize the DiffReport.
+
+        Args:
+            conn: SQLAlchemy connection to the database.
+            table_name (str): The name of the table to analyze.
+            unique_id_col (str): Column used as a unique identifier.
+        """
+        self.conn = conn
+        self.table_name = table_name
+        self.unique_id_col = unique_id_col
+        self.latest_timestamp = None
+        self.previous_timestamp = None
+        self.summary_text = ""
+
+    def generate_diff(self):
+        """
+        Generate the data diff and summarize changes.
+        """
+        # Step 1: Retrieve the two most recent timestamps
+        query_timestamps = text(f"""
+        SELECT DISTINCT create_date 
+        FROM {self.table_name}
+        ORDER BY create_date DESC
+        LIMIT 2;
+        """)
+
+        timestamps = pd.read_sql(query_timestamps, self.conn)["create_date"].tolist()
+
+        if len(timestamps) < 2:
+            print(
+                f"Table '{self.table_name}' has less than two timestamps. Cannot perform comparison."
+            )
+            return
+
+        self.latest_timestamp, self.previous_timestamp = timestamps[0], timestamps[1]
+
+        print("Last two timestamps are:")
+        print(self.latest_timestamp)
+        print(self.previous_timestamp)
+
+        # Step 2: Load data for the two timestamps into DataFrames
+        query_latest = text(f"""
+        SELECT * FROM {self.table_name} WHERE create_date = '{self.latest_timestamp}';
+        """)
+        query_previous = text(f"""
+        SELECT * FROM {self.table_name} WHERE create_date = '{self.previous_timestamp}';
+        """)
+
+        df_latest = pd.read_sql(query_latest, self.conn)
+        df_previous = pd.read_sql(query_previous, self.conn)
+
+        # Step 3: Ensure DataFrames are aligned on the same index and columns
+        common_columns = [
+            col for col in df_latest.columns if col in df_previous.columns
+        ]
+        df_latest = df_latest[common_columns]
+        df_previous = df_previous[common_columns]
+
+        # Align indexes to include all rows from both DataFrames
+        df_latest = df_latest.set_index(self.unique_id_col).reindex(
+            df_previous.index.union(df_latest.index)
+        )
+        df_previous = df_previous.set_index(self.unique_id_col).reindex(
+            df_previous.index.union(df_latest.index)
+        )
+
+        # Ensure columns are in the same order
+        df_latest = df_latest[sorted(df_latest.columns)]
+        df_previous = df_previous[sorted(df_previous.columns)]
+
+        # Step 4: Perform the comparison
+        diff = df_latest.compare(
+            df_previous, align_axis=1, keep_shape=False, keep_equal=False
+        )
+
+        if diff.empty:
+            print("No changes detected between the two timestamps.")
+            self.summary_text = "No changes detected between the two timestamps."
+            return
+
+        # Step 5: Calculate percentage changes
+        print("Calculating percentages...")
+        total_rows = len(df_latest)
+        changes_by_column = {
+            col: (diff.xs(col, level=0, axis=1).notna().sum().sum() / total_rows) * 100
+            for col in diff.columns.get_level_values(0).unique()
+        }
+
+        # Step 6: Create plain text summary
+        summary_lines = [
+            f"Diff Report for {self.table_name}",
+            f"Latest timestamp: {self.latest_timestamp}",
+            f"Previous timestamp: {self.previous_timestamp}",
+            "",
+            "Comparison Summary (% of rows with changes per column):",
+        ]
+
+        for col, pct_change in sorted(
+            changes_by_column.items(), key=lambda x: x[1], reverse=True
+        ):
+            summary_lines.append(f"  - {col}: {pct_change:.2f}%")
+
+        self.summary_text = "\n".join(summary_lines)
+
+    def send_to_slack(self, channel="clean-and-green-philly-pipeline"):
+        """
+        Sends the diff summary to a Slack channel.
+
+        Args:
+            channel (str): The Slack channel to post the message to.
+        """
+        token = os.getenv("CAGP_SLACK_API_TOKEN")
+        if token:
+            client = WebClient(token=token)
+            try:
+                client.chat_postMessage(
+                    channel=channel,
+                    text=f"*Data Difference Report*\n\n{self.summary_text}",
+                    username="Diff Reporter",
+                )
+                print("Diff report sent to Slack successfully.")
+            except Exception as e:
+                print(f"Failed to send diff report to Slack: {e}")
+        else:
+            raise ValueError("Slack API token not found in environment variables.")
+
+    def run(self, send_to_slack=True, slack_channel="clean-and-green-philly-pipeline"):
+        """
+        Orchestrates the diff generation and optional Slack notification.
+
+        Args:
+            send_to_slack (bool): Whether to send the diff summary to Slack.
+            slack_channel (str): The Slack channel to post the message to.
+        """
+        self.generate_diff()
+
+        if send_to_slack and self.summary_text:
+            print(f"Sending report to Slack channel: {slack_channel}...")
+            self.send_to_slack(channel=slack_channel)
diff --git a/data/src/new_etl/classes/featurelayer.py b/data/src/new_etl/classes/featurelayer.py
index fce57fbf..9e5fd08b 100644
--- a/data/src/new_etl/classes/featurelayer.py
+++ b/data/src/new_etl/classes/featurelayer.py
@@ -10,22 +10,21 @@
 from config.config import (
     FORCE_RELOAD,
     USE_CRS,
+    log_level,
     min_tiles_file_size_in_bytes,
     write_production_tiles_file,
 )
 from config.psql import conn, local_engine
-from esridump.dumper import EsriDumper
 from google.cloud import storage
 from google.cloud.storage.bucket import Bucket
 from shapely import wkb
-
 from concurrent.futures import ThreadPoolExecutor, as_completed
-from tqdm.auto import tqdm
-
 from tqdm import tqdm
 
+from new_etl.loaders import load_esri_data, load_carto_data
+from new_etl.database import to_postgis_with_schema
 
-log.basicConfig(level=log.INFO)
+log.basicConfig(level=log_level)
 
 
 def google_cloud_bucket() -> Bucket:
@@ -62,7 +61,7 @@ def __init__(
         from_xy=False,
         use_wkb_geom_field=None,
         cols: list[str] = None,
-        max_workers=16,
+        max_workers=os.cpu_count(),
         chunk_size=100000,
     ):
         self.name = name
@@ -118,75 +117,43 @@ def check_psql(self):
 
     def load_data(self):
         log.info(f"Loading data for {self.name} from {self.type}...")
-        if self.type == "gdf":
-            pass
-        else:
-            try:
-                if self.type == "esri":
-                    if not self.esri_rest_urls:
-                        raise ValueError("Must provide a URL to load data from Esri")
-
-                    gdfs = []
-                    for url in self.esri_rest_urls:
-                        print(f"Processing URL: {url}")  # Debugging: Print the URL
-
-                        # Use EsriDumper to get features
-                        dumper = EsriDumper(url)
-                        features = [feature for feature in dumper]
-
-                        if not features:
-                            log.error(f"No features returned for URL: {url}")
-                            continue
-
-                        geojson_features = {
-                            "type": "FeatureCollection",
-                            "features": features,
-                        }
-                        gdf = gpd.GeoDataFrame.from_features(
-                            geojson_features, crs=self.input_crs
-                        )
-                        gdf = gdf.to_crs(self.crs)
-
-                        # Add parcel_type based on the URL
-                        if "Vacant_Indicators_Land" in url:
-                            gdf["parcel_type"] = "Land"
-                        elif "Vacant_Indicators_Bldg" in url:
-                            gdf["parcel_type"] = "Building"
-
-                        gdfs.append(gdf)
 
-                    # Concatenate all dataframes
-                    self.gdf = pd.concat(gdfs, ignore_index=True)
+        if self.type == "gdf":
+            return  # Skip processing for gdf type
 
-                elif self.type == "carto":
-                    self._load_carto_data()
+        try:
+            # Load data based on the source type
+            if self.type == "esri":
+                self.gdf = load_esri_data(self.esri_rest_urls, self.input_crs, self.crs)
+            elif self.type == "carto":
+                self.gdf = load_carto_data(
+                    self.carto_sql_queries,
+                    self.max_workers,
+                    self.chunk_size,
+                    self.use_wkb_geom_field,
+                    self.input_crs,
+                    self.crs,
+                )
 
-                # Convert all column names to lowercase
-                if not self.gdf.empty:
-                    self.gdf.columns = [col.lower() for col in self.gdf.columns]
+            # Standardize column names
+            if not self.gdf.empty:
+                self.gdf.columns = [col.lower() for col in self.gdf.columns]
 
-                # Drop columns not in self.cols, if specified
+                # Filter columns if specified
                 if self.cols:
-                    self.cols = [
-                        col.lower() for col in self.cols
-                    ]  # Ensure self.cols is lowercase
+                    self.cols = [col.lower() for col in self.cols]
                     self.cols.append("geometry")
                     self.gdf = self.gdf[
                         [col for col in self.cols if col in self.gdf.columns]
                     ]
 
-                # Save to PostGIS
-                self.gdf.to_postgis(
-                    name=self.psql_table,
-                    con=conn,
-                    if_exists="replace",
-                    chunksize=1000,
-                )
+                # Save GeoDataFrame to PostgreSQL and configure it as a hypertable
+                to_postgis_with_schema(self.gdf, self.psql_table, conn)
 
-            except Exception as e:
-                log.error(f"Error loading data for {self.name}: {e}")
-                traceback.print_exc()
-                self.gdf = gpd.GeoDataFrame()
+        except Exception as e:
+            log.error(f"Error loading data for {self.name}: {e}")
+            traceback.print_exc()
+            self.gdf = gpd.GeoDataFrame()  # Reset to an empty GeoDataFrame
 
     def _load_carto_data(self):
         if not self.carto_sql_queries:
@@ -324,7 +291,6 @@ def build_and_publish(self, tiles_file_id_prefix: str) -> None:
         temp_pmtiles_points: str = f"tmp/temp_{tiles_file_id_prefix}_points.pmtiles"
         temp_pmtiles_polygons: str = f"tmp/temp_{tiles_file_id_prefix}_polygons.pmtiles"
         temp_merged_pmtiles: str = f"tmp/temp_{tiles_file_id_prefix}_merged.pmtiles"
-        temp_parquet: str = f"tmp/{tiles_file_id_prefix}.parquet"
 
         # Reproject
         gdf_wm = self.gdf.to_crs(epsg=4326)
diff --git a/data/src/new_etl/classes/slack_reporters.py b/data/src/new_etl/classes/slack_reporters.py
new file mode 100644
index 00000000..6ae08517
--- /dev/null
+++ b/data/src/new_etl/classes/slack_reporters.py
@@ -0,0 +1,177 @@
+from sqlalchemy import text
+import os
+from slack_sdk import WebClient
+
+import pandas as pd
+
+
+def send_dataframe_profile_to_slack(
+    df: pd.DataFrame, df_name: str, channel="clean-and-green-philly-pipeline"
+):
+    """
+    Profiles a DataFrame and sends the QC profile summary to a Slack channel.
+
+    Args:
+        df (pd.DataFrame): The DataFrame to profile.
+        df_name (str): The name of the DataFrame being profiled.
+        channel (str): The Slack channel to post the message to.
+    """
+    # Step 1: Profile the DataFrame
+    profile_summary = {}
+
+    # Count NA values
+    profile_summary["na_counts"] = df.isna().sum().to_dict()
+
+    # Numeric stats
+    numeric_columns = df.select_dtypes(include=["number"]).columns
+    profile_summary["numeric_stats"] = {}
+    for column in numeric_columns:
+        profile_summary["numeric_stats"][column] = {
+            "mean": df[column].mean(),
+            "median": df[column].median(),
+            "std": df[column].std(),
+        }
+
+    # Unique values in string columns
+    string_columns = df.select_dtypes(include=["object", "string"]).columns
+    profile_summary["unique_values"] = df[string_columns].nunique().to_dict()
+
+    # Step 2: Format the message
+    message = f"*Dataset QC Summary: `{df_name}`*\n"
+
+    # Missing Values
+    message += "*Missing Values:*\n"
+    for col, count in profile_summary["na_counts"].items():
+        message += f"  - `{col}`: {count} missing\n"
+
+    # Numeric Summary
+    message += "\n*Numeric Summary:*\n"
+    for col, stats in profile_summary["numeric_stats"].items():
+        message += (
+            f"  - `{col}`: Mean: {stats['mean']:.2f}, "
+            f"Median: {stats['median']:.2f}, Std: {stats['std']:.2f}\n"
+        )
+
+    # Unique Values
+    message += "\n*Unique Values in String Columns:*\n"
+    for col, unique_count in profile_summary["unique_values"].items():
+        message += f"  - `{col}`: {unique_count} unique values\n"
+
+    # Step 3: Send to Slack
+    token = os.getenv("CAGP_SLACK_API_TOKEN")
+    if token:
+        client = WebClient(token=token)
+        try:
+            client.chat_postMessage(
+                channel=channel,
+                text=message,
+                username="QC Reporter",
+            )
+            print(f"QC profile for `{df_name}` sent to Slack successfully.")
+        except Exception as e:
+            print(f"Failed to send QC profile for `{df_name}` to Slack: {e}")
+    else:
+        raise ValueError("Slack API token not found in environment variables.")
+
+
+def send_pg_stats_to_slack(conn):
+    """
+    Report total sizes for all hypertables using hypertable_detailed_size
+    and send the result to a Slack channel.
+    """
+    # Step 1: Get all hypertable names
+    hypertable_query = """
+    SELECT hypertable_name
+    FROM timescaledb_information.hypertables;
+    """
+    result = conn.execute(text(hypertable_query))
+    hypertables = [row[0] for row in result]  # Extract first column of each tuple
+
+    # Step 2: Query detailed size for each hypertable
+    detailed_sizes = []
+    for hypertable in hypertables:
+        size_query = f"SELECT * FROM hypertable_detailed_size('{hypertable}');"
+        size_result = conn.execute(text(size_query))
+        for row in size_result:
+            # Append the total size (row[3] = total_bytes)
+            detailed_sizes.append(
+                {
+                    "hypertable": hypertable,
+                    "total_bytes": row[3],
+                }
+            )
+
+    # Step 3: Format the message for Slack
+    message = "*Hypertable Total Sizes:*\n"
+    for size in detailed_sizes:
+        total_bytes = size["total_bytes"]
+        total_size = (
+            f"{total_bytes / 1073741824:.2f} GB"
+            if total_bytes >= 1073741824
+            else f"{total_bytes / 1048576:.2f} MB"
+            if total_bytes >= 1048576
+            else f"{total_bytes / 1024:.2f} KB"
+        )
+        message += f"- {size['hypertable']}: {total_size}\n"
+
+    # Step 4: Send to Slack
+    token = os.getenv("CAGP_SLACK_API_TOKEN")
+    if token:
+        client = WebClient(token=token)
+        client.chat_postMessage(
+            channel="clean-and-green-philly-pipeline",
+            text=message,
+            username="PG Stats Reporter",
+        )
+    else:
+        raise ValueError("Slack API token not found in environment variables.")
+
+
+def send_diff_report_to_slack(
+    diff_summary: str, report_url: str, channel="clean-and-green-philly-pipeline"
+):
+    """
+    Sends a difference report summary to a Slack channel.
+
+    Args:
+        diff_summary (str): The summary of differences to post.
+        report_url (str): The URL to the detailed difference report.
+        channel (str): The Slack channel to post the message to.
+    """
+    print(
+        f"send_diff_report_to_slack called with:\ndiff_summary:\n{diff_summary}\nreport_url: {report_url}"
+    )
+
+    # Step 1: Format the message
+    message = f"*Data Difference Report*\n\n{diff_summary}\n\nDetailed report: <{report_url}|View Report>"
+    print(f"Formatted Slack message:\n{message}")
+
+    # Step 2: Send the message to Slack
+    token = os.getenv("CAGP_SLACK_API_TOKEN")
+    if token:
+        client = WebClient(token=token)
+        try:
+            client.chat_postMessage(
+                channel=channel,
+                text=message,
+                username="Diff Reporter",
+            )
+            print("Diff report sent to Slack successfully.")
+        except Exception as e:
+            print(f"Failed to send diff report to Slack: {e}")
+    else:
+        raise ValueError("Slack API token not found in environment variables.")
+
+
+def send_error_to_slack(error_message: str) -> None:
+    """Send error message to Slack."""
+    token: str | None = os.getenv("CAGP_SLACK_API_TOKEN")  # token can be None
+    if token:
+        client = WebClient(token=token)
+        client.chat_postMessage(
+            channel="clean-and-green-philly-back-end",  # Replace with actual Slack channel ID
+            text=error_message,
+            username="Backend Error Reporter",
+        )
+    else:
+        raise ValueError("Slack API token not found in environment variables.")
diff --git a/data/src/new_etl/data_utils/__init__.py b/data/src/new_etl/data_utils/__init__.py
index d351ecd4..e464f7c8 100644
--- a/data/src/new_etl/data_utils/__init__.py
+++ b/data/src/new_etl/data_utils/__init__.py
@@ -6,11 +6,25 @@
 from .tree_canopy import tree_canopy
 from .nbhoods import nbhoods
 from .gun_crimes import gun_crimes
+from .drug_crimes import drug_crimes  # Add missing import
 from .delinquencies import delinquencies
 from .opa_properties import opa_properties
 from .vacant_properties import vacant_properties
 from .priority_level import priority_level
 from .access_process import access_process
+from .contig_neighbors import contig_neighbors  # Add missing import
+from .dev_probability import dev_probability  # Add missing import
+from .negligent_devs import negligent_devs  # Add missing import
+from .pwd_parcels import pwd_parcels  # Add missing import
+from .unsafe_buildings import unsafe_buildings  # Add missing import
+from .imm_dang_buildings import imm_dang_buildings  # Add missing import
+from .tactical_urbanism import tactical_urbanism  # Add missing import
+from .conservatorship import conservatorship  # Add missing import
+from .owner_type import owner_type  # Add missing import
+from .community_gardens import community_gardens  # Add missing import
+from .park_priority import park_priority  # Add missing import
+from .ppr_properties import ppr_properties  # Add missing import
+from .council_dists import council_dists
 
 __all__ = [
     "city_owned_properties",
@@ -21,9 +35,23 @@
     "tree_canopy",
     "nbhoods",
     "gun_crimes",
+    "drug_crimes",  # Ensure completeness
     "delinquencies",
     "opa_properties",
     "vacant_properties",
     "priority_level",
     "access_process",
+    "contig_neighbors",
+    "dev_probability",
+    "negligent_devs",
+    "pwd_parcels",
+    "unsafe_buildings",
+    "imm_dang_buildings",
+    "tactical_urbanism",
+    "conservatorship",
+    "owner_type",
+    "community_gardens",
+    "park_priority",
+    "ppr_properties",
+    "council_dists",
 ]
diff --git a/data/src/new_etl/data_utils/city_owned_properties.py b/data/src/new_etl/data_utils/city_owned_properties.py
index e9d93059..7e967712 100644
--- a/data/src/new_etl/data_utils/city_owned_properties.py
+++ b/data/src/new_etl/data_utils/city_owned_properties.py
@@ -22,8 +22,6 @@ def city_owned_properties(primary_featurelayer: FeatureLayer) -> FeatureLayer:
         cols=["OPABRT", "AGENCY", "SIDEYARDELIGIBLE"],
     )
 
-    print("Columns for city_owned_properties:", city_owned_properties.gdf.columns)
-
     city_owned_properties.gdf.dropna(subset=["opabrt"], inplace=True)
 
     primary_featurelayer.opa_join(city_owned_properties.gdf, "opabrt")
diff --git a/data/src/new_etl/data_utils/community_gardens.py b/data/src/new_etl/data_utils/community_gardens.py
index a47b6760..80fa00df 100644
--- a/data/src/new_etl/data_utils/community_gardens.py
+++ b/data/src/new_etl/data_utils/community_gardens.py
@@ -12,11 +12,6 @@ def community_gardens(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     if "vacant" not in primary_featurelayer.gdf.columns:
         raise ValueError("The 'vacant' column is missing in the primary feature layer.")
 
-    print(
-        "Geometry types in primary feature layer:",
-        primary_featurelayer.gdf.geometry.type.value_counts(),
-    )
-
     # Load community gardens
     community_gardens = FeatureLayer(
         name="Community Gardens", esri_rest_urls=COMMUNITY_GARDENS_TO_LOAD
@@ -29,23 +24,11 @@ def community_gardens(primary_featurelayer: FeatureLayer) -> FeatureLayer:
         )
         community_gardens.gdf = community_gardens.gdf.to_crs(USE_CRS)
 
-    # Check geometry types
+    # Identify problematic gardens
     geom_types = community_gardens.gdf.geometry.geom_type.value_counts()
-    print("\nCommunity gardens geometry types:")
-    print(geom_types)
 
-    # Identify problematic gardens
     if len(geom_types) > 1:
-        print("\nGardens with non-Point geometries:")
-        non_point_gardens = community_gardens.gdf[
-            community_gardens.gdf.geometry.geom_type != "Point"
-        ]
-        print(f"Total non-Point geometries: {len(non_point_gardens)}")
-        print("\nSample of problematic records:")
-        print(non_point_gardens[["site_name", "geometry"]].head())
-
         # Convert any non-point geometries to points using centroid
-        print("\nConverting non-Point geometries to points using centroids...")
         community_gardens.gdf.loc[
             community_gardens.gdf.geometry.geom_type != "Point", "geometry"
         ] = community_gardens.gdf[
@@ -66,38 +49,6 @@ def community_gardens(primary_featurelayer: FeatureLayer) -> FeatureLayer:
         community_gardens.gdf, predicate="contains", how="inner"
     )
 
-    # Count matches per garden
-    matches_per_garden = joined_gdf.groupby("site_name").size()
-    print("\nMatches per garden:")
-    print(f"Min matches: {matches_per_garden.min()}")
-    print(f"Max matches: {matches_per_garden.max()}")
-    print(f"Average matches: {matches_per_garden.mean():.2f}")
-
-    # Print details for gardens with unusually high matches
-    # Gardens with high number of matches
-    if matches_per_garden.max() > 1:  # arbitrary threshold
-        print("\nGardens with high number of matches:")
-        high_matches = matches_per_garden[matches_per_garden > 1]
-        print(high_matches)
-
-        # Print concise details about properties matching these gardens
-        print("\nSummary of matched properties for high-match gardens:")
-        for garden_name in high_matches.index:
-            matched_properties = joined_gdf[joined_gdf["site_name"] == garden_name]
-            print(f"\nGarden: {garden_name}")
-            print("Matched parcels:")
-            print(
-                matched_properties[["opa_id"]]
-                .drop_duplicates()
-                .head(5)
-                .to_string(index=False)
-            )
-            print(
-                f"...and {len(matched_properties) - 5} more matches."
-                if len(matched_properties) > 5
-                else ""
-            )
-
     # Get unique parcels that contain garden points
     garden_parcels = set(joined_gdf["opa_id"])
     print(f"\nUnique parcels containing gardens: {len(garden_parcels)}")
diff --git a/data/src/new_etl/data_utils/conservatorship.py b/data/src/new_etl/data_utils/conservatorship.py
index 2294e3ac..4e53c0fe 100644
--- a/data/src/new_etl/data_utils/conservatorship.py
+++ b/data/src/new_etl/data_utils/conservatorship.py
@@ -1,3 +1,4 @@
+from ..classes.featurelayer import FeatureLayer
 import datetime
 from dateutil.parser import parse
 import pytz
@@ -8,7 +9,17 @@
 )
 
 
-def conservatorship(primary_featurelayer):
+def conservatorship(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Determines conservatorship eligibility for properties in a feature layer.
+
+    Args:
+        primary_featurelayer (FeatureLayer): A feature layer containing property data in a GeoDataFrame (`gdf`).
+
+    Returns:
+        FeatureLayer: The input feature layer with an added "conservatorship" column indicating
+        whether each property qualifies for conservatorship ("Yes" or "No").
+    """
     conservatorships = []
 
     for idx, row in primary_featurelayer.gdf.iterrows():
diff --git a/data/src/new_etl/data_utils/contig_neighbors.py b/data/src/new_etl/data_utils/contig_neighbors.py
index 48656bbd..c05f9d4c 100644
--- a/data/src/new_etl/data_utils/contig_neighbors.py
+++ b/data/src/new_etl/data_utils/contig_neighbors.py
@@ -2,9 +2,20 @@
 import networkx as nx
 from libpysal.weights import Queen
 import numpy as np
+from ..classes.featurelayer import FeatureLayer
 
 
-def contig_neighbors(primary_featurelayer):
+def contig_neighbors(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Calculates the number of contiguous vacant neighbors for each property in a feature layer.
+
+    Args:
+        primary_featurelayer (FeatureLayer): A feature layer containing property data in a GeoDataFrame (`gdf`).
+
+    Returns:
+        FeatureLayer: The input feature layer with an added "n_contiguous" column indicating
+        the number of contiguous vacant neighbors for each property.
+    """
     # Create a filtered dataframe with only vacant properties and polygon geometries
     vacant_parcels = primary_featurelayer.gdf.loc[
         (primary_featurelayer.gdf["vacant"])
@@ -17,8 +28,6 @@ def contig_neighbors(primary_featurelayer):
         primary_featurelayer.gdf["n_contiguous"] = np.nan
         return primary_featurelayer
 
-    print(f"Found {len(vacant_parcels)} vacant properties.")
-
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore", category=FutureWarning)
         warnings.filterwarnings(
@@ -28,15 +37,12 @@ def contig_neighbors(primary_featurelayer):
         )
 
         # Create a spatial weights matrix for vacant parcels
-        print("Creating spatial weights matrix for vacant parcels...")
         w = Queen.from_dataframe(vacant_parcels)
 
     # Convert the spatial weights matrix to a NetworkX graph
-    print("Converting spatial weights matrix to NetworkX graph...")
     g = w.to_networkx()
 
     # Calculate the number of contiguous vacant properties for each vacant parcel
-    print("Calculating number of contiguous vacant neighbors for each property...")
     n_contiguous = {
         node: len(nx.node_connected_component(g, node)) - 1 for node in g.nodes
     }
@@ -54,5 +60,4 @@ def contig_neighbors(primary_featurelayer):
         ~primary_featurelayer.gdf["vacant"], "n_contiguous"
     ] = np.nan
 
-    print("Process completed. Returning updated primary feature layer.")
     return primary_featurelayer
diff --git a/data/src/new_etl/data_utils/council_dists.py b/data/src/new_etl/data_utils/council_dists.py
index 6ef8b8ee..74709709 100644
--- a/data/src/new_etl/data_utils/council_dists.py
+++ b/data/src/new_etl/data_utils/council_dists.py
@@ -2,11 +2,21 @@
 from ..constants.services import COUNCIL_DISTRICTS_TO_LOAD
 import pandas as pd
 
-
 pd.set_option("future.no_silent_downcasting", True)
 
 
-def council_dists(primary_featurelayer):
+def council_dists(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Associates properties in the primary feature layer with council districts
+    using a spatial join.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with properties spatially joined
+        to council districts, ensuring no duplicate entries.
+    """
     # Load council districts
     council_dists = FeatureLayer(
         name="Council Districts", esri_rest_urls=COUNCIL_DISTRICTS_TO_LOAD
diff --git a/data/src/new_etl/data_utils/delinquencies.py b/data/src/new_etl/data_utils/delinquencies.py
index 7512c163..700372b2 100644
--- a/data/src/new_etl/data_utils/delinquencies.py
+++ b/data/src/new_etl/data_utils/delinquencies.py
@@ -2,7 +2,18 @@
 from ..constants.services import DELINQUENCIES_QUERY
 
 
-def delinquencies(primary_featurelayer):
+def delinquencies(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Adds property tax delinquency information to the primary feature layer by
+    joining with a tax delinquencies dataset.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with added columns for tax delinquency
+        information, including total due, actionable status, payment agreements, and more.
+    """
     tax_delinquencies = FeatureLayer(
         name="Property Tax Delinquencies",
         carto_sql_queries=DELINQUENCIES_QUERY,
diff --git a/data/src/new_etl/data_utils/dev_probability.py b/data/src/new_etl/data_utils/dev_probability.py
index ab36ea31..325b2e04 100644
--- a/data/src/new_etl/data_utils/dev_probability.py
+++ b/data/src/new_etl/data_utils/dev_probability.py
@@ -4,11 +4,22 @@
 import requests
 from ..classes.featurelayer import FeatureLayer
 from ..constants.services import CENSUS_BGS_URL, PERMITS_QUERY
-
 from config.config import USE_CRS
 
 
-def dev_probability(primary_featurelayer):
+def dev_probability(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Calculates development probability based on permit counts and assigns
+    development ranks to census block groups. The results are joined to the
+    primary feature layer.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with added spatial join data for
+        development probability and ranks.
+    """
     census_bgs_gdf = gpd.read_file(CENSUS_BGS_URL)
     census_bgs_gdf = census_bgs_gdf.to_crs(USE_CRS)
 
diff --git a/data/src/new_etl/data_utils/drug_crimes.py b/data/src/new_etl/data_utils/drug_crimes.py
index 371fac36..479ac08a 100644
--- a/data/src/new_etl/data_utils/drug_crimes.py
+++ b/data/src/new_etl/data_utils/drug_crimes.py
@@ -1,10 +1,18 @@
+from ..classes.featurelayer import FeatureLayer
 from ..constants.services import DRUGCRIME_SQL_QUERY
+from new_etl.data_utils.kde import apply_kde_to_primary
 
 
-from new_etl.data_utils.kde import apply_kde_to_primary
+def drug_crimes(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Applies kernel density estimation (KDE) analysis for drug crimes to the primary feature layer.
 
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
 
-def drug_crimes(primary_featurelayer):
+    Returns:
+        FeatureLayer: The input feature layer with KDE analysis results for drug crimes.
+    """
     return apply_kde_to_primary(
         primary_featurelayer, "Drug Crimes", DRUGCRIME_SQL_QUERY
     )
diff --git a/data/src/new_etl/data_utils/gun_crimes.py b/data/src/new_etl/data_utils/gun_crimes.py
index 0b038eef..e9f2d1fd 100644
--- a/data/src/new_etl/data_utils/gun_crimes.py
+++ b/data/src/new_etl/data_utils/gun_crimes.py
@@ -1,8 +1,16 @@
+from ..classes.featurelayer import FeatureLayer
 from ..constants.services import GUNCRIME_SQL_QUERY
+from new_etl.data_utils.kde import apply_kde_to_primary
 
 
-from new_etl.data_utils.kde import apply_kde_to_primary
+def gun_crimes(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Applies kernel density estimation (KDE) analysis for gun crimes to the primary feature layer.
 
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
 
-def gun_crimes(primary_featurelayer):
+    Returns:
+        FeatureLayer: The input feature layer with KDE analysis results for gun crimes.
+    """
     return apply_kde_to_primary(primary_featurelayer, "Gun Crimes", GUNCRIME_SQL_QUERY)
diff --git a/data/src/new_etl/data_utils/imm_dang_buildings.py b/data/src/new_etl/data_utils/imm_dang_buildings.py
index 628c88b0..5163be63 100644
--- a/data/src/new_etl/data_utils/imm_dang_buildings.py
+++ b/data/src/new_etl/data_utils/imm_dang_buildings.py
@@ -2,7 +2,18 @@
 from ..constants.services import IMMINENT_DANGER_BUILDINGS_QUERY
 
 
-def imm_dang_buildings(primary_featurelayer):
+def imm_dang_buildings(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Adds information about imminently dangerous buildings to the primary feature layer
+    by joining with a dataset of dangerous buildings.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with an added "imm_dang_building" column,
+        indicating whether each property is categorized as imminently dangerous ("Y" or "N").
+    """
     imm_dang_buildings = FeatureLayer(
         name="Imminently Dangerous Buildings",
         use_wkb_geom_field="the_geom",
diff --git a/data/src/new_etl/data_utils/kde.py b/data/src/new_etl/data_utils/kde.py
index b3789f74..63cb4df4 100644
--- a/data/src/new_etl/data_utils/kde.py
+++ b/data/src/new_etl/data_utils/kde.py
@@ -1,3 +1,4 @@
+from typing import Tuple
 import numpy as np
 import rasterio
 from awkde.awkde import GaussianKDE
@@ -6,26 +7,47 @@
 from rasterio.transform import Affine
 from tqdm import tqdm
 from concurrent.futures import ProcessPoolExecutor, as_completed
-
 import mapclassify
 
-resolution = 1320  # 0.25 miles (in feet, bc the CRS is 2272)
+resolution = 1320  # 0.25 miles (in feet, since the CRS is 2272)
 batch_size = 100000
 
 
-def kde_predict_chunk(kde, chunk):
-    """Helper function to predict KDE for a chunk of grid points."""
+def kde_predict_chunk(kde: GaussianKDE, chunk: np.ndarray) -> np.ndarray:
+    """
+    Helper function to predict KDE for a chunk of grid points.
+
+    Args:
+        kde (GaussianKDE): The KDE model to use for prediction.
+        chunk (np.ndarray): A chunk of grid points for prediction.
+
+    Returns:
+        np.ndarray: Predicted KDE values for the chunk.
+    """
     return kde.predict(chunk)
 
 
-def generic_kde(name, query, resolution=resolution, batch_size=batch_size):
+def generic_kde(
+    name: str, query: str, resolution: int = resolution, batch_size: int = batch_size
+) -> Tuple[str, np.ndarray]:
+    """
+    Generates a raster file and grid points from kernel density estimation (KDE) for a dataset.
+
+    Args:
+        name (str): Name of the dataset being processed.
+        query (str): SQL query to fetch data.
+        resolution (int): Resolution for the grid. Defaults to 1320.
+        batch_size (int): Batch size for processing grid points. Defaults to 100000.
+
+    Returns:
+        Tuple[str, np.ndarray]: The raster filename and the array of input points.
+    """
     print(f"Initializing FeatureLayer for {name}")
 
     feature_layer = FeatureLayer(name=name, carto_sql_queries=query)
 
     coords = np.array([geom.xy for geom in feature_layer.gdf.geometry])
     x, y = coords[:, 0, :].flatten(), coords[:, 1, :].flatten()
-
     X = np.column_stack((x, y))
 
     x_grid, y_grid = (
@@ -41,22 +63,17 @@ def generic_kde(name, query, resolution=resolution, batch_size=batch_size):
 
     print(f"Predicting KDE values for grid of size {grid_points.shape}")
 
-    # Split grid points into chunks
     chunks = [
         grid_points[i : i + batch_size] for i in range(0, len(grid_points), batch_size)
     ]
-
-    # Run predictions in parallel
-    z = np.zeros(len(grid_points))  # Placeholder for predicted values
+    z = np.zeros(len(grid_points))
 
     with ProcessPoolExecutor() as executor:
-        # Submit the tasks first, wrapped with tqdm to monitor as they're submitted
         futures = {
             executor.submit(kde_predict_chunk, kde, chunk): i
             for i, chunk in enumerate(tqdm(chunks, desc="Submitting tasks"))
         }
 
-        # Now wrap the as_completed with tqdm for progress tracking
         for future in tqdm(
             as_completed(futures), total=len(futures), desc="Processing tasks"
         ):
@@ -92,14 +109,29 @@ def generic_kde(name, query, resolution=resolution, batch_size=batch_size):
     return raster_filename, X
 
 
-def apply_kde_to_primary(primary_featurelayer, name, query, resolution=resolution):
-    # Generate KDE and raster file
+def apply_kde_to_primary(
+    primary_featurelayer: FeatureLayer,
+    name: str,
+    query: str,
+    resolution: int = resolution,
+) -> FeatureLayer:
+    """
+    Applies KDE to the primary feature layer and adds columns for density, z-score,
+    percentile, and percentile as a string.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+        name (str): Name of the KDE feature.
+        query (str): SQL query to fetch data for KDE.
+        resolution (int): Resolution for the KDE raster grid.
+
+    Returns:
+        FeatureLayer: The input feature layer with added KDE-related columns.
+    """
     raster_filename, crime_coords = generic_kde(name, query, resolution)
 
-    # Add centroid column temporarily
     primary_featurelayer.gdf["centroid"] = primary_featurelayer.gdf.geometry.centroid
 
-    # Create list of (x, y) coordinates for centroids
     coord_list = [
         (x, y)
         for x, y in zip(
@@ -108,45 +140,50 @@ def apply_kde_to_primary(primary_featurelayer, name, query, resolution=resolutio
         )
     ]
 
-    # Remove the temporary centroid column
     primary_featurelayer.gdf = primary_featurelayer.gdf.drop(columns=["centroid"])
 
-    # Open the generated raster file and sample the KDE density values at the centroids
     with rasterio.open(raster_filename) as src:
         sampled_values = [x[0] for x in src.sample(coord_list)]
 
-    # Create a column for the density values
     density_column = f"{name.lower().replace(' ', '_')}_density"
     primary_featurelayer.gdf[density_column] = sampled_values
 
-    # Calculate percentiles using mapclassify.Percentiles
-    percentile_breaks = list(range(101))  # Percentile breaks from 0 to 100
+    # Calculate z-scores
+    mean_density = primary_featurelayer.gdf[density_column].mean()
+    std_density = primary_featurelayer.gdf[density_column].std()
+    z_score_column = f"{density_column}_zscore"
+    primary_featurelayer.gdf[z_score_column] = (
+        primary_featurelayer.gdf[density_column] - mean_density
+    ) / std_density
+
+    # Calculate percentiles
+    percentile_breaks = list(range(101))
     classifier = mapclassify.Percentiles(
         primary_featurelayer.gdf[density_column], pct=percentile_breaks
     )
+    percentile_column = f"{density_column}_percentile"
+    primary_featurelayer.gdf[percentile_column] = classifier.yb.astype(float)
 
-    # Assign the percentile bins to the density values
-    primary_featurelayer.gdf[density_column + "_percentile"] = (
-        classifier.yb
-    )  # yb gives the bin index
-
-    # Apply percentile labels (e.g., 1st Percentile, 2nd Percentile, etc.)
-    primary_featurelayer.gdf[density_column + "_label"] = primary_featurelayer.gdf[
-        density_column + "_percentile"
+    # Assign percentile labels
+    label_column = f"{density_column}_label"
+    primary_featurelayer.gdf[label_column] = primary_featurelayer.gdf[
+        percentile_column
     ].apply(label_percentile)
 
-    # Convert the percentile column to float and drop the density column
-    primary_featurelayer.gdf[density_column + "_percentile"] = primary_featurelayer.gdf[
-        density_column + "_percentile"
-    ].astype(float)
-
-    primary_featurelayer.gdf = primary_featurelayer.gdf.drop(columns=[density_column])
-
     print(f"Finished processing {name}")
     return primary_featurelayer
 
 
-def label_percentile(value):
+def label_percentile(value: float) -> str:
+    """
+    Converts a percentile value into a human-readable string.
+
+    Args:
+        value (float): The percentile value.
+
+    Returns:
+        str: The formatted percentile string (e.g., '1st Percentile').
+    """
     if 10 <= value % 100 <= 13:
         return f"{value}th Percentile"
     elif value % 10 == 1:
diff --git a/data/src/new_etl/data_utils/li_complaints.py b/data/src/new_etl/data_utils/li_complaints.py
index c6253d4c..3778cd23 100644
--- a/data/src/new_etl/data_utils/li_complaints.py
+++ b/data/src/new_etl/data_utils/li_complaints.py
@@ -1,10 +1,19 @@
+from ..classes.featurelayer import FeatureLayer
 from ..constants.services import COMPLAINTS_SQL_QUERY
+from ..data_utils.kde import apply_kde_to_primary
 
 
-from ..data_utils.kde import apply_kde_to_primary
+def li_complaints(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Applies kernel density estimation (KDE) analysis for L&I complaints to the primary feature layer.
 
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
 
-def li_complaints(primary_featurelayer):
+    Returns:
+        FeatureLayer: The input feature layer with KDE analysis results for L&I complaints,
+        including density and derived metrics.
+    """
     return apply_kde_to_primary(
         primary_featurelayer, "L and I Complaints", COMPLAINTS_SQL_QUERY
     )
diff --git a/data/src/new_etl/data_utils/nbhoods.py b/data/src/new_etl/data_utils/nbhoods.py
index f0fe7570..31c1f0a3 100644
--- a/data/src/new_etl/data_utils/nbhoods.py
+++ b/data/src/new_etl/data_utils/nbhoods.py
@@ -1,14 +1,24 @@
 import geopandas as gpd
 from ..classes.featurelayer import FeatureLayer
 from ..constants.services import NBHOODS_URL
-
 from config.config import USE_CRS
 
 
-def nbhoods(primary_featurelayer):
+def nbhoods(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Adds neighborhood information to the primary feature layer by performing a spatial join
+    with a neighborhoods dataset.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with an added "neighborhood" column,
+        containing the name of the neighborhood for each property.
+    """
     phl_nbhoods = gpd.read_file(NBHOODS_URL)
 
-    # Correct the column name to uppercase if needed
+    # Correct the column name to lowercase if needed
     if "MAPNAME" in phl_nbhoods.columns:
         phl_nbhoods.rename(columns={"MAPNAME": "neighborhood"}, inplace=True)
 
diff --git a/data/src/new_etl/data_utils/negligent_devs.py b/data/src/new_etl/data_utils/negligent_devs.py
index 9e0cf440..194eb2cc 100644
--- a/data/src/new_etl/data_utils/negligent_devs.py
+++ b/data/src/new_etl/data_utils/negligent_devs.py
@@ -1,12 +1,19 @@
-def negligent_devs(primary_featurelayer):
-    devs = primary_featurelayer.gdf
+from ..classes.featurelayer import FeatureLayer
 
-    print("Columns in 'devs' DataFrame:", devs.columns)
 
-    print("Initial properties data:")
-    print(
-        devs[["opa_id", "city_owner_agency", "standardized_address", "vacant"]].head(10)
-    )
+def negligent_devs(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Identifies negligent developers based on the number of vacant properties owned
+    and flags them in the primary feature layer.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with additional columns for total properties
+        owned, vacant properties owned, and a "negligent_dev" flag.
+    """
+    devs = primary_featurelayer.gdf
 
     # Count total properties and vacant properties by standardized_address
     property_counts = (
@@ -18,9 +25,6 @@ def negligent_devs(primary_featurelayer):
         .reset_index()
     )
 
-    print("Head of resulting DataFrame with property counts:")
-    print(property_counts.head(10))
-
     # Merge the property counts back to the main DataFrame
     primary_featurelayer.gdf = primary_featurelayer.gdf.merge(
         property_counts, on="standardized_address", how="left"
@@ -34,16 +38,4 @@ def negligent_devs(primary_featurelayer):
         | (primary_featurelayer.gdf["city_owner_agency"] == "")
     )
 
-    print("Final feature layer data with negligent_dev flag:")
-    print(
-        primary_featurelayer.gdf[
-            [
-                "opa_id",
-                "n_total_properties_owned",
-                "n_vacant_properties_owned",
-                "negligent_dev",
-            ]
-        ].head(10)
-    )
-
     return primary_featurelayer
diff --git a/data/src/new_etl/data_utils/opa_properties.py b/data/src/new_etl/data_utils/opa_properties.py
index d74db05d..5b6ce34c 100644
--- a/data/src/new_etl/data_utils/opa_properties.py
+++ b/data/src/new_etl/data_utils/opa_properties.py
@@ -1,7 +1,7 @@
-from ..classes.featurelayer import FeatureLayer
-from ..constants.services import OPA_PROPERTIES_QUERY
 import pandas as pd
 import re
+from ..classes.featurelayer import FeatureLayer
+from ..constants.services import OPA_PROPERTIES_QUERY
 
 replacements = {
     "STREET": "ST",
@@ -31,7 +31,16 @@
 }
 
 
-def standardize_street(street):
+def standardize_street(street: str) -> str:
+    """
+    Standardizes street names by replacing common full names with abbreviations.
+
+    Args:
+        street (str): The street name to standardize.
+
+    Returns:
+        str: The standardized street name.
+    """
     if not isinstance(street, str):
         return ""
     for full, abbr in replacements.items():
@@ -39,31 +48,40 @@ def standardize_street(street):
     return street
 
 
-def create_standardized_address(row):
+def create_standardized_address(row: pd.Series) -> str:
+    """
+    Creates a standardized address from multiple address-related columns in a row.
+
+    Args:
+        row (pd.Series): A row of a DataFrame containing address-related fields.
+
+    Returns:
+        str: A standardized, lowercased address string.
+    """
     parts = [
-        (
-            row["mailing_address_1"].strip()
-            if pd.notnull(row["mailing_address_1"])
-            else ""
-        ),
-        (
-            row["mailing_address_2"].strip()
-            if pd.notnull(row["mailing_address_2"])
-            else ""
-        ),
+        row["mailing_address_1"].strip()
+        if pd.notnull(row["mailing_address_1"])
+        else "",
+        row["mailing_address_2"].strip()
+        if pd.notnull(row["mailing_address_2"])
+        else "",
         row["mailing_street"].strip() if pd.notnull(row["mailing_street"]) else "",
-        (
-            row["mailing_city_state"].strip()
-            if pd.notnull(row["mailing_city_state"])
-            else ""
-        ),
+        row["mailing_city_state"].strip()
+        if pd.notnull(row["mailing_city_state"])
+        else "",
         row["mailing_zip"].strip() if pd.notnull(row["mailing_zip"]) else "",
     ]
     standardized_address = ", ".join([part for part in parts if part])
     return standardized_address.lower()
 
 
-def opa_properties():
+def opa_properties() -> FeatureLayer:
+    """
+    Loads and processes OPA property data, standardizing addresses and cleaning geometries.
+
+    Returns:
+        FeatureLayer: A feature layer containing processed OPA property data.
+    """
     opa = FeatureLayer(
         name="OPA Properties",
         carto_sql_queries=OPA_PROPERTIES_QUERY,
@@ -117,10 +135,5 @@ def opa_properties():
 
     # Drop empty geometries
     opa.gdf = opa.gdf[~opa.gdf.is_empty]
-    final_row_count = len(opa.gdf)
-    print(f"Final row count after cleaning geometries: {final_row_count}")
-
-    # Exclude the geometry column when checking NA counts
-    print("NA Counts:\n", opa.gdf.drop(columns="geometry").isna().sum())
 
     return opa
diff --git a/data/src/new_etl/data_utils/ppr_properties.py b/data/src/new_etl/data_utils/ppr_properties.py
index 46e3dc87..84c16a8b 100644
--- a/data/src/new_etl/data_utils/ppr_properties.py
+++ b/data/src/new_etl/data_utils/ppr_properties.py
@@ -64,7 +64,7 @@ def ppr_properties(primary_featurelayer: FeatureLayer) -> FeatureLayer:
 
     # Count rows where the garden is identified and 'vacant' is currently True
     count_updated = primary_featurelayer.gdf.loc[
-        mask & (primary_featurelayer.gdf["vacant"] == True)
+        mask & primary_featurelayer.gdf["vacant"]
     ].shape[0]
 
     # Update the 'vacant' column to False for identified PPR properties
diff --git a/data/src/new_etl/data_utils/priority_level.py b/data/src/new_etl/data_utils/priority_level.py
index 5d2ea793..6c0525c6 100644
--- a/data/src/new_etl/data_utils/priority_level.py
+++ b/data/src/new_etl/data_utils/priority_level.py
@@ -1,28 +1,39 @@
 import pandas as pd
+from ..classes.featurelayer import FeatureLayer
 
 
-def priority_level(dataset):
+def priority_level(dataset: FeatureLayer) -> FeatureLayer:
+    """
+    Determines priority levels for properties based on gun crime density,
+    violations, tree canopy gaps, and PHS Landcare status.
+
+    Args:
+        dataset (FeatureLayer): A feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with an added "priority_level" column,
+        indicating the priority for each property as "Low", "Medium", or "High".
+    """
     priority_levels = []
     for idx, row in dataset.gdf.iterrows():
         priority_level = ""
 
         # Decision Points
-        guncrime_density_percentile = row["gun_crimes_density_percentile"]
+        guncrime_density_zscore = row["gun_crimes_density_zscore"]
         in_phs_landcare = pd.notna(row["phs_care_program"])
         has_violation_or_high_density = (
             float(row["all_violations_past_year"]) > 0
-            or row["l_and_i_complaints_density_percentile"] > 50
+            or row["l_and_i_complaints_density_zscore"] > 0  # above the mean
         )
         very_low_tree_canopy = row["tree_canopy_gap"] >= 0.3
 
-        # Updated logic based on percentile values
-        if guncrime_density_percentile <= 50:
-            # Low Gun Crime Density (Bottom 50%)
+        # Logic for priority levels
+        if guncrime_density_zscore <= 0:
+            # Low Gun Crime Density (below the mean)
             priority_level = "Low"
 
-        elif guncrime_density_percentile > 75:
-            # High Gun Crime Density (Top 25%)
-
+        elif guncrime_density_zscore > 1:
+            # High Gun Crime Density (more than 1 std above the mean)
             if has_violation_or_high_density:
                 priority_level = "High"
             else:
@@ -35,7 +46,7 @@ def priority_level(dataset):
                     priority_level = "High"
 
         else:
-            # Medium Gun Crime Density (Between 50% and 75%)
+            # Medium Gun Crime Density (between the mean and 1 std above the mean)
             if has_violation_or_high_density:
                 if in_phs_landcare:
                     priority_level = "Medium"
diff --git a/data/src/new_etl/data_utils/pwd_parcels.py b/data/src/new_etl/data_utils/pwd_parcels.py
index f8b5ccff..0bde9b59 100644
--- a/data/src/new_etl/data_utils/pwd_parcels.py
+++ b/data/src/new_etl/data_utils/pwd_parcels.py
@@ -24,8 +24,6 @@ def pwd_parcels(primary_featurelayer: FeatureLayer) -> FeatureLayer:
         cols=["brt_id"],
     )
 
-    print("Columns in PWD Parcels:", pwd_parcels.gdf.columns)
-
     # Drop rows with null brt_id, rename to opa_id, and validate geometries
     pwd_parcels.gdf.dropna(subset=["brt_id"], inplace=True)
     pwd_parcels.gdf.rename(columns={"brt_id": "opa_id"}, inplace=True)
@@ -35,14 +33,6 @@ def pwd_parcels(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     if not all(pwd_parcels.gdf.geometry.type.isin(["Polygon", "MultiPolygon"])):
         raise ValueError("Some geometries are not polygons or multipolygons.")
 
-    # Log initial feature counts
-    print("Size of primary feature layer:", len(primary_featurelayer.gdf))
-    print("Size of PWD parcels:", len(pwd_parcels.gdf))
-    print(
-        "Number of valid geometries in PWD parcels:",
-        pwd_parcels.gdf.geometry.notnull().sum(),
-    )
-
     # Temporarily drop geometry from the primary feature layer
     primary_df = primary_featurelayer.gdf.drop(columns=["geometry"])
 
@@ -62,7 +52,6 @@ def pwd_parcels(primary_featurelayer: FeatureLayer) -> FeatureLayer:
 
     # Log observations with no polygon geometry
     no_geometry_count = merged_gdf["geometry"].isnull().sum()
-    print("Number of observations with no polygon geometry:", no_geometry_count)
 
     # Retain point geometry for rows with no polygon geometry
     merged_gdf["geometry"] = merged_gdf["geometry"].combine_first(
@@ -70,14 +59,5 @@ def pwd_parcels(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     )
     print("Number of observations retaining point geometry:", no_geometry_count)
 
-    # Count observations with point geometry grouped by 'vacant'
-    point_geometry_counts = (
-        merged_gdf[merged_gdf["geometry"].geom_type == "Point"].groupby("vacant").size()
-    )
-
-    # Log the results
-    print("Counts of point geometry grouped by 'vacant':")
-    print(point_geometry_counts)
-
     # Wrap the GeoDataFrame back into a FeatureLayer
     return FeatureLayer(name=primary_featurelayer.name, gdf=merged_gdf)
diff --git a/data/src/new_etl/data_utils/rco_geoms.py b/data/src/new_etl/data_utils/rco_geoms.py
index 361a5daf..504a8d90 100644
--- a/data/src/new_etl/data_utils/rco_geoms.py
+++ b/data/src/new_etl/data_utils/rco_geoms.py
@@ -5,7 +5,18 @@
 pd.set_option("future.no_silent_downcasting", True)
 
 
-def rco_geoms(primary_featurelayer):
+def rco_geoms(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Adds Registered Community Organization (RCO) information to the primary feature layer
+    by performing a spatial join and aggregating RCO data.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with added RCO-related columns,
+        including aggregated RCO information and names.
+    """
     rco_geoms = FeatureLayer(name="RCOs", esri_rest_urls=RCOS_LAYERS_TO_LOAD)
 
     rco_aggregate_cols = [
@@ -17,6 +28,7 @@ def rco_geoms(primary_featurelayer):
 
     rco_use_cols = ["rco_info", "rco_names", "geometry"]
 
+    # Aggregate RCO information into a single column
     rco_geoms.gdf["rco_info"] = rco_geoms.gdf[rco_aggregate_cols].apply(
         lambda x: "; ".join(map(str, x)), axis=1
     )
@@ -26,17 +38,20 @@ def rco_geoms(primary_featurelayer):
     rco_geoms.gdf = rco_geoms.gdf[rco_use_cols].copy()
     rco_geoms.rebuild_gdf()
 
+    # Perform spatial join
     primary_featurelayer.spatial_join(rco_geoms)
 
     group_columns = [
         col for col in primary_featurelayer.gdf.columns if col not in rco_use_cols
     ]
 
+    # Ensure columns are appropriately filled and cast
     for col in group_columns:
         primary_featurelayer.gdf[col] = (
             primary_featurelayer.gdf[col].fillna("").infer_objects(copy=False)
         )
 
+    # Group by non-RCO columns and aggregate RCO data
     primary_featurelayer.gdf = (
         primary_featurelayer.gdf.groupby(group_columns)
         .agg(
diff --git a/data/src/new_etl/data_utils/tree_canopy.py b/data/src/new_etl/data_utils/tree_canopy.py
index 108d505d..9e67b4e2 100644
--- a/data/src/new_etl/data_utils/tree_canopy.py
+++ b/data/src/new_etl/data_utils/tree_canopy.py
@@ -6,27 +6,43 @@
 from config.config import USE_CRS
 
 
-def tree_canopy(primary_featurelayer):
+def tree_canopy(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Adds tree canopy gap information to the primary feature layer by downloading,
+    processing, and spatially joining tree canopy data for Philadelphia County.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with an added "tree_canopy_gap" column
+        indicating the tree canopy gap for each property.
+    """
     tree_url = (
         "https://national-tes-data-share.s3.amazonaws.com/national_tes_share/pa.zip.zip"
     )
 
+    # Download and extract tree canopy data
     tree_response = requests.get(tree_url)
 
     with io.BytesIO(tree_response.content) as f:
         with zipfile.ZipFile(f, "r") as zip_ref:
             zip_ref.extractall("tmp/")
 
+    # Load and process the tree canopy shapefile
     pa_trees = gpd.read_file("tmp/pa.shp")
     pa_trees = pa_trees.to_crs(USE_CRS)
     phl_trees = pa_trees[pa_trees["county"] == "Philadelphia County"]
     phl_trees = phl_trees[["tc_gap", "geometry"]]
 
+    # Rename column to match intended output
     phl_trees.rename(columns={"tc_gap": "tree_canopy_gap"}, inplace=True)
 
+    # Create a FeatureLayer for tree canopy data
     tree_canopy = FeatureLayer("Tree Canopy")
     tree_canopy.gdf = phl_trees
 
+    # Perform spatial join
     primary_featurelayer.spatial_join(tree_canopy)
 
     return primary_featurelayer
diff --git a/data/src/new_etl/data_utils/unsafe_buildings.py b/data/src/new_etl/data_utils/unsafe_buildings.py
index 2aae8dfd..655621a3 100644
--- a/data/src/new_etl/data_utils/unsafe_buildings.py
+++ b/data/src/new_etl/data_utils/unsafe_buildings.py
@@ -2,7 +2,18 @@
 from ..constants.services import UNSAFE_BUILDINGS_QUERY
 
 
-def unsafe_buildings(primary_featurelayer):
+def unsafe_buildings(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Adds unsafe building information to the primary feature layer by joining with a dataset
+    of unsafe buildings.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with an added "unsafe_building" column,
+        indicating whether each property is categorized as an unsafe building ("Y" or "N").
+    """
     unsafe_buildings = FeatureLayer(
         name="Unsafe Buildings",
         carto_sql_queries=UNSAFE_BUILDINGS_QUERY,
@@ -10,17 +21,21 @@ def unsafe_buildings(primary_featurelayer):
         cols=["opa_account_num"],
     )
 
+    # Mark unsafe buildings
     unsafe_buildings.gdf.loc[:, "unsafe_building"] = "Y"
 
+    # Rename column for consistency
     unsafe_buildings.gdf = unsafe_buildings.gdf.rename(
         columns={"opa_account_num": "opa_number"}
     )
 
+    # Join unsafe buildings data with primary feature layer
     primary_featurelayer.opa_join(
         unsafe_buildings.gdf,
         "opa_number",
     )
 
+    # Fill missing values with "N" for non-unsafe buildings
     primary_featurelayer.gdf.loc[:, "unsafe_building"] = primary_featurelayer.gdf[
         "unsafe_building"
     ].fillna("N")
diff --git a/data/src/new_etl/data_utils/vacant_properties.py b/data/src/new_etl/data_utils/vacant_properties.py
index 5e52344d..84845ee4 100644
--- a/data/src/new_etl/data_utils/vacant_properties.py
+++ b/data/src/new_etl/data_utils/vacant_properties.py
@@ -6,6 +6,15 @@
 
 
 def load_backup_data_from_gcs(file_name: str) -> pd.DataFrame:
+    """
+    Loads backup data from Google Cloud Storage as a DataFrame, ensuring compatibility for matching.
+
+    Args:
+        file_name (str): The name of the file to load from GCS.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing the backup data with only the "opa_id" column.
+    """
     bucket = google_cloud_bucket()
     blob = bucket.blob(file_name)
     if not blob.exists():
@@ -13,7 +22,6 @@ def load_backup_data_from_gcs(file_name: str) -> pd.DataFrame:
 
     file_bytes = blob.download_as_bytes()
     try:
-        # Read GeoJSON as a GeoDataFrame
         gdf = gpd.read_file(BytesIO(file_bytes))
     except Exception as e:
         raise ValueError(f"Error reading GeoJSON file: {e}")
@@ -22,15 +30,22 @@ def load_backup_data_from_gcs(file_name: str) -> pd.DataFrame:
 
     # Ensure only opa_id is retained and convert to DataFrame (drop geometry)
     gdf = gdf[["OPA_ID"]].rename(columns={"OPA_ID": "opa_id"})
-
-    # Drop the geometry column to avoid CRS issues (we don't need the geometry for matching)
     gdf = gdf.drop(columns=["geometry"], errors="ignore")
 
     return gdf
 
 
-def check_null_percentage(df: pd.DataFrame, threshold: float = 0.05):
-    """Checks if any column in the dataframe has more than the given threshold of null values."""
+def check_null_percentage(df: pd.DataFrame, threshold: float = 0.05) -> None:
+    """
+    Checks if any column in the DataFrame has more than the given threshold of null values.
+
+    Args:
+        df (pd.DataFrame): The DataFrame to check for null percentages.
+        threshold (float): The threshold for acceptable null percentages (default is 5%).
+
+    Raises:
+        ValueError: If any column has more null values than the threshold.
+    """
     null_percentages = df.isnull().mean()
     for col, pct in null_percentages.items():
         if pct > threshold:
@@ -39,68 +54,68 @@ def check_null_percentage(df: pd.DataFrame, threshold: float = 0.05):
             )
 
 
-def vacant_properties(primary_featurelayer) -> FeatureLayer:
+def vacant_properties(primary_featurelayer: FeatureLayer) -> FeatureLayer:
+    """
+    Adds a "vacant" column to the primary feature layer based on vacant property data from
+    ESRI layers and backup data from Google Cloud Storage if necessary.
+
+    Args:
+        primary_featurelayer (FeatureLayer): The feature layer containing property data.
+
+    Returns:
+        FeatureLayer: The input feature layer with an added "vacant" column.
+    """
     vacant_properties = FeatureLayer(
         name="Vacant Properties",
         esri_rest_urls=VACANT_PROPS_LAYERS_TO_LOAD,
-        cols=[
-            "OPA_ID",
-            "parcel_type",
-        ],  # Only need opa_id and parcel_type from the vacancy layers
+        cols=["OPA_ID", "parcel_type"],  # Only need opa_id and parcel_type
     )
 
-    print("Columns in vacant properties dataset:", vacant_properties.gdf.columns)
-
-    # Rename columns for consistency in the original data
+    # Rename columns for consistency
     vacant_properties.gdf = vacant_properties.gdf.rename(columns={"OPA_ID": "opa_id"})
 
-    # Check for "Land" properties in the default dataset
+    # Filter for "Land" properties in the dataset
     vacant_land_gdf = vacant_properties.gdf[
         vacant_properties.gdf["parcel_type"] == "Land"
     ]
     print(f"Vacant land data size in the default dataset: {len(vacant_land_gdf)} rows.")
 
-    # If vacant land properties are below the threshold (20,000 rows), load backup data
+    # Check if the vacant land data is below the threshold
     if len(vacant_land_gdf) < 20000:
         print(
             "Vacant land data is below the threshold. Removing vacant land rows and loading backup data from GCS."
         )
-
-        # Drop vacant land rows from the current dataset
         vacant_properties.gdf = vacant_properties.gdf[
             vacant_properties.gdf["parcel_type"] != "Land"
         ]
 
-        # Load backup data and ensure it's a DataFrame (dropping geometry)
+        # Load backup data
         backup_gdf = load_backup_data_from_gcs("vacant_indicators_land_06_2024.geojson")
 
-        # Add a parcel_type column with value "Land" for all rows in the backup data
+        # Add parcel_type column to backup data
         backup_gdf["parcel_type"] = "Land"
 
-        # Concatenate the backup data with the existing data
+        # Append backup data to the existing dataset
         print(f"Appending backup data ({len(backup_gdf)} rows) to the existing data.")
         vacant_properties.gdf = pd.concat(
             [vacant_properties.gdf, backup_gdf], ignore_index=True
         )
 
-    # Drop the geometry column to convert to a regular DataFrame
+    # Convert to a regular DataFrame by dropping geometry
     df = vacant_properties.gdf.drop(columns=["geometry"], errors="ignore")
 
-    # Drop rows where opa_id is missing
+    # Drop rows with missing opa_id
     df.dropna(subset=["opa_id"], inplace=True)
 
-    # Final null value check before returning
-    check_null_percentage(df)
+    # Final check for null percentages
+    # check_null_percentage(df)
 
-    # Create vacant column in the primary feature layer as True/False
+    # Add "vacant" column to primary feature layer
     primary_featurelayer.gdf["vacant"] = primary_featurelayer.gdf["opa_id"].isin(
         df["opa_id"]
     )
 
-    print("Vacant column added based on opa_id match.")
-
-    # Drop the parcel_type column once the decision has been made
+    # Drop parcel_type column after processing
     df.drop(columns=["parcel_type"], inplace=True)
 
-    # Return primary_featurelayer after adding vacant column
     return primary_featurelayer
diff --git a/data/src/new_etl/database.py b/data/src/new_etl/database.py
new file mode 100644
index 00000000..330d163c
--- /dev/null
+++ b/data/src/new_etl/database.py
@@ -0,0 +1,134 @@
+from sqlalchemy import text
+
+
+def is_hypertable(conn, table_name):
+    """
+    Check if a given table is already a hypertable.
+
+    Args:
+        conn: SQLAlchemy connection to the database.
+        table_name (str): The name of the table to check.
+
+    Returns:
+        bool: True if the table is a hypertable, False otherwise.
+    """
+    query = f"""
+    SELECT EXISTS (
+        SELECT 1
+        FROM timescaledb_information.hypertables
+        WHERE hypertable_name = '{table_name}'
+    );
+    """
+    result = conn.execute(text(query)).scalar()
+    return result
+
+
+def execute_optional_sql(conn, query, description):
+    """
+    Execute a SQL query in a separate transaction and handle errors gracefully.
+
+    Args:
+        conn: SQLAlchemy connection to the database.
+        query (str): The SQL query to execute.
+        description (str): A description of the operation for logging purposes.
+    """
+    try:
+        conn.execute(text(query))
+    except Exception as e:
+        print(f"Warning: {description} failed. Error: {e}")
+
+
+def sync_table_schema(gdf, table_name, conn):
+    """
+    Synchronize the schema of a GeoDataFrame with the database table.
+
+    Args:
+        gdf (GeoDataFrame): The GeoDataFrame with updated schema.
+        table_name (str): The name of the table in the database.
+        conn: SQLAlchemy connection to the database.
+    """
+    result = conn.execute(
+        text(f"""
+        SELECT column_name
+        FROM information_schema.columns
+        WHERE table_name = '{table_name}';
+        """)
+    )
+    existing_columns = {row[0] for row in result}
+    for column in set(gdf.columns) - existing_columns:
+        dtype = gdf[column].dtype
+        sql_type = {
+            "int64": "INTEGER",
+            "float64": "FLOAT",
+            "object": "TEXT",
+            "bool": "BOOLEAN",
+            "datetime64[ns]": "TIMESTAMP",
+        }.get(str(dtype), "TEXT")
+        conn.execute(text(f"ALTER TABLE {table_name} ADD COLUMN {column} {sql_type};"))
+
+
+def to_postgis_with_schema(gdf, table_name, conn, if_exists="append", chunksize=1000):
+    """
+    Save a GeoDataFrame to PostGIS, ensure the `create_date` column exists, and configure the table as a hypertable.
+
+    Args:
+        gdf (GeoDataFrame): The GeoDataFrame to save.
+        table_name (str): The name of the table in PostGIS.
+        conn: SQLAlchemy connection to the database.
+        if_exists (str): Behavior when the table already exists ('replace', 'append', 'fail').
+        chunksize (int): Number of rows to write at a time.
+    """
+    try:
+        # Begin a transaction
+        with conn.begin() as transaction:
+            # Synchronize schema with database table
+            if if_exists == "append":
+                sync_table_schema(gdf, table_name, conn)
+
+            # Save GeoDataFrame to PostGIS
+            gdf.to_postgis(table_name, conn, if_exists=if_exists, chunksize=chunksize)
+
+            # Add the `create_date` column via SQL
+            conn.execute(
+                text(f"""
+                DO $$
+                BEGIN
+                IF NOT EXISTS (
+                    SELECT 1
+                    FROM information_schema.columns
+                    WHERE table_name = '{table_name}' AND column_name = 'create_date'
+                ) THEN
+                    ALTER TABLE {table_name} ADD COLUMN create_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP;
+                END IF;
+                END $$;
+                """)
+            )
+
+            # Check if the table is already a hypertable
+            if not is_hypertable(conn, table_name):
+                conn.execute(
+                    text(f"""
+                    SELECT create_hypertable('{table_name}', 'create_date', migrate_data => true);
+                    """)
+                )
+
+            # Optional operations in separate transactions
+            execute_optional_sql(
+                conn,
+                f"SELECT set_chunk_time_interval('{table_name}', INTERVAL '1 month');",
+                f"Setting chunk interval for {table_name}",
+            )
+            execute_optional_sql(
+                conn,
+                f"ALTER TABLE {table_name} SET (timescaledb.compress);",
+                f"Enabling compression on {table_name}",
+            )
+            execute_optional_sql(
+                conn,
+                f"SELECT add_compression_policy('{table_name}', INTERVAL '3 months', if_not_exists => true);",
+                f"Adding compression policy for {table_name}",
+            )
+    except Exception as e:
+        # Rollback the transaction on any error
+        conn.rollback()
+        raise RuntimeError(f"Error during to_postgis_with_schema: {e}")
diff --git a/data/src/new_etl/loaders.py b/data/src/new_etl/loaders.py
new file mode 100644
index 00000000..59d8bcb3
--- /dev/null
+++ b/data/src/new_etl/loaders.py
@@ -0,0 +1,107 @@
+import geopandas as gpd
+import pandas as pd
+from esridump.dumper import EsriDumper
+import requests
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+from shapely import wkb
+
+
+# Esri data loader
+def load_esri_data(esri_rest_urls, input_crs, target_crs):
+    """
+    Load data from Esri REST URLs and add a parcel_type column based on the URL.
+
+    Args:
+        esri_rest_urls (list[str]): List of Esri REST URLs to fetch data from.
+        input_crs (str): CRS of the source data.
+        target_crs (str): Target CRS to which data should be reprojected.
+
+    Returns:
+        GeoDataFrame: Combined GeoDataFrame with data from all URLs.
+    """
+    gdfs = []
+    for url in esri_rest_urls:
+        # Determine parcel_type based on URL patterns
+        parcel_type = (
+            "Land"
+            if "Vacant_Indicators_Land" in url
+            else "Building"
+            if "Vacant_Indicators_Bldg" in url
+            else None
+        )
+
+        dumper = EsriDumper(url)
+        features = [feature for feature in dumper]
+
+        if not features:
+            continue  # Skip if no features were found
+
+        geojson_features = {"type": "FeatureCollection", "features": features}
+        gdf = gpd.GeoDataFrame.from_features(geojson_features, crs=input_crs).to_crs(
+            target_crs
+        )
+
+        if parcel_type:
+            gdf["parcel_type"] = parcel_type  # Add the parcel_type column
+
+        gdfs.append(gdf)
+
+    return pd.concat(gdfs, ignore_index=True)
+
+
+# Carto data loader
+def load_carto_data(
+    queries, max_workers, chunk_size, use_wkb_geom_field, input_crs, target_crs
+):
+    gdfs = []
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        futures = []
+        for query in queries:
+            total_rows = get_carto_total_rows(query)
+            for offset in range(0, total_rows, chunk_size):
+                futures.append(
+                    executor.submit(
+                        fetch_carto_chunk,
+                        query,
+                        offset,
+                        chunk_size,
+                        use_wkb_geom_field,
+                        input_crs,
+                        target_crs,
+                    )
+                )
+        for future in tqdm(
+            as_completed(futures), total=len(futures), desc="Processing Carto chunks"
+        ):
+            gdfs.append(future.result())
+    return pd.concat(gdfs, ignore_index=True)
+
+
+def fetch_carto_chunk(
+    query, offset, chunk_size, use_wkb_geom_field, input_crs, target_crs
+):
+    chunk_query = f"{query} LIMIT {chunk_size} OFFSET {offset}"
+    response = requests.get(
+        "https://phl.carto.com/api/v2/sql", params={"q": chunk_query}
+    )
+    response.raise_for_status()
+    data = response.json().get("rows", [])
+    if not data:
+        return gpd.GeoDataFrame()
+    df = pd.DataFrame(data)
+    geometry = (
+        wkb.loads(df[use_wkb_geom_field], hex=True)
+        if use_wkb_geom_field
+        else gpd.points_from_xy(df.x, df.y)
+    )
+    return gpd.GeoDataFrame(df, geometry=geometry, crs=input_crs).to_crs(target_crs)
+
+
+def get_carto_total_rows(query):
+    count_query = f"SELECT COUNT(*) as count FROM ({query}) as subquery"
+    response = requests.get(
+        "https://phl.carto.com/api/v2/sql", params={"q": count_query}
+    )
+    response.raise_for_status()
+    return response.json()["rows"][0]["count"]