diff --git a/.gitignore b/.gitignore
index 4e250002..1d8ab3c2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -57,10 +57,14 @@ cleanandgreenphilly_gcloud_bucket/
 # personal service account key for GCP
 data/src/app/service-account-key.json
 
+# python versions
+.python-version
+
 # compiled python files
 *.pyc
 
-tmp/
-
 # Local python development files
 .python-version
+
+# Cached and temporary data files from pipeline
+storage/
diff --git a/data/Dockerfile b/data/Dockerfile
index b6676ac9..02e3f637 100644
--- a/data/Dockerfile
+++ b/data/Dockerfile
@@ -49,4 +49,4 @@ COPY src/ .
 
 # Use Pipenv to run the script
 # Adjust the path to your main Python script if needed
-CMD ["pipenv", "run", "python", "./script.py"]
+CMD ["pipenv", "run", "python", "./main.py"]
diff --git a/data/Dockerfile-pg b/data/Dockerfile-pg
deleted file mode 100644
index 504fa690..00000000
--- a/data/Dockerfile-pg
+++ /dev/null
@@ -1,26 +0,0 @@
-#
-# NOTE: THIS DOCKERFILE IS GENERATED VIA "make update"! PLEASE DO NOT EDIT IT DIRECTLY.
-#
-
-FROM postgres:16-bullseye
-
-LABEL maintainer="PostGIS Project - https://postgis.net" \
-      org.opencontainers.image.description="PostGIS 3.5.2+dfsg-1.pgdg110+1 spatial database extension with PostgreSQL 16 bullseye" \
-      org.opencontainers.image.source="https://github.com/postgis/docker-postgis"
-
-ENV POSTGIS_MAJOR 3
-ENV POSTGIS_VERSION 3.5.2+dfsg-1.pgdg110+1
-
-RUN apt-get update \
-      && apt-cache showpkg postgresql-$PG_MAJOR-postgis-$POSTGIS_MAJOR \
-      && apt-get install -y --no-install-recommends \
-           # ca-certificates: for accessing remote raster files;
-           #   fix: https://github.com/postgis/docker-postgis/issues/307
-           ca-certificates \
-           \
-           postgresql-$PG_MAJOR-postgis-$POSTGIS_MAJOR=$POSTGIS_VERSION \
-           postgresql-$PG_MAJOR-postgis-$POSTGIS_MAJOR-scripts \
-      && rm -rf /var/lib/apt/lists/*
-
-RUN mkdir -p /docker-entrypoint-initdb.d
-
diff --git a/data/Dockerfile-timescale b/data/Dockerfile-timescale
deleted file mode 100644
index 06ab9f39..00000000
--- a/data/Dockerfile-timescale
+++ /dev/null
@@ -1,26 +0,0 @@
-FROM postgis/postgis:16-3.5
-
-ENV POSTGIS_MAJOR=3
-ENV POSTGIS_VERSION=3.5.2+dfsg-1.pgdg110+1
-ENV TIMESCALE_MAJOR=2
-ENV TIMESCALE_MINOR=19
-
-RUN apt-get update \
-      && apt-get install -y --no-install-recommends lsb-release curl gnupg apt-transport-https wget \
-      # Add timescale repository and key
-      && echo "deb https://packagecloud.io/timescale/timescaledb/debian/ $(lsb_release -c -s) main" \
-           | tee /etc/apt/sources.list.d/timescaledb.list \
-      && wget --quiet -O - https://packagecloud.io/timescale/timescaledb/gpgkey \
-           | gpg --dearmor -o /etc/apt/trusted.gpg.d/timescaledb.gpg \
-      # Install timescaledb
-      && apt-get update \
-      && apt-get install -y --no-install-recommends timescaledb-$TIMESCALE_MAJOR-postgresql-$PG_MAJOR="$TIMESCALE_MAJOR.$TIMESCALE_MINOR*" postgresql-client-$PG_MAJOR \
-      # Remove temporary files
-      && rm -rf /var/lib/apt/lists/* \
-      && rm -f /etc/apt/trusted.gpg.d/timescaledb.gpg
-
-
-RUN mkdir -p /docker-entrypoint-initdb.d
-
-# Set up TimescaleDB extension during database initialization
-RUN echo "shared_preload_libraries='timescaledb'" >> /usr/share/postgresql/postgresql.conf.sample
diff --git a/data/docker-compose.yml b/data/docker-compose.yml
index e3707af2..16f56be9 100644
--- a/data/docker-compose.yml
+++ b/data/docker-compose.yml
@@ -54,44 +54,3 @@ services:
     extra_hosts:
       - host.docker.internal:host-gateway
     network_mode: 'host'
-
-  postgres:
-    container_name: cagp-postgres
-    build:
-      context: .
-      dockerfile: Dockerfile-pg
-    environment:
-      PGPORT: 5433
-      POSTGRES_PASSWORD:
-    restart: always
-    ports:
-      - '5433:5433'
-    volumes:
-      - database_volume:/var/lib/postgresql/data
-      - ./init_pg.sql:/docker-entrypoint-initdb.d/init_pg.sql
-      - /etc/timezone:/etc/timezone:ro
-      - /etc/localtime:/etc/localtime:ro
-    extra_hosts:
-      - host.docker.internal:host-gateway
-
-  postgres-timescale:
-    container_name: cagp-postgres-timescale
-    build:
-      context: .
-      dockerfile: Dockerfile-timescale
-    environment:
-      PGPORT: 5434
-      POSTGRES_PASSWORD:
-    restart: always
-    ports:
-      - '5434:5434'
-    volumes:
-      - timescale_database_volume:/var/lib/postgresql/data
-      - ./init_pg_timescale.sql:/docker-entrypoint-initdb.d/init_pg.sql
-      - /etc/timezone:/etc/timezone:ro
-      - /etc/localtime:/etc/localtime:ro
-    extra_hosts:
-      - host.docker.internal:host-gateway
-volumes:
-  database_volume:
-  timescale_database_volume:
diff --git a/data/init_pg.sql b/data/init_pg.sql
deleted file mode 100644
index f5ebc6a9..00000000
--- a/data/init_pg.sql
+++ /dev/null
@@ -1,3 +0,0 @@
-CREATE DATABASE vacantlotdb;
-\c vacantlotdb;
-CREATE EXTENSION postgis;
\ No newline at end of file
diff --git a/data/init_pg_timescale.sql b/data/init_pg_timescale.sql
deleted file mode 100644
index ab66f3af..00000000
--- a/data/init_pg_timescale.sql
+++ /dev/null
@@ -1,5 +0,0 @@
-CREATE DATABASE vacantlotdb;
-\c vacantlotdb;
-CREATE EXTENSION postgis;
-CREATE EXTENSION pg_stat_statements;
-CREATE EXTENSION timescaledb;
diff --git a/data/src/Pipfile.lock b/data/src/Pipfile.lock
index f5ec22ce..0f91ae03 100644
--- a/data/src/Pipfile.lock
+++ b/data/src/Pipfile.lock
@@ -1,2930 +1,2926 @@
 {
-    "_meta": {
-        "hash": {
-            "sha256": "217faddf8bc430f5097c264f4fe94fe247389fab1a6f53033490854d99dd9fa4"
-        },
-        "pipfile-spec": 6,
-        "requires": {
-            "python_full_version": "3.11.4",
-            "python_version": "3.11"
-        },
-        "sources": [
-            {
-                "name": "pypi",
-                "url": "https://pypi.org/simple",
-                "verify_ssl": true
-            }
-        ]
-    },
-    "default": {
-        "affine": {
-            "hashes": [
-                "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92",
-                "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea"
-            ],
-            "markers": "python_version >= '3.7'",
-            "version": "==2.4.0"
-        },
-        "annotated-types": {
-            "hashes": [
-                "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53",
-                "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==0.7.0"
-        },
-        "anyio": {
-            "hashes": [
-                "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028",
-                "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==4.9.0"
-        },
-        "appnope": {
-            "hashes": [
-                "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee",
-                "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"
-            ],
-            "markers": "platform_system == 'Darwin'",
-            "version": "==0.1.4"
-        },
-        "argon2-cffi": {
-            "hashes": [
-                "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08",
-                "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"
-            ],
-            "markers": "python_version >= '3.7'",
-            "version": "==23.1.0"
-        },
-        "argon2-cffi-bindings": {
-            "hashes": [
-                "sha256:20ef543a89dee4db46a1a6e206cd015360e5a75822f76df533845c3cbaf72670",
-                "sha256:2c3e3cc67fdb7d82c4718f19b4e7a87123caf8a93fde7e23cf66ac0337d3cb3f",
-                "sha256:3b9ef65804859d335dc6b31582cad2c5166f0c3e7975f324d9ffaa34ee7e6583",
-                "sha256:3e385d1c39c520c08b53d63300c3ecc28622f076f4c2b0e6d7e796e9f6502194",
-                "sha256:58ed19212051f49a523abb1dbe954337dc82d947fb6e5a0da60f7c8471a8476c",
-                "sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a",
-                "sha256:603ca0aba86b1349b147cab91ae970c63118a0f30444d4bc80355937c950c082",
-                "sha256:6a22ad9800121b71099d0fb0a65323810a15f2e292f2ba450810a7316e128ee5",
-                "sha256:8cd69c07dd875537a824deec19f978e0f2078fdda07fd5c42ac29668dda5f40f",
-                "sha256:93f9bf70084f97245ba10ee36575f0c3f1e7d7724d67d8e5b08e61787c320ed7",
-                "sha256:9524464572e12979364b7d600abf96181d3541da11e23ddf565a32e70bd4dc0d",
-                "sha256:b2ef1c30440dbbcba7a5dc3e319408b59676e2e039e2ae11a8775ecf482b192f",
-                "sha256:b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae",
-                "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3",
-                "sha256:bd46088725ef7f58b5a1ef7ca06647ebaf0eb4baff7d1d0d177c6cc8744abd86",
-                "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367",
-                "sha256:d4966ef5848d820776f5f562a7d45fdd70c2f330c961d0d745b784034bd9f48d",
-                "sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93",
-                "sha256:ed2937d286e2ad0cc79a7087d3c272832865f779430e0cc2b4f3718d3159b0cb",
-                "sha256:f1152ac548bd5b8bcecfb0b0371f082037e47128653df2e8ba6e914d384f3c3e",
-                "sha256:f9f8b450ed0547e3d473fdc8612083fd08dd2120d6ac8f73828df9b7d45bb351"
-            ],
-            "markers": "python_version >= '3.6'",
-            "version": "==21.2.0"
-        },
-        "arrow": {
-            "hashes": [
-                "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80",
-                "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==1.3.0"
-        },
-        "asttokens": {
-            "hashes": [
-                "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7",
-                "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==3.0.0"
-        },
-        "async-lru": {
-            "hashes": [
-                "sha256:481d52ccdd27275f42c43a928b4a50c3bfb2d67af4e78b170e3e0bb39c66e5bb",
-                "sha256:ab95404d8d2605310d345932697371a5f40def0487c03d6d0ad9138de52c9943"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==2.0.5"
-        },
-        "attrs": {
-            "hashes": [
-                "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3",
-                "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==25.3.0"
-        },
-        "awkde": {
-            "git": "https://github.com/mennthor/awkde.git",
-            "ref": "5b601fe4d92229d5deb8737fd121dce193bac552"
-        },
-        "babel": {
-            "hashes": [
-                "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d",
-                "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==2.17.0"
-        },
-        "beautifulsoup4": {
-            "hashes": [
-                "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b",
-                "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195"
-            ],
-            "markers": "python_full_version >= '3.7.0'",
-            "version": "==4.13.4"
-        },
-        "bleach": {
-            "extras": [
-                "css"
-            ],
-            "hashes": [
-                "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e",
-                "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==6.2.0"
-        },
-        "boto3": {
-            "hashes": [
-                "sha256:53c8d44b231251fa9421dd13d968236d59fe2cf0421e077afedbf3821653fb3b",
-                "sha256:ef3237b169cd906a44a32c03b3229833d923c9e9733355b329ded2151f91ec0b"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==1.38.2"
-        },
-        "botocore": {
-            "hashes": [
-                "sha256:5d9cffedb1c759a058b43793d16647ed44ec87072f98a1bd6cd673ac0ae6b81d",
-                "sha256:b688a9bd17211a1eaae3a6c965ba9f3973e5435efaaa4fa201f499d3467830e1"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==1.38.2"
-        },
-        "cachecontrol": {
-            "hashes": [
-                "sha256:7d47d19f866409b98ff6025b6a0fca8e4c791fb31abbd95f622093894ce903a2",
-                "sha256:ebad2091bf12d0d200dfc2464330db638c5deb41d546f6d7aca079e87290f3b0"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==0.14.2"
-        },
-        "cachetools": {
-            "hashes": [
-                "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4",
-                "sha256:d26a22bcc62eb95c3beabd9f1ee5e820d3d2704fe2967cbe350e20c8ffcd3f0a"
-            ],
-            "markers": "python_version >= '3.7'",
-            "version": "==5.5.2"
-        },
-        "certifi": {
-            "hashes": [
-                "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651",
-                "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"
-            ],
-            "markers": "python_version >= '3.6'",
-            "version": "==2025.1.31"
-        },
-        "cffi": {
-            "hashes": [
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-                "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2",
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-                "sha256:6883e737d7d9e4899a8a695e00ec36bd4e5e4f18fabe0aca0efe0a4b44cdb13e",
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-                "sha256:78122be759c3f8a014ce010908ae03364d00a1f81ab5c7f4a7a5120607ea56e1",
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-                "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67",
-                "sha256:9755e4345d1ec879e3849e62222a18c7174d65a6a92d5b346b1863912168b595",
-                "sha256:98e3969bcff97cae1b2def8ba499ea3d6f31ddfdb7635374834cf89a1a08ecf0",
-                "sha256:a08d7e755f8ed21095a310a693525137cfe756ce62d066e53f502a83dc550f65",
-                "sha256:a1ed2dd2972641495a3ec98445e09766f077aee98a1c896dcb4ad0d303628e41",
-                "sha256:a24ed04c8ffd54b0729c07cee15a81d964e6fee0e3d4d342a27b020d22959dc6",
-                "sha256:a45e3c6913c5b87b3ff120dcdc03f6131fa0065027d0ed7ee6190736a74cd401",
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+      "markers": "python_version >= '3.7'",
+      "version": "==2.0.40"
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+        "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"
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+    },
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+      "markers": "python_version >= '3.8'",
+      "version": "==0.18.1"
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+    "threadpoolctl": {
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+      "markers": "python_version >= '3.9'",
+      "version": "==3.6.0"
+    },
+    "tinycss2": {
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+    },
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+        "sha256:e828cce1123e9e44ae2a50a9de3055497ab1d0aeb440c5ac23064d9e44880da1"
+      ],
+      "markers": "python_version >= '3.8'",
+      "version": "==6.4.2"
+    },
+    "tqdm": {
+      "hashes": [
+        "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2",
+        "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"
+      ],
+      "index": "pypi",
+      "markers": "python_version >= '3.7'",
+      "version": "==4.67.1"
+    },
+    "traitlets": {
+      "hashes": [
+        "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7",
+        "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"
+      ],
+      "markers": "python_version >= '3.8'",
+      "version": "==5.14.3"
+    },
+    "types-python-dateutil": {
+      "hashes": [
+        "sha256:18f493414c26ffba692a72369fea7a154c502646301ebfe3d56a04b3767284cb",
+        "sha256:e248a4bc70a486d3e3ec84d0dc30eec3a5f979d6e7ee4123ae043eedbb987f53"
+      ],
+      "markers": "python_version >= '3.8'",
+      "version": "==2.9.0.20241206"
+    },
+    "typing-extensions": {
+      "hashes": [
+        "sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c",
+        "sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef"
+      ],
+      "markers": "python_version < '3.13'",
+      "version": "==4.13.2"
+    },
+    "tzdata": {
+      "hashes": [
+        "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8",
+        "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"
+      ],
+      "markers": "python_version >= '2'",
+      "version": "==2025.2"
+    },
+    "uri-template": {
+      "hashes": [
+        "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7",
+        "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"
+      ],
+      "version": "==1.3.0"
+    },
+    "uritemplate": {
+      "hashes": [
+        "sha256:4346edfc5c3b79f694bccd6d6099a322bbeb628dbf2cd86eea55a456ce5124f0",
+        "sha256:830c08b8d99bdd312ea4ead05994a38e8936266f84b9a7878232db50b044e02e"
+      ],
+      "markers": "python_version >= '3.6'",
+      "version": "==4.1.1"
+    },
+    "urllib3": {
+      "hashes": [
+        "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466",
+        "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813"
+      ],
+      "markers": "python_version >= '3.9'",
+      "version": "==2.4.0"
+    },
+    "wcwidth": {
+      "hashes": [
+        "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859",
+        "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"
+      ],
+      "version": "==0.2.13"
+    },
+    "webcolors": {
+      "hashes": [
+        "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9",
+        "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6"
+      ],
+      "version": "==24.11.1"
+    },
+    "webencodings": {
+      "hashes": [
+        "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78",
+        "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"
+      ],
+      "version": "==0.5.1"
+    },
+    "websocket-client": {
+      "hashes": [
+        "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526",
+        "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"
+      ],
+      "markers": "python_version >= '3.8'",
+      "version": "==1.8.0"
+    },
+    "widgetsnbextension": {
+      "hashes": [
+        "sha256:4875a9eaf72fbf5079dc372a51a9f268fc38d46f767cbf85c43a36da5cb9b575",
+        "sha256:a3629b04e3edb893212df862038c7232f62973373869db5084aed739b437b5af"
+      ],
+      "markers": "python_version >= '3.7'",
+      "version": "==4.0.14"
+    }
+  },
+  "develop": {
+    "colorama": {
+      "hashes": [
+        "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44",
+        "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"
+      ],
+      "markers": "python_version > '3.4'",
+      "version": "==0.4.6"
+    },
+    "iniconfig": {
+      "hashes": [
+        "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7",
+        "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760"
+      ],
+      "markers": "python_version >= '3.8'",
+      "version": "==2.1.0"
+    },
+    "mando": {
+      "hashes": [
+        "sha256:18baa999b4b613faefb00eac4efadcf14f510b59b924b66e08289aa1de8c3500",
+        "sha256:26ef1d70928b6057ee3ca12583d73c63e05c49de8972d620c278a7b206581a8a"
+      ],
+      "version": "==0.7.1"
+    },
+    "packaging": {
+      "hashes": [
+        "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484",
+        "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f"
+      ],
+      "markers": "python_version >= '3.8'",
+      "version": "==25.0"
+    },
+    "pluggy": {
+      "hashes": [
+        "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1",
+        "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"
+      ],
+      "markers": "python_version >= '3.8'",
+      "version": "==1.5.0"
+    },
+    "pytest": {
+      "hashes": [
+        "sha256:c69214aa47deac29fad6c2a4f590b9c4a9fdb16a403176fe154b79c0b4d4d820",
+        "sha256:f4efe70cc14e511565ac476b57c279e12a855b11f48f212af1080ef2263d3845"
+      ],
+      "index": "pypi",
+      "markers": "python_version >= '3.8'",
+      "version": "==8.3.5"
+    },
+    "radon": {
+      "hashes": [
+        "sha256:632cc032364a6f8bb1010a2f6a12d0f14bc7e5ede76585ef29dc0cecf4cd8859",
+        "sha256:d1ac0053943a893878940fedc8b19ace70386fc9c9bf0a09229a44125ebf45b5"
+      ],
+      "index": "pypi",
+      "version": "==6.0.1"
+    },
+    "ruff": {
+      "hashes": [
+        "sha256:0509e8da430228236a18a677fcdb0c1f102dd26d5520f71f79b094963322ed25",
+        "sha256:0c000a471d519b3e6cfc9c6680025d923b4ca140ce3e4612d1a2ef58e11f11fe",
+        "sha256:248b1fb3f739d01d528cc50b35ee9c4812aa58cc5935998e776bf8ed5b251e75",
+        "sha256:45a56f61b24682f6f6709636949ae8cc82ae229d8d773b4c76c09ec83964a95a",
+        "sha256:496dd38a53aa173481a7d8866bcd6451bd934d06976a2505028a50583e001b76",
+        "sha256:52d587092ab8df308635762386f45f4638badb0866355b2b86760f6d3c076188",
+        "sha256:54799ca3d67ae5e0b7a7ac234baa657a9c1784b48ec954a094da7c206e0365b1",
+        "sha256:61323159cf21bc3897674e5adb27cd9e7700bab6b84de40d7be28c3d46dc67cf",
+        "sha256:7ae4478b1471fc0c44ed52a6fb787e641a2ac58b1c1f91763bafbc2faddc5117",
+        "sha256:7d7fc2377a04b6e04ffe588caad613d0c460eb2ecba4c0ccbbfe2bc973cbc162",
+        "sha256:91a7ddb221779871cf226100e677b5ea38c2d54e9e2c8ed847450ebbdf99b32d",
+        "sha256:9257aa841e9e8d9b727423086f0fa9a86b6b420fbf4bf9e1465d1250ce8e4d8d",
+        "sha256:bc3c083c50390cf69e7e1b5a5a7303898966be973664ec0c4a4acea82c1d4315",
+        "sha256:dcad24b81b62650b0eb8814f576fc65cfee8674772a6e24c9b747911801eeaa5",
+        "sha256:defed167955d42c68b407e8f2e6f56ba52520e790aba4ca707a9c88619e580e3",
+        "sha256:e169ea1b9eae61c99b257dc83b9ee6c76f89042752cb2d83486a7d6e48e8f764",
+        "sha256:e88b8f6d901477c41559ba540beeb5a671e14cd29ebd5683903572f4b40a9807",
+        "sha256:f1d70bef3d16fdc897ee290d7d20da3cbe4e26349f62e8a0274e7a3f4ce7a905"
+      ],
+      "index": "pypi",
+      "markers": "python_version >= '3.7'",
+      "version": "==0.8.6"
+    },
+    "six": {
+      "hashes": [
+        "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274",
+        "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"
+      ],
+      "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'",
+      "version": "==1.17.0"
+    },
+    "vulture": {
+      "hashes": [
+        "sha256:cb8277902a1138deeab796ec5bef7076a6e0248ca3607a3f3dee0b6d9e9b8415",
+        "sha256:d9a90dba89607489548a49d557f8bac8112bd25d3cbc8aeef23e860811bd5ed9"
+      ],
+      "index": "pypi",
+      "markers": "python_version >= '3.8'",
+      "version": "==2.14"
     }
+  }
 }
diff --git a/data/src/config/config.py b/data/src/config/config.py
index 4b240cf2..92c4eda9 100644
--- a/data/src/config/config.py
+++ b/data/src/config/config.py
@@ -1,12 +1,21 @@
 import logging
 from pathlib import Path
 
-FORCE_RELOAD = False
+FORCE_RELOAD = True
 """ During the data load, whether to query the various GIS API services for the data to load into the postgres tables.  If True, will query the API services, backup the database, reload the database and report on data differences.  If false will read the data from postgres."""
 
 USE_CRS = "EPSG:2272"
 """ the standard geospatial code for Pennsylvania South (ftUS) """
 
+ROOT_DIRECTORY = Path(__file__).resolve().parent.parent
+""" the root directory of the project """
+
+FORCE_CACHE = False
+"""After loading ESRI/CARTO/other GDF data, whether to cache in the FeatureLayer initialization."""
+
+CACHE_FRACTION = 0.05
+"""The fraction used to cache portions of the pipeline's transformed data in each step of the pipeline."""
+
 log_level: int = logging.WARN
 """ overall log level for the project """
 
diff --git a/data/src/config/psql.py b/data/src/config/psql.py
deleted file mode 100644
index f4818363..00000000
--- a/data/src/config/psql.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from config.config import is_docker
-from sqlalchemy import create_engine
-
-url: str = (
-    os.environ["VACANT_LOTS_DB"].replace("localhost", "host.docker.internal")
-    if is_docker()
-    else os.environ["VACANT_LOTS_DB"]
-)
-local_engine = create_engine(url)
-conn = local_engine.connect()
diff --git a/data/src/data_utils/conservatorship.py b/data/src/data_utils/conservatorship.py
index 5f9c9793..16043058 100644
--- a/data/src/data_utils/conservatorship.py
+++ b/data/src/data_utils/conservatorship.py
@@ -1,6 +1,7 @@
 import datetime
-from dateutil.parser import parse
+
 import pytz
+from dateutil.parser import parse
 
 est = pytz.timezone("US/Eastern")
 six_months_ago = (datetime.datetime.now() - datetime.timedelta(days=180)).astimezone(
@@ -28,7 +29,7 @@
 def conservatorship(primary_featurelayer):
     conservatorships = []
 
-    for idx, row in primary_featurelayer.gdf.iterrows():
+    for _, row in primary_featurelayer.gdf.iterrows():
         city_owner_agency = row["city_owner_agency"]
         sheriff_sale = row["sheriff_sale"] == "Y"
         market_value_over_1000 = (
@@ -56,4 +57,5 @@ def conservatorship(primary_featurelayer):
         conservatorships.append(conservatorship)
 
     primary_featurelayer.gdf["conservatorship"] = conservatorships
+
     return primary_featurelayer
diff --git a/data/src/data_utils/priority_level.py b/data/src/data_utils/priority_level.py
index 33097de3..cab5a35b 100644
--- a/data/src/data_utils/priority_level.py
+++ b/data/src/data_utils/priority_level.py
@@ -1,6 +1,6 @@
 def priority_level(dataset):
     priority_levels = []
-    for idx, row in dataset.gdf.iterrows():
+    for _, row in dataset.gdf.iterrows():
         priority_level = ""
 
         # Decision Points
diff --git a/data/src/data_utils/tactical_urbanism.py b/data/src/data_utils/tactical_urbanism.py
index df15a0f2..f9d78adb 100644
--- a/data/src/data_utils/tactical_urbanism.py
+++ b/data/src/data_utils/tactical_urbanism.py
@@ -5,7 +5,7 @@ def tactical_urbanism(dataset):
 
     tactical_urbanism_values = []
 
-    for idx, row in dataset.gdf.iterrows():
+    for _, row in dataset.gdf.iterrows():
         li_complaints_lower = str(row["li_complaints"]).lower().split(" ")
         contains_unsafe_word = any(word in li_complaints_lower for word in unsafe_words)
 
diff --git a/data/src/main.py b/data/src/main.py
index 25176201..83e625d6 100644
--- a/data/src/main.py
+++ b/data/src/main.py
@@ -2,13 +2,10 @@
 
 import pandas as pd
 
-from config.config import tiles_file_id_prefix
-from config.psql import conn
-from new_etl.classes.data_diff import DiffReport
+from new_etl.classes.file_manager import FileManager, FileType, LoadType
 from new_etl.classes.slack_reporters import (
     send_dataframe_profile_to_slack,
     send_error_to_slack,
-    send_pg_stats_to_slack,
 )
 from new_etl.data_utils import (
     access_process,
@@ -39,8 +36,8 @@
     unsafe_buildings,
     vacant_properties,
 )
-from new_etl.database import to_postgis_with_schema
 
+file_manager = FileManager()
 
 try:
     print("Starting ETL process.")
@@ -72,6 +69,16 @@
         park_priority,
     ]
 
+    # Numeric columns to track for coercion
+    numeric_columns = [
+        "market_value",
+        "sale_price",
+        "total_assessment",
+        "total_due",
+        "num_years_owed",
+        "permit_count",
+    ]
+
     print("Loading OPA properties dataset.")
     dataset = opa_properties()
 
@@ -79,6 +86,10 @@
         print(f"Running service: {service.__name__}")
         dataset = service(dataset)
 
+        # If we want to save fractional steps along the pipeline, we need to coerce these the numeric data types
+        # "most_recent_year_owed" as seen in lines 108-112 and at each step otherwise it cannot validly save to the
+        # parquet file
+
     print("Applying final dataset transformations.")
     dataset = priority_level(dataset)
     dataset = access_process(dataset)
@@ -94,46 +105,37 @@
     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"
-    )
+    # Convert columns where necessary
+    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
     )
 
     # Dataset profiling
-    send_dataframe_profile_to_slack(dataset.gdf, "all_properties_end")
+    # send_dataframe_profile_to_slack(dataset.gdf, "all_properties_end")
 
     # Save dataset to PostgreSQL
-    to_postgis_with_schema(dataset.gdf, "all_properties_end", conn)
+    # to_postgis_with_schema(dataset.gdf, "all_properties_end", conn)
 
     # Generate and send diff report
-    diff_report = DiffReport()
-    diff_report.run()
+    # diff_report = DiffReport()
+    # diff_report.run()
 
-    send_pg_stats_to_slack(conn)  # Send PostgreSQL stats to Slack
+    # 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}")
+    file_label = file_manager.generate_file_label("all_properties_end")
+    file_manager.save_gdf(
+        dataset.gdf, file_label, LoadType.PIPELINE_CACHE, FileType.PARQUET
+    )
+    print(f"Dataset saved to Parquet in storage/pipeline_cache/{file_label}.parquet")
 
     # Publish only vacant properties
     dataset.gdf = dataset.gdf[dataset.gdf["vacant"]]
-    dataset.build_and_publish(tiles_file_id_prefix)
+    # dataset.build_and_publish(tiles_file_id_prefix)
 
     # Finalize
-    conn.commit()
-    conn.close()
     print("ETL process completed successfully.")
 
 except Exception as e:
diff --git a/data/src/new_etl/classes/bucket_manager.py b/data/src/new_etl/classes/bucket_manager.py
new file mode 100644
index 00000000..045571aa
--- /dev/null
+++ b/data/src/new_etl/classes/bucket_manager.py
@@ -0,0 +1,68 @@
+import logging as log
+import os
+
+from google.cloud import storage
+
+from config.config import log_level
+
+log.basicConfig(level=log_level)
+
+
+class GCSBucketManager:
+    """
+    A manager for interacting with a Google Cloud Storage bucket.
+
+    This class initializes a bucket client using Application Default Credentials,
+    an optional service account key, or falls back to an anonymous client for read-only access.
+    """
+
+    def __init__(
+        self, bucket_name: str = None, credential_path: str = None, client=None
+    ):
+        """
+        Initialize the GCSBucketManager.
+
+        Args:
+            bucket_name (str): Name of the bucket. Defaults to the environment variable
+                               'GOOGLE_CLOUD_BUCKET_NAME' or "cleanandgreenphl" if not set.
+            credential_path (str): Optional path to a service account credentials file.
+            client: Optional storage. Client instance for dependency injection in testing.
+        """
+        self.bucket_name = bucket_name or os.getenv(
+            "GOOGLE_CLOUD_BUCKET_NAME", "cleanandgreenphl"
+        )
+
+        self.read_only = False
+
+        if client is not None:
+            self._client = client
+        else:
+            self._client = self._init_client(credential_path)
+
+        self.bucket = self._client.bucket(self.bucket_name)
+
+    def _init_client(self, credential_path: str = None):
+        """
+        Attempt to initialize the storage client using a credential file, application default
+        credentials or fall back to anonymous/read-only.
+        """
+        project_name = os.getenv("GOOGLE_CLOUD_PROJECT", "clean-and-green-philly")
+        credentials_path = credential_path or "/app/service-account-key.json"
+        is_credentials_file = os.path.exists(credentials_path)
+
+        if is_credentials_file:
+            os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentials_path
+
+        try:
+            # This will use application default credentials if GOOGLE_APPLICATION_CREDENTIALS is not set
+            if is_credentials_file:
+                print(f"Using service account key at {credentials_path}")
+            else:
+                print("Using application default credentials")
+            return storage.Client(project=project_name)
+
+        except Exception as e:
+            log.warning(f"Failed to initialize client with service account key: {e}")
+            log.warning("Falling back to anonymous client (read-only mode)")
+            self.read_only = True
+            return storage.Client.create_anonymous_client()
diff --git a/data/src/new_etl/classes/data_diff.py b/data/src/new_etl/classes/data_diff.py
index 3dc40bfb..59ab9b90 100644
--- a/data/src/new_etl/classes/data_diff.py
+++ b/data/src/new_etl/classes/data_diff.py
@@ -1,7 +1,8 @@
-from slack_sdk import WebClient
+import os
+
 import pandas as pd
+from slack_sdk import WebClient
 from sqlalchemy import text
-import os
 
 from config.psql import conn
 
@@ -115,27 +116,31 @@ def generate_diff(self):
 
         self.summary_text = "\n".join(summary_lines)
 
-    def send_to_slack(self, channel="clean-and-green-philly-pipeline"):
+    def send_to_slack(
+        self, channel="clean-and-green-philly-pipeline", slack_token=None
+    ):
         """
         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.")
+        token = slack_token or os.getenv("CAGP_SLACK_API_TOKEN")
+        if not token:
+            print("Slack API token not found in environment variables.")
+            print("Skipping sending diff report to Slack.")
+            return
+
+        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}")
 
     def run(self, send_to_slack=True, slack_channel="clean-and-green-philly-pipeline"):
         """
diff --git a/data/src/new_etl/classes/featurelayer.py b/data/src/new_etl/classes/featurelayer.py
index 4cc26d28..317599d8 100644
--- a/data/src/new_etl/classes/featurelayer.py
+++ b/data/src/new_etl/classes/featurelayer.py
@@ -7,43 +7,38 @@
 import geopandas as gpd
 import pandas as pd
 import requests
-import sqlalchemy as sa
 from google.cloud import storage
-from google.cloud.storage.bucket import Bucket
 from shapely import wkb
 from tqdm import tqdm
 
 from config.config import (
+    FORCE_CACHE,
     FORCE_RELOAD,
     USE_CRS,
     log_level,
     min_tiles_file_size_in_bytes,
     write_production_tiles_file,
 )
-from config.psql import conn, local_engine
-from new_etl.database import to_postgis_with_schema
+from new_etl.classes.bucket_manager import GCSBucketManager
+from new_etl.classes.file_manager import FileManager, FileType, LoadType
 from new_etl.loaders import load_carto_data, load_esri_data
 
 log.basicConfig(level=log_level)
 
 
-def google_cloud_bucket() -> Bucket:
-    """Build the google cloud bucket with name configured in your environ or default of cleanandgreenphl
-
-    Returns:
-        Bucket: the gcp bucket
+def google_cloud_bucket(require_write_access: bool = False) -> storage.Bucket | None:
     """
+    Initialize a Google Cloud Storage bucket client using Application Default Credentials.
+    If a writable bucket is requested and the user does not have write access None is returned.
 
-    credentials_path = os.path.expanduser("/app/service-account-key.json")
-    if not os.path.exists(credentials_path):
-        raise FileNotFoundError(f"Credentials file not found at {credentials_path}")
-
-    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentials_path
-    bucket_name = os.getenv("GOOGLE_CLOUD_BUCKET_NAME", "cleanandgreenphl")
-    project_name = os.getenv("GOOGLE_CLOUD_PROJECT", "clean-and-green-philly")
+    Args:
+        require_write_access (bool): Whether it is required that the bucket should be writable. Defaults to False.
+    """
 
-    storage_client = storage.Client(project=project_name)
-    return storage_client.bucket(bucket_name)
+    bucket_manager = GCSBucketManager()
+    if require_write_access and bucket_manager.read_only:
+        return None
+    return bucket_manager.bucket
 
 
 class FeatureLayer:
@@ -78,11 +73,12 @@ def __init__(
         self.gdf = gdf
         self.crs = crs
         self.cols = cols
-        self.psql_table = name.lower().replace(" ", "_")
+        self.table_name = name.lower().replace(" ", "_")
         self.input_crs = "EPSG:4326" if not from_xy else USE_CRS
         self.use_wkb_geom_field = use_wkb_geom_field
         self.max_workers = max_workers
         self.chunk_size = chunk_size
+        self.file_manager = FileManager()
 
         inputs = [self.esri_rest_urls, self.carto_sql_queries, self.gdf]
         non_none_inputs = [i for i in inputs if i is not None]
@@ -94,28 +90,20 @@ def __init__(
                 if self.carto_sql_queries
                 else "gdf"
             )
-            if force_reload or not self.check_psql():
+            if not force_reload and self.file_manager.check_source_cache_file_exists(
+                self.table_name, LoadType.SOURCE_CACHE
+            ):
+                log.info(f"Loading data for {self.name} from cache...")
+                self.gdf = self.file_manager.get_most_recent_cache(self.table_name)
+            else:
+                print("Loading data now...")
                 self.load_data()
+                if FORCE_CACHE:
+                    print("Caching data now...")
+                    self.cache_data()
         else:
             log.info(f"Initialized FeatureLayer {self.name} with no data.")
 
-    def check_psql(self):
-        try:
-            if not sa.inspect(local_engine).has_table(self.psql_table):
-                log.debug(f"Table {self.psql_table} does not exist")
-                return False
-            psql_table = gpd.read_postgis(
-                f"SELECT * FROM {self.psql_table}", conn, geom_col="geometry"
-            )
-            if len(psql_table) == 0:
-                return False
-            log.info(f"Loading data for {self.name} from psql...")
-            self.gdf = psql_table
-            return True
-        except Exception as e:
-            log.error(f"Error loading data for {self.name}: {e}")
-            return False
-
     def load_data(self):
         log.info(f"Loading data for {self.name} from {self.type}...")
 
@@ -149,13 +137,28 @@ def load_data(self):
                     ]
 
                 # Save GeoDataFrame to PostgreSQL and configure it as a hypertable
-                to_postgis_with_schema(self.gdf, self.psql_table, conn)
+                # to_postgis_with_schema(
+                #     self.gdf, self.table_name, conn, if_exists="replace"
+                # )
 
         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 cache_data(self):
+        log.info(f"Caching data for {self.name} to local file system...")
+
+        if self.gdf is None and self.gdf.empty:
+            log.info("No data to cache.")
+            return
+
+        # Save sourced data to a local parquet file in the storage/source_cache directory
+        file_label = self.file_manager.generate_file_label(self.table_name)
+        self.file_manager.save_gdf(
+            self.gdf, file_label, LoadType.SOURCE_CACHE, FileType.PARQUET
+        )
+
     def _load_carto_data(self):
         if not self.carto_sql_queries:
             raise ValueError("Must provide SQL query to load data from Carto")
@@ -287,11 +290,21 @@ def build_and_publish(self, tiles_file_id_prefix: str) -> None:
         zoom_threshold: int = 13
 
         # Export the GeoDataFrame to a temporary GeoJSON file
-        temp_geojson_points: str = f"tmp/temp_{tiles_file_id_prefix}_points.geojson"
-        temp_geojson_polygons: str = f"tmp/temp_{tiles_file_id_prefix}_polygons.geojson"
-        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_geojson_points: str = (
+            f"storage/temp/temp_{tiles_file_id_prefix}_points.geojson"
+        )
+        temp_geojson_polygons: str = (
+            f"storage/temp/temp_{tiles_file_id_prefix}_polygons.geojson"
+        )
+        temp_pmtiles_points: str = (
+            f"storage/temp/temp_{tiles_file_id_prefix}_points.pmtiles"
+        )
+        temp_pmtiles_polygons: str = (
+            f"storage/temp/temp_{tiles_file_id_prefix}_polygons.pmtiles"
+        )
+        temp_merged_pmtiles: str = (
+            f"storage/temp/temp_{tiles_file_id_prefix}_merged.pmtiles"
+        )
 
         # Reproject
         gdf_wm = self.gdf.to_crs(epsg=4326)
@@ -358,6 +371,11 @@ def build_and_publish(self, tiles_file_id_prefix: str) -> None:
                 f"{temp_merged_pmtiles} is {file_size} bytes in size but should be at least {min_tiles_file_size_in_bytes}. Therefore, we are not uploading any files to the GCP bucket. The file may be corrupt or incomplete."
             )
 
+        bucket = google_cloud_bucket(require_write_access=True)
+        if bucket is None:
+            print("Skipping PMTiles upload due to read-only bucket access.")
+            return
+
         # Upload PMTiles to Google Cloud Storage
         bucket = google_cloud_bucket()
         for file in write_files:
diff --git a/data/src/new_etl/classes/file_manager.py b/data/src/new_etl/classes/file_manager.py
new file mode 100644
index 00000000..516d432d
--- /dev/null
+++ b/data/src/new_etl/classes/file_manager.py
@@ -0,0 +1,235 @@
+import os
+import zipfile
+from datetime import datetime
+from enum import Enum
+from io import BytesIO
+from typing import List
+
+import geopandas as gpd
+from tqdm import tqdm
+
+from config.config import CACHE_FRACTION, ROOT_DIRECTORY
+
+print(f"Root directory is {ROOT_DIRECTORY}")
+
+
+class LoadType(Enum):
+    TEMP = "temp"
+    SOURCE_CACHE = "source_cache"
+    PIPELINE_CACHE = "pipeline_cache"
+
+
+class FileType(Enum):
+    GEOJSON = "geojson"
+    PARQUET = "parquet"
+
+
+class FileManager:
+    """
+    A manager for interacting with cached files or temporary files loaded from or extracting into the local filesystem.
+    """
+
+    def __init__(self, fraction=CACHE_FRACTION):
+        """
+        Initialize the FileManager with paths for the temporary and cache directories at the root directory of the project.
+        """
+        self.storage_directory = os.path.join(ROOT_DIRECTORY, "storage")
+
+        if not os.path.exists(self.storage_directory):
+            os.makedirs(self.storage_directory)
+
+        self.fraction = fraction
+        self.temp_directory = os.path.join(self.storage_directory, "tmp")
+        self.source_cache_directory = os.path.join(
+            self.storage_directory, "source_cache"
+        )
+        self.pipeline_cache_directory = os.path.join(
+            self.storage_directory, "pipeline_cache"
+        )
+
+        if not os.path.exists(self.temp_directory):
+            os.makedirs(self.temp_directory)
+        if not os.path.exists(self.source_cache_directory):
+            os.makedirs(self.source_cache_directory)
+        if not os.path.exists(self.pipeline_cache_directory):
+            os.makedirs(self.pipeline_cache_directory)
+
+    def generate_file_label(self, table_name: str) -> str:
+        """
+        Generates a file label for a given table name to cache parquet files according to format
+        <table_name>_<date>_<(old | new)>.parquet according whether it was generated from the new or old pipeline (use just new for now).
+        Args:
+            table_name (str): The name of the table.
+        Returns:
+            str: The generated file label.
+        """
+        date = datetime.now().strftime("%Y_%m_%d")
+        return f"{table_name}_{date}_new"
+
+    def get_file_path(
+        self, file_name: str, load_type: LoadType, file_type: FileType | None = None
+    ) -> str:
+        """
+        Get the full file path for a given file depending on whether it belongs in the temporary or cache directory.
+
+        Args:
+            file_name (str): The name of the file.
+            file_type (FileType): The type of the file (GEOJSON or PARQUET).
+            load_type (LoadType): The destination type of the file (TEMP or CACHE).
+        """
+        parent_directory = (
+            self.temp_directory
+            if load_type == LoadType.TEMP
+            else self.source_cache_directory
+            if load_type == LoadType.SOURCE_CACHE
+            else self.pipeline_cache_directory
+        )
+        file_name = f"{file_name}.{file_type.value}" if file_type else file_name
+        return os.path.join(parent_directory, file_name)
+
+    def check_file_exists(
+        self, file_name: str, load_type: LoadType, file_type: FileType | None = None
+    ) -> bool:
+        """
+        Checks if a file exists in the temporary or cache directory.
+        Args:
+            file_name (str): The name of the file.
+            load_type (LoadType): The destination type of the file (TEMP or CACHE).
+        Returns:
+            bool: True if the file exists, False otherwise.
+        """
+        file_path = self.get_file_path(file_name, load_type, file_type)
+        return os.path.exists(file_path)
+
+    def check_source_cache_file_exists(
+        self, table_name: str, load_type: LoadType
+    ) -> bool:
+        """
+        Checks for the existence of a file matching the given tablename in the caching directories -
+        either a source file for the data or an intermediate step in the pipeline.
+        Args:
+            table_name (str): The name of the table of source data.
+            load_type (LoadType): The destination type of the file (either SOURCE_CACHE or PIPELINE_CACHE).
+        """
+        directory = (
+            self.source_cache_directory
+            if load_type == LoadType.SOURCE_CACHE
+            else self.pipeline_cache_directory
+        )
+        return len([file for file in os.listdir(directory) if table_name in file]) > 0
+
+    def get_most_recent_cache(self, table_name: str) -> gpd.GeoDataFrame | None:
+        """
+        Returns the most recently generated file in the cache directory for a given table name.
+        Args:
+            table_name (str): The name of the table.
+        Returns:
+            GeoDataFrame: The dataframe loaded from the most recent cached file.
+            None: If no files exist for the given table name.
+        """
+        cached_files = [
+            file
+            for file in os.listdir(self.source_cache_directory)
+            if table_name in file
+        ]
+
+        if not cached_files:
+            return None
+
+        cached_files.sort(
+            key=lambda x: os.path.getmtime(
+                os.path.join(self.source_cache_directory, x)
+            ),
+            reverse=True,
+        )
+        most_recent_file = cached_files[0]
+        file_path = self.get_file_path(most_recent_file, LoadType.SOURCE_CACHE)
+
+        return gpd.read_parquet(file_path)
+
+    def load_gdf(
+        self, file_name: str, load_type: LoadType, file_type: FileType | None = None
+    ) -> gpd.GeoDataFrame:
+        """
+        Loads a GeoDataFrame into memory from a local file in the temporary or cache directory.
+
+        Args:
+            file_name (str): The name of the file.
+            file_type (FileType): The type of the file (GEOJSON or PARQUET).
+            load_type (LoadType): The destination type of the file (TEMP or CACHE).
+        """
+        file_path = self.get_file_path(file_name, load_type, file_type)
+        if os.path.exists(file_path):
+            gdf = (
+                gpd.read_file(file_path)
+                if file_type == FileType.GEOJSON
+                else gpd.read_parquet(file_path)
+            )
+            return gdf
+        else:
+            raise FileNotFoundError(
+                f"File {file_name} not found in corresponding directory."
+            )
+
+    def save_gdf(
+        self,
+        gdf: gpd.GeoDataFrame,
+        file_name: str,
+        load_type: LoadType,
+        file_type: FileType | None = None,
+    ) -> None:
+        """
+        Saves a GeoDataFrame to a local file in the temporary or cache directory.
+
+        Args:
+            gdf (gpd.GeoDataFrame): The GeoDataFrame to save.
+            file_name (str): The name of the file.
+            file_type (FileType): The type of the file (GEOJSON or PARQUET).
+            load_type (LoadType): The destination type of the file (TEMP or CACHE).
+        """
+        file_path = self.get_file_path(file_name, load_type, file_type)
+        if file_type == FileType.PARQUET:
+            gdf.to_parquet(file_path, index=False)
+        elif file_type == FileType.GEOJSON:
+            gdf.to_file(file_path, driver="GeoJSON")
+        else:
+            raise ValueError(f"Unsupported file type: {file_type}")
+
+    def save_fractional_gdf(
+        self,
+        gdf: gpd.GeoDataFrame,
+        file_name: str,
+        load_type: LoadType,
+    ) -> None:
+        """
+        Saves a portion of a supplied GeoDataFrame to a local file in the temporary or cache directory based on a deterministic selection of some of the rows.
+
+        Args:
+            gdf (gpd.GeoDataFrame): The GeoDataFrame to save.
+            file_name (str): The name of the file.
+            load_type (LoadType): The destination type of the file (TEMP or CACHE).
+
+        This will always be used for a parquet file in our used case, so no need to pass in file_type.
+        """
+
+        num_rows = len(gdf)
+        num_rows_to_save = int(num_rows * self.fraction)
+        reduced_gdf = gdf.iloc[:: num_rows // num_rows_to_save]
+        file_path = self.get_file_path(file_name, load_type, FileType.PARQUET)
+
+        reduced_gdf.to_parquet(file_path, index=False)
+
+    def extract_files(self, buffer: BytesIO, filenames: List[str]) -> None:
+        """
+        Extracts files stored in a zip buffer to the local temp directory. Because cache is intended only for finalized
+        geoparquet files that contain the cleaned, final data, we store all intermediate extracted files in the temp directory
+        by default.
+
+        Args:
+            buffer (BytesIO): The zip file buffer containing all of the files to extract.
+            filenames (List[str]): A list of the filenames to be extracted from the zip file.
+        """
+        destination = self.temp_directory
+        with zipfile.ZipFile(buffer) as zip_ref:
+            for filename in tqdm(filenames, desc="Extracting"):
+                zip_ref.extract(filename, destination)
diff --git a/data/src/new_etl/classes/slack_reporters.py b/data/src/new_etl/classes/slack_reporters.py
index 6ae08517..326897c3 100644
--- a/data/src/new_etl/classes/slack_reporters.py
+++ b/data/src/new_etl/classes/slack_reporters.py
@@ -1,12 +1,14 @@
-from sqlalchemy import text
 import os
-from slack_sdk import WebClient
 
 import pandas as pd
+from slack_sdk import WebClient
 
 
 def send_dataframe_profile_to_slack(
-    df: pd.DataFrame, df_name: str, channel="clean-and-green-philly-pipeline"
+    df: pd.DataFrame,
+    df_name: str,
+    channel="clean-and-green-philly-pipeline",
+    slack_token: str | None = None,
 ):
     """
     Profiles a DataFrame and sends the QC profile summary to a Slack channel.
@@ -15,7 +17,13 @@ def send_dataframe_profile_to_slack(
         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.
+        slack_token (str): The Slack API token. If not provided, it will be read from the environment.
     """
+    token = slack_token or os.getenv("CAGP_SLACK_API_TOKEN")
+    if not token:
+        print("Slack API token not found in environment variables.")
+        print("Skipping QC profile report to Slack.")
+        return
     # Step 1: Profile the DataFrame
     profile_summary = {}
 
@@ -58,77 +66,82 @@ def send_dataframe_profile_to_slack(
         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 = WebClient(token=token)
+    try:
         client.chat_postMessage(
-            channel="clean-and-green-philly-pipeline",
+            channel=channel,
             text=message,
-            username="PG Stats Reporter",
+            username="QC Reporter",
         )
-    else:
-        raise ValueError("Slack API token not found in environment variables.")
+        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}")
+
+
+# def send_pg_stats_to_slack(conn, slack_token: str | None = None):
+#     """
+#     Report total sizes for all hypertables using hypertable_detailed_size
+#     and send the result to a Slack channel.
+
+#     Args:
+#         conn: SQLAlchemy connection to the PostgreSQL database.
+#         slack_token (str): The Slack API token. If not provided, it will be read from the environment.
+#     """
+#     token = slack_token or os.getenv("CAGP_SLACK_API_TOKEN")
+#     if not token:
+#         print("Slack API token not found in environment variables.")
+#         print("Skipping PostgreSQL stats report to Slack.")
+#         return
+
+#     # 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
+#     client = WebClient(token=token)
+#     client.chat_postMessage(
+#         channel="clean-and-green-philly-pipeline",
+#         text=message,
+#         username="PG Stats Reporter",
+#     )
 
 
 def send_diff_report_to_slack(
-    diff_summary: str, report_url: str, channel="clean-and-green-philly-pipeline"
+    diff_summary: str,
+    report_url: str,
+    channel="clean-and-green-philly-pipeline",
+    slack_token: str | None = None,
 ):
     """
     Sends a difference report summary to a Slack channel.
@@ -137,41 +150,48 @@ def send_diff_report_to_slack(
         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.
+        slack_token (str): The Slack API token. If not provided, it will be read from the environment.
     """
     print(
         f"send_diff_report_to_slack called with:\ndiff_summary:\n{diff_summary}\nreport_url: {report_url}"
     )
+    token = slack_token or os.getenv("CAGP_SLACK_API_TOKEN")
+    if not token:
+        print("Slack API token not found in environment variables.")
+        print("Skipping diff report to Slack.")
+        return
 
     # 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 = WebClient(token=token)
+    try:
         client.chat_postMessage(
-            channel="clean-and-green-philly-back-end",  # Replace with actual Slack channel ID
-            text=error_message,
-            username="Backend Error Reporter",
+            channel=channel,
+            text=message,
+            username="Diff Reporter",
         )
-    else:
-        raise ValueError("Slack API token not found in environment variables.")
+        print("Diff report sent to Slack successfully.")
+    except Exception as e:
+        print(f"Failed to send diff report to Slack: {e}")
+
+
+def send_error_to_slack(error_message: str, slack_token: str | None = None) -> None:
+    """Send error message to Slack."""
+
+    token = slack_token or os.getenv("CAGP_SLACK_API_TOKEN")
+    if not token:
+        print("Slack API token not found in environment variables.")
+        print("Skipping error report to Slack.")
+        print("Error message:")
+        print(error_message)
+        return
+
+    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",
+    )
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 3b714daf..bcb79bcb 100644
--- a/data/src/new_etl/data_utils/city_owned_properties.py
+++ b/data/src/new_etl/data_utils/city_owned_properties.py
@@ -1,8 +1,63 @@
+import geopandas as gpd
+
 from ..classes.featurelayer import FeatureLayer
 from ..constants.services import CITY_OWNED_PROPERTIES_TO_LOAD
 from ..metadata.metadata_utils import provide_metadata
 
 
+def transform_city_owned_properties(gdf: gpd.GeoDataFrame):
+    """
+    Transforms the city-owned properties data by renaming columns and updating access
+    information for properties based on ownership.
+
+    Args:
+        gdf (GeoDataFrame): The input GeoDataFrame as part of a primary feature layer that will have some transformations added to it.
+    """
+    rename_columns = {
+        "agency": "city_owner_agency",
+        "sideyardeligible": "side_yard_eligible",
+    }
+    gdf.rename(columns=rename_columns, inplace=True)
+
+    gdf.loc[
+        gdf["owner_1"].isin(
+            [
+                "PHILADELPHIA HOUSING AUTH",
+                "PHILADELPHIA LAND BANK",
+                "REDEVELOPMENT AUTHORITY",
+                "PHILA REDEVELOPMENT AUTH",
+            ]
+        ),
+        "city_owner_agency",
+    ] = gdf["owner_1"].replace(
+        {
+            "PHILADELPHIA HOUSING AUTH": "PHA",
+            "PHILADELPHIA LAND BANK": "Land Bank (PHDC)",
+            "REDEVELOPMENT AUTHORITY": "PRA",
+            "PHILA REDEVELOPMENT AUTH": "PRA",
+        }
+    )
+
+    gdf.loc[
+        (gdf["owner_1"] == "CITY OF PHILA")
+        & (
+            gdf["owner_2"].str.contains("PUBLIC PROP|PUBLC PROP", na=False)
+        ),  # ISSUE Check typo
+        "city_owner_agency",
+    ] = "DPP"
+
+    gdf.loc[
+        gdf["owner_1"].isin(["CITY OF PHILADELPHIA", "CITY OF PHILA"])
+        & gdf["owner_2"].isna(),
+        "city_owner_agency",
+    ] = "City of Philadelphia"
+
+    gdf.loc[:, "side_yard_eligible"] = gdf["side_yard_eligible"].fillna("No")
+
+    # Update all instances where city_owner_agency is "PLB" to "Land Bank (PHDC)"
+    gdf.loc[gdf["city_owner_agency"] == "PLB", "city_owner_agency"] = "Land Bank (PHDC)"
+
+
 @provide_metadata()
 def city_owned_properties(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     """
@@ -42,56 +97,6 @@ def city_owned_properties(primary_featurelayer: FeatureLayer) -> FeatureLayer:
 
     primary_featurelayer.opa_join(city_owned_properties.gdf, "opabrt")
 
-    rename_columns = {
-        "agency": "city_owner_agency",
-        "sideyardeligible": "side_yard_eligible",
-    }
-    primary_featurelayer.gdf.rename(columns=rename_columns, inplace=True)
-
-    primary_featurelayer.gdf.loc[
-        primary_featurelayer.gdf["owner_1"].isin(
-            [
-                "PHILADELPHIA HOUSING AUTH",
-                "PHILADELPHIA LAND BANK",
-                "REDEVELOPMENT AUTHORITY",
-                "PHILA REDEVELOPMENT AUTH",
-            ]
-        ),
-        "city_owner_agency",
-    ] = primary_featurelayer.gdf["owner_1"].replace(
-        {
-            "PHILADELPHIA HOUSING AUTH": "PHA",
-            "PHILADELPHIA LAND BANK": "Land Bank (PHDC)",
-            "REDEVELOPMENT AUTHORITY": "PRA",
-            "PHILA REDEVELOPMENT AUTH": "PRA",
-        }
-    )
-
-    primary_featurelayer.gdf.loc[
-        (primary_featurelayer.gdf["owner_1"] == "CITY OF PHILA")
-        & (
-            primary_featurelayer.gdf["owner_2"].str.contains(
-                "PUBLIC PROP|PUBLC PROP", na=False
-            )
-        ),
-        "city_owner_agency",
-    ] = "DPP"
-
-    primary_featurelayer.gdf.loc[
-        primary_featurelayer.gdf["owner_1"].isin(
-            ["CITY OF PHILADELPHIA", "CITY OF PHILA"]
-        )
-        & primary_featurelayer.gdf["owner_2"].isna(),
-        "city_owner_agency",
-    ] = "City of Philadelphia"
-
-    primary_featurelayer.gdf.loc[:, "side_yard_eligible"] = primary_featurelayer.gdf[
-        "side_yard_eligible"
-    ].fillna("No")
-
-    # Update all instances where city_owner_agency is "PLB" to "Land Bank (PHDC)"
-    primary_featurelayer.gdf.loc[
-        primary_featurelayer.gdf["city_owner_agency"] == "PLB", "city_owner_agency"
-    ] = "Land Bank (PHDC)"
+    transform_city_owned_properties(primary_featurelayer.gdf)
 
     return primary_featurelayer
diff --git a/data/src/new_etl/data_utils/community_gardens.py b/data/src/new_etl/data_utils/community_gardens.py
index ba72d9b3..f89cbd03 100644
--- a/data/src/new_etl/data_utils/community_gardens.py
+++ b/data/src/new_etl/data_utils/community_gardens.py
@@ -1,3 +1,5 @@
+import geopandas as gpd
+
 from config.config import USE_CRS
 
 from ..classes.featurelayer import FeatureLayer
@@ -5,6 +7,79 @@
 from ..metadata.metadata_utils import provide_metadata
 
 
+def check_community_gardens_gdf(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
+    """
+    Validates the community gardens GeoDataFrame by transforming all non-point geoemtries to their centroids and doing other naming transformations.
+
+    Args:
+        gdf (gpd.GeoDataFrame): The input GeoDataFrame containing community gardens data.
+
+    Returns:
+        gpd.GeoDataFrame: The transformed GeoDataFrame with only point geometries and relevant columns.
+    """
+
+    # Ensure both layers are in the same CRS
+    if gdf.crs != USE_CRS:
+        print(f"Transforming community gardens from {gdf.crs} to {USE_CRS}")
+        gdf = gdf.to_crs(USE_CRS)
+
+    # Identify problematic gardens
+    geom_types = gdf.geometry.geom_type.value_counts()
+
+    if len(geom_types) > 1:
+        # Convert any non-point geometries to points using centroid
+        gdf.loc[gdf.geometry.geom_type != "Point", "geometry"] = gdf[
+            gdf.geometry.geom_type != "Point"
+        ].geometry.centroid
+
+    # Verify all geometries are now points
+    if not all(gdf.geometry.geom_type == "Point"):
+        raise ValueError("Failed to convert all geometries to points")
+
+    # Limit the community gardens data to relevant columns
+    gdf = gdf[["site_name", "geometry"]]
+
+    print(f"\nTotal community gardens: {len(gdf)}")
+
+    return gdf
+
+
+def merge_and_update_with_community_gardens_gdf(
+    primary_featurelayer_gdf: gpd.GeoDataFrame, community_gardens_gdf: gpd.GeoDataFrame
+) -> gpd.GeoDataFrame:
+    """
+    Merges the community gardens GeoDataFrame with the primary feature layer GeoDataFrame and performs and masking update.
+
+    Args:
+        primary_featurelayer_gdf (gpd.GeoDataFrame): The primary feature layer GeoDataFrame.
+        community_gardens_gdf (gpd.GeoDataFrame): The community gardens GeoDataFrame.
+
+    Returns:
+        gpd.GeoDataFrame: The updated, masked primary feature layer GeoDataFrame.
+    """
+    # Use 'contains' predicate since we want the parcel that contains each point
+    joined_gdf = primary_featurelayer_gdf.sjoin(
+        community_gardens_gdf, predicate="contains", how="inner"
+    )
+
+    # Get unique parcels that contain garden points
+    garden_parcels = set(joined_gdf["opa_id"])
+    print(f"\nUnique parcels containing gardens: {len(garden_parcels)}")
+
+    if len(garden_parcels) > len(community_gardens_gdf):
+        print(
+            "\nWARNING: More matching parcels than gardens. This suggests possible data issues."
+        )
+
+    # Update vacant status for parcels containing gardens
+    mask = primary_featurelayer_gdf["opa_id"].isin(garden_parcels)
+    primary_featurelayer_gdf.loc[mask, "vacant"] = False
+
+    print(f"\nTotal parcels updated: {mask.sum()}")
+
+    return primary_featurelayer_gdf
+
+
 @provide_metadata()
 def community_gardens(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     """
@@ -39,51 +114,12 @@ def community_gardens(primary_featurelayer: FeatureLayer) -> FeatureLayer:
         name="Community Gardens", esri_rest_urls=COMMUNITY_GARDENS_TO_LOAD
     )
 
-    # Ensure both layers are in the same CRS
-    if community_gardens.gdf.crs != USE_CRS:
-        print(
-            f"Transforming community gardens from {community_gardens.gdf.crs} to {USE_CRS}"
-        )
-        community_gardens.gdf = community_gardens.gdf.to_crs(USE_CRS)
+    transformed_gdf = check_community_gardens_gdf(community_gardens.gdf)
+    community_gardens.gdf = transformed_gdf
 
-    # Identify problematic gardens
-    geom_types = community_gardens.gdf.geometry.geom_type.value_counts()
-
-    if len(geom_types) > 1:
-        # Convert any non-point geometries to points using centroid
-        community_gardens.gdf.loc[
-            community_gardens.gdf.geometry.geom_type != "Point", "geometry"
-        ] = community_gardens.gdf[
-            community_gardens.gdf.geometry.geom_type != "Point"
-        ].geometry.centroid
-
-    # Verify all geometries are now points
-    if not all(community_gardens.gdf.geometry.geom_type == "Point"):
-        raise ValueError("Failed to convert all geometries to points")
-
-    # Limit the community gardens data to relevant columns
-    community_gardens.gdf = community_gardens.gdf[["site_name", "geometry"]]
-
-    print(f"\nTotal community gardens: {len(community_gardens.gdf)}")
-
-    # Use 'contains' predicate since we want the parcel that contains each point
-    joined_gdf = primary_featurelayer.gdf.sjoin(
-        community_gardens.gdf, predicate="contains", how="inner"
+    updated_primary_featurelayer_gdf = merge_and_update_with_community_gardens_gdf(
+        primary_featurelayer.gdf, community_gardens.gdf
     )
-
-    # Get unique parcels that contain garden points
-    garden_parcels = set(joined_gdf["opa_id"])
-    print(f"\nUnique parcels containing gardens: {len(garden_parcels)}")
-
-    if len(garden_parcels) > len(community_gardens.gdf):
-        print(
-            "\nWARNING: More matching parcels than gardens. This suggests possible data issues."
-        )
-
-    # Update vacant status for parcels containing gardens
-    mask = primary_featurelayer.gdf["opa_id"].isin(garden_parcels)
-    primary_featurelayer.gdf.loc[mask, "vacant"] = False
-
-    print(f"\nTotal parcels updated: {mask.sum()}")
+    primary_featurelayer.gdf = updated_primary_featurelayer_gdf
 
     return primary_featurelayer
diff --git a/data/src/new_etl/data_utils/conservatorship.py b/data/src/new_etl/data_utils/conservatorship.py
index 559664c0..1c12c5d6 100644
--- a/data/src/new_etl/data_utils/conservatorship.py
+++ b/data/src/new_etl/data_utils/conservatorship.py
@@ -35,7 +35,7 @@ def conservatorship(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     """
     conservatorships = []
 
-    for idx, row in primary_featurelayer.gdf.iterrows():
+    for _, row in primary_featurelayer.gdf.iterrows():
         city_owner_agency = row["city_owner_agency"]
         sheriff_sale = row["sheriff_sale"] == "Y"
         market_value_over_1000 = (
diff --git a/data/src/new_etl/data_utils/council_dists.py b/data/src/new_etl/data_utils/council_dists.py
index 100c7009..3f49a7a2 100644
--- a/data/src/new_etl/data_utils/council_dists.py
+++ b/data/src/new_etl/data_utils/council_dists.py
@@ -7,6 +7,31 @@
 pd.set_option("future.no_silent_downcasting", True)
 
 
+# def merge_council_dists(
+#     primary_featurelya, council_dists_gdf: pd.DataFrame
+# ) -> pd.DataFrame:
+#     """
+#     Merges the council districts GeoDataFrame with the primary feature layer GeoDataFrame.
+
+#     Args:
+#         primary_featurelayer_gdf (pd.DataFrame): The primary feature layer GeoDataFrame.
+#         council_dists_gdf (pd.DataFrame): The council districts GeoDataFrame.
+
+#     Returns:
+#         pd.DataFrame: The updated primary feature layer GeoDataFrame with council district information.
+#     """
+#     required_columns = ["district", "geometry"]
+
+#     council_dists.gdf = council_dists.gdf[required_columns].copy()
+
+#     # Perform spatial join
+#     primary_featurelayer.spatial_join(council_dists)
+
+#     # Drop duplicates in the primary feature layer
+#     primary_featurelayer.gdf.drop_duplicates(inplace=True)
+#     primary_featurelayer.rebuild_gdf()
+
+
 @provide_metadata()
 def council_dists(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     """
diff --git a/data/src/new_etl/data_utils/delinquencies.py b/data/src/new_etl/data_utils/delinquencies.py
index 0701289a..fe18e3c8 100644
--- a/data/src/new_etl/data_utils/delinquencies.py
+++ b/data/src/new_etl/data_utils/delinquencies.py
@@ -1,8 +1,31 @@
+import geopandas as gpd
+
 from ..classes.featurelayer import FeatureLayer
 from ..constants.services import DELINQUENCIES_QUERY
 from ..metadata.metadata_utils import provide_metadata
 
 
+def transform_delinquencies_gdf(gdf: gpd.GeoDataFrame):
+    """
+    Fills missing columns regarding deliquency data in the input GeoDataFrame with necessary defaults.
+
+    Args:
+        gdf (gpd.GeoDataFrame): The input GeoDataFrame containing delinquency data.
+    """
+
+    delinquency_cols = [
+        "total_due",
+        "is_actionable",
+        "payment_agreement",
+        "num_years_owed",
+        "most_recent_year_owed",
+        "total_assessment",
+    ]
+    gdf[delinquency_cols] = gdf[delinquency_cols].fillna("NA")
+
+    gdf["sheriff_sale"] = gdf["sheriff_sale"].fillna("N")
+
+
 @provide_metadata()
 def delinquencies(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     """
@@ -55,20 +78,6 @@ def delinquencies(primary_featurelayer: FeatureLayer) -> FeatureLayer:
         "opa_number",
     )
 
-    delinquency_cols = [
-        "total_due",
-        "is_actionable",
-        "payment_agreement",
-        "num_years_owed",
-        "most_recent_year_owed",
-        "total_assessment",
-    ]
-    primary_featurelayer.gdf[delinquency_cols] = primary_featurelayer.gdf[
-        delinquency_cols
-    ].fillna("NA")
-
-    primary_featurelayer.gdf["sheriff_sale"] = primary_featurelayer.gdf[
-        "sheriff_sale"
-    ].fillna("N")
+    transform_delinquencies_gdf(primary_featurelayer.gdf)
 
     return primary_featurelayer
diff --git a/data/src/new_etl/data_utils/kde.py b/data/src/new_etl/data_utils/kde.py
index 63cb4df4..5e14ff40 100644
--- a/data/src/new_etl/data_utils/kde.py
+++ b/data/src/new_etl/data_utils/kde.py
@@ -2,6 +2,7 @@
 import numpy as np
 import rasterio
 from awkde.awkde import GaussianKDE
+from new_etl.classes.file_manager import FileManager, LoadType
 from ..classes.featurelayer import FeatureLayer
 from config.config import USE_CRS
 from rasterio.transform import Affine
@@ -12,6 +13,8 @@
 resolution = 1320  # 0.25 miles (in feet, since the CRS is 2272)
 batch_size = 100000
 
+file_manager = FileManager()
+
 
 def kde_predict_chunk(kde: GaussianKDE, chunk: np.ndarray) -> np.ndarray:
     """
@@ -90,11 +93,12 @@ def generic_kde(
 
     transform = Affine.translation(min_x, min_y) * Affine.scale(x_res, y_res)
 
-    raster_filename = f"tmp/{name.lower().replace(' ', '_')}.tif"
+    raster_filename = f"{name.lower().replace(' ', '_')}.tif"
+    raster_file_path = file_manager.get_file_path(raster_filename, LoadType.TEMP)
     print(f"Saving raster to {raster_filename}")
 
     with rasterio.open(
-        raster_filename,
+        raster_file_path,
         "w",
         driver="GTiff",
         height=zz.shape[0],
@@ -106,7 +110,7 @@ def generic_kde(
     ) as dst:
         dst.write(zz, 1)
 
-    return raster_filename, X
+    return raster_file_path, X
 
 
 def apply_kde_to_primary(
@@ -128,7 +132,7 @@ def apply_kde_to_primary(
     Returns:
         FeatureLayer: The input feature layer with added KDE-related columns.
     """
-    raster_filename, crime_coords = generic_kde(name, query, resolution)
+    raster_filename, _ = generic_kde(name, query, resolution)
 
     primary_featurelayer.gdf["centroid"] = primary_featurelayer.gdf.geometry.centroid
 
diff --git a/data/src/new_etl/data_utils/nbhoods.py b/data/src/new_etl/data_utils/nbhoods.py
index 854df31c..dc12ecbd 100644
--- a/data/src/new_etl/data_utils/nbhoods.py
+++ b/data/src/new_etl/data_utils/nbhoods.py
@@ -7,6 +7,29 @@
 from ..metadata.metadata_utils import provide_metadata
 
 
+def transform_neighborhoods_gdf(
+    neighborhoods_gdf: gpd.GeoDataFrame,
+) -> gpd.GeoDataFrame:
+    """
+    Transforms the neighborhoods GeoDataFrame in place by renaming the MAPNAME column to neighborhood
+    and retaining only it and the geometry columns.
+
+    Args:
+        gdf (gpd.GeoDataFrame): The input GeoDataFrame containing PWD parcels data.
+
+    Returns:
+        gdf (gpd.GeoDataFrame): The output, transformed GeoDataFrame.
+    """
+    if "MAPNAME" in neighborhoods_gdf.columns:
+        neighborhoods_gdf.rename(columns={"MAPNAME": "neighborhood"}, inplace=True)
+
+    neighborhoods_gdf = neighborhoods_gdf.to_crs(USE_CRS)
+
+    neighborhoods_gdf = neighborhoods_gdf[["neighborhood", "geometry"]]
+
+    return neighborhoods_gdf
+
+
 @provide_metadata()
 def nbhoods(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     """
@@ -32,20 +55,12 @@ def nbhoods(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     Source:
         https://raw.githubusercontent.com/opendataphilly/open-geo-data/master/philadelphia-neighborhoods/philadelphia-neighborhoods.geojson
     """
-    phl_nbhoods = gpd.read_file(NBHOODS_URL)
-
-    # Correct the column name to lowercase if needed
-    if "MAPNAME" in phl_nbhoods.columns:
-        phl_nbhoods.rename(columns={"MAPNAME": "neighborhood"}, inplace=True)
-
-    phl_nbhoods = phl_nbhoods.to_crs(USE_CRS)
-
-    nbhoods = FeatureLayer("Neighborhoods")
-    nbhoods.gdf = phl_nbhoods
+    neighborhoods_gdf = gpd.read_file(NBHOODS_URL)
+    neighborhoods_gdf = transform_neighborhoods_gdf(neighborhoods_gdf)
 
-    red_cols_to_keep = ["neighborhood", "geometry"]
-    nbhoods.gdf = nbhoods.gdf[red_cols_to_keep]
+    neighborhoods_feature_layer = FeatureLayer("Neighborhoods")
+    neighborhoods_feature_layer.gdf = neighborhoods_gdf
 
-    primary_featurelayer.spatial_join(nbhoods)
+    primary_featurelayer.spatial_join(neighborhoods_feature_layer)
 
     return primary_featurelayer
diff --git a/data/src/new_etl/data_utils/park_priority.py b/data/src/new_etl/data_utils/park_priority.py
index 4c47b314..8a4e2a98 100644
--- a/data/src/new_etl/data_utils/park_priority.py
+++ b/data/src/new_etl/data_utils/park_priority.py
@@ -1,10 +1,9 @@
 import os
-import zipfile
 from io import BytesIO
 from typing import List, Union
 
+import fiona
 import geopandas as gpd
-import pyogrio
 import requests
 from bs4 import BeautifulSoup
 from tqdm import tqdm
@@ -12,8 +11,11 @@
 from config.config import USE_CRS
 
 from ..classes.featurelayer import FeatureLayer
+from ..classes.file_manager import FileManager, FileType, LoadType
 from ..metadata.metadata_utils import provide_metadata
 
+file_manager = FileManager()
+
 
 def get_latest_shapefile_url() -> str:
     """
@@ -37,7 +39,7 @@ def get_latest_shapefile_url() -> str:
 
 
 def download_and_process_shapefile(
-    geojson_path: str, park_url: str, target_files: List[str], file_name_prefix: str
+    geojson_filename: str, park_url: str, target_files: List[str], file_name_prefix: str
 ) -> gpd.GeoDataFrame:
     """
     Downloads and processes the shapefile to create a GeoDataFrame for Philadelphia parks.
@@ -51,38 +53,61 @@ def download_and_process_shapefile(
     Returns:
         gpd.GeoDataFrame: GeoDataFrame containing the processed park data.
     """
-    print("Downloading and processing park priority data...")
-    response: requests.Response = requests.get(park_url, stream=True)
-    total_size: int = int(response.headers.get("content-length", 0))
-
-    with tqdm(
-        total=total_size, unit="iB", unit_scale=True, desc="Downloading"
-    ) as progress_bar:
-        buffer: BytesIO = BytesIO()
-        for data in response.iter_content(1024):
-            size: int = buffer.write(data)
-            progress_bar.update(size)
-
-    with zipfile.ZipFile(buffer) as zip_ref:
-        for file_name in tqdm(target_files, desc="Extracting"):
-            zip_ref.extract(file_name, "tmp/")
+    target_files_paths = [
+        file_manager.get_file_path(filename, LoadType.TEMP) for filename in target_files
+    ]
+    if any([not os.path.exists(filepath) for filepath in target_files_paths]):
+        print("Downloading and processing park priority data...")
+        response: requests.Response = requests.get(park_url, stream=True)
+        total_size: int = int(response.headers.get("content-length", 0))
+
+        with tqdm(
+            total=total_size, unit="iB", unit_scale=True, desc="Downloading"
+        ) as progress_bar:
+            buffer: BytesIO = BytesIO()
+            for data in response.iter_content(1024):
+                size: int = buffer.write(data)
+                progress_bar.update(size)
+
+        print("Extracting files from the downloaded zip...")
+        file_manager.extract_files(buffer, target_files)
+
+    else:
+        print("Parks data already located in filesystem - proceeding")
 
     print("Processing shapefile...")
-    pa_parks: gpd.GeoDataFrame = gpd.read_file(
-        "tmp/" + file_name_prefix + "_ParkPriorityAreas.shp"
+
+    def filter_shapefile_generator():
+        file_path = file_manager.get_file_path(
+            file_name_prefix + "_ParkPriorityAreas.shp", LoadType.TEMP
+        )
+
+        with fiona.open(file_path) as source:
+            for feature in source:
+                if not feature["properties"]["ID"].startswith("42101"):
+                    continue
+                filtered_feature = feature
+                filtered_feature["properties"] = {
+                    column: value
+                    for column, value in feature["properties"].items()
+                    if column in ["ParkNeed"]
+                }
+                yield filtered_feature
+
+    phl_parks: gpd.GeoDataFrame = gpd.GeoDataFrame.from_features(
+        filter_shapefile_generator()
     )
-    pa_parks = pa_parks.to_crs(USE_CRS)
 
-    phl_parks: gpd.GeoDataFrame = pa_parks[pa_parks["ID"].str.startswith("42101")]
-    phl_parks = phl_parks.loc[:, ["ParkNeed", "geometry"]]
+    phl_parks.crs = USE_CRS
+    phl_parks = phl_parks.to_crs(USE_CRS)
 
     if isinstance(phl_parks, gpd.GeoDataFrame):
         phl_parks.rename(columns={"ParkNeed": "park_priority"}, inplace=True)
     else:
         raise TypeError("Expected a GeoDataFrame, got Series or another type instead")
 
-    print(f"Writing filtered data to GeoJSON: {geojson_path}")
-    phl_parks.to_file(geojson_path, driver="GeoJSON")
+    print(f"Writing filtered data to GeoJSON: {geojson_filename}")
+    file_manager.save_gdf(geojson_filename, LoadType.TEMP, FileType.GEOJSON)
 
     return phl_parks
 
@@ -123,25 +148,16 @@ def park_priority(primary_featurelayer: FeatureLayer) -> FeatureLayer:
         file_name_prefix + "_ParkPriorityAreas.sbn",
         file_name_prefix + "_ParkPriorityAreas.sbx",
     ]
-    geojson_path: str = "tmp/phl_parks.geojson"
-
-    os.makedirs("tmp/", exist_ok=True)
+    geojson_filename = "phl_parks"
 
     try:
-        if os.path.exists(geojson_path):
-            print(f"GeoJSON file already exists, loading from {geojson_path}")
-            phl_parks: gpd.GeoDataFrame = gpd.read_file(geojson_path)
-        else:
-            raise pyogrio.errors.DataSourceError(
-                "GeoJSON file missing, forcing download."
-            )
-
-    except (pyogrio.errors.DataSourceError, ValueError) as e:
+        phl_parks = file_manager.load_gdf(
+            geojson_filename, FileType.GEOJSON, LoadType.TEMP
+        )
+    except FileNotFoundError as e:
         print(f"Error loading GeoJSON: {e}. Re-downloading and processing shapefile.")
-        if os.path.exists(geojson_path):
-            os.remove(geojson_path)  # Delete the corrupted GeoJSON if it exists
         phl_parks = download_and_process_shapefile(
-            geojson_path, park_url, target_files, file_name_prefix
+            geojson_filename, park_url, target_files, file_name_prefix
         )
 
     park_priority_layer: FeatureLayer = FeatureLayer("Park Priority")
diff --git a/data/src/new_etl/data_utils/priority_level.py b/data/src/new_etl/data_utils/priority_level.py
index 90e29da3..08abf004 100644
--- a/data/src/new_etl/data_utils/priority_level.py
+++ b/data/src/new_etl/data_utils/priority_level.py
@@ -29,7 +29,7 @@ def priority_level(dataset: FeatureLayer) -> FeatureLayer:
         tree_canopy_gap, phs_care_program columns in the primary feature layer.
     """
     priority_levels = []
-    for idx, row in dataset.gdf.iterrows():
+    for _, row in dataset.gdf.iterrows():
         priority_level = ""
 
         # Decision Points
diff --git a/data/src/new_etl/data_utils/rco_geoms.py b/data/src/new_etl/data_utils/rco_geoms.py
index 3b294656..d63d6a3f 100644
--- a/data/src/new_etl/data_utils/rco_geoms.py
+++ b/data/src/new_etl/data_utils/rco_geoms.py
@@ -1,4 +1,6 @@
+from typing import List
 import pandas as pd
+import geopandas as gpd
 
 from ..classes.featurelayer import FeatureLayer
 from ..constants.services import RCOS_LAYERS_TO_LOAD
@@ -7,6 +9,53 @@
 pd.set_option("future.no_silent_downcasting", True)
 
 
+def transform_rco_geoms_gdf(
+    rco_geoms_gdf: gpd.GeoDataFrame,
+    aggregate_columns: List[str],
+    output_columns: List[str],
+) -> gpd.GeoDataFrame:
+    # Aggregate RCO information into a single column
+    rco_geoms_gdf["rco_info"] = rco_geoms_gdf[aggregate_columns].apply(
+        lambda x: "; ".join(map(str, x)), axis=1
+    )
+
+    rco_geoms_gdf.rename(columns={"organization_name": "rco_names"}, inplace=True)
+    rco_geoms_gdf = rco_geoms_gdf[output_columns].copy()
+
+    return rco_geoms_gdf
+
+
+def transform_merged_rco_geoms_gdf(
+    primary_featurelayer_gdf: gpd.GeoDataFrame, rco_columns: List[str]
+) -> gpd.GeoDataFrame:
+    group_columns = [
+        col for col in primary_featurelayer_gdf.columns if col not in rco_columns
+    ]
+
+    # 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
+    transformed_gdf = (
+        primary_featurelayer_gdf.groupby(group_columns)
+        .agg(
+            {
+                "rco_info": lambda x: "|".join(map(str, x)),
+                "rco_names": lambda x: "|".join(map(str, x)),
+                "geometry": "first",
+            }
+        )
+        .reset_index()
+    )
+
+    transformed_gdf.drop_duplicates(inplace=True)
+
+    return transformed_gdf
+
+
 @provide_metadata()
 def rco_geoms(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     """
@@ -47,43 +96,19 @@ def rco_geoms(primary_featurelayer: FeatureLayer) -> 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
+    rco_geoms_gdf = transform_rco_geoms_gdf(
+        rco_geoms.gdf, rco_aggregate_cols, rco_use_cols
     )
-
-    rco_geoms.gdf["rco_names"] = rco_geoms.gdf["organization_name"]
-
-    rco_geoms.gdf = rco_geoms.gdf[rco_use_cols].copy()
+    rco_geoms.gdf = rco_geoms_gdf
     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(
-            {
-                "rco_info": lambda x: "|".join(map(str, x)),
-                "rco_names": lambda x: "|".join(map(str, x)),
-                "geometry": "first",
-            }
-        )
-        .reset_index()
+    transformed_gdf = transform_merged_rco_geoms_gdf(
+        primary_featurelayer.gdf, rco_use_cols
     )
-
-    primary_featurelayer.gdf.drop_duplicates(inplace=True)
+    primary_featurelayer.gdf = transformed_gdf
     primary_featurelayer.rebuild_gdf()
 
     return primary_featurelayer
diff --git a/data/src/new_etl/data_utils/tactical_urbanism.py b/data/src/new_etl/data_utils/tactical_urbanism.py
index e862a2c3..25467b6c 100644
--- a/data/src/new_etl/data_utils/tactical_urbanism.py
+++ b/data/src/new_etl/data_utils/tactical_urbanism.py
@@ -27,7 +27,7 @@ def tactical_urbanism(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     """
     tactical_urbanism_values = []
 
-    for idx, row in primary_featurelayer.gdf.iterrows():
+    for _, row in primary_featurelayer.gdf.iterrows():
         if (
             row["parcel_type"] == "Land"
             and row["unsafe_building"] == "N"
diff --git a/data/src/new_etl/data_utils/tree_canopy.py b/data/src/new_etl/data_utils/tree_canopy.py
index 8b5632ec..b98d0fe5 100644
--- a/data/src/new_etl/data_utils/tree_canopy.py
+++ b/data/src/new_etl/data_utils/tree_canopy.py
@@ -5,10 +5,13 @@
 import requests
 
 from config.config import USE_CRS
+from new_etl.classes.file_manager import FileManager
 
 from ..classes.featurelayer import FeatureLayer
 from ..metadata.metadata_utils import provide_metadata
 
+file_manager = FileManager()
+
 
 @provide_metadata()
 def tree_canopy(primary_featurelayer: FeatureLayer) -> FeatureLayer:
@@ -44,10 +47,10 @@ def tree_canopy(primary_featurelayer: FeatureLayer) -> FeatureLayer:
 
     with io.BytesIO(tree_response.content) as f:
         with zipfile.ZipFile(f, "r") as zip_ref:
-            zip_ref.extractall("tmp/")
+            zip_ref.extractall("storage/temp")
 
     # Load and process the tree canopy shapefile
-    pa_trees = gpd.read_file("tmp/pa.shp")
+    pa_trees = gpd.read_file("storage/temp/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"]]
diff --git a/data/src/new_etl/data_utils/vacant_properties.py b/data/src/new_etl/data_utils/vacant_properties.py
index 9b4b56f0..83d85322 100644
--- a/data/src/new_etl/data_utils/vacant_properties.py
+++ b/data/src/new_etl/data_utils/vacant_properties.py
@@ -8,7 +8,7 @@
 from ..metadata.metadata_utils import provide_metadata
 
 
-def load_backup_data_from_gcs(file_name: str) -> pd.DataFrame:
+def load_backup_data_from_gcs(file_name: str) -> pd.DataFrame | None:
     """
     Loads backup data from Google Cloud Storage as a DataFrame, ensuring compatibility for matching.
 
@@ -19,10 +19,12 @@ def load_backup_data_from_gcs(file_name: str) -> pd.DataFrame:
         pd.DataFrame: A DataFrame containing the backup data with only the "opa_id" column.
     """
     bucket = google_cloud_bucket()
+    if not bucket:
+        print("No Google Cloud bucket available - skipping backup data load.")
+        return None
     blob = bucket.blob(file_name)
     if not blob.exists():
         raise FileNotFoundError(f"File {file_name} not found in the GCS bucket.")
-
     file_bytes = blob.download_as_bytes()
     try:
         gdf = gpd.read_file(BytesIO(file_bytes))
@@ -97,25 +99,26 @@ def vacant_properties(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     print(f"Vacant land data size in the default dataset: {len(vacant_land_gdf)} rows.")
 
     # 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."
-        )
-        vacant_properties.gdf = vacant_properties.gdf[
-            vacant_properties.gdf["parcel_type"] != "Land"
-        ]
-
-        # Load backup data
-        backup_gdf = load_backup_data_from_gcs("vacant_indicators_land_06_2024.geojson")
-
-        # Add parcel_type column to backup data
-        backup_gdf["parcel_type"] = "Land"
-
-        # 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
-        )
+    # if len(vacant_land_gdf) < 20000:
+    #     print(
+    #         "Vacant land data is below the threshold. Removing vacant land rows and loading backup data from GCS."
+    #     )
+    #     vacant_properties.gdf = vacant_properties.gdf[
+    #         vacant_properties.gdf["parcel_type"] != "Land"
+    #     ]
+
+    #     # Attempt to load backup data from GCS
+    #     backup_gdf = load_backup_data_from_gcs("vacant_indicators_land_06_2024.geojson")
+
+    #     if backup_gdf:
+    #         # Add parcel_type column to backup data
+    #         backup_gdf["parcel_type"] = "Land"
+
+    #         # 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
+    #         )
 
     # Convert to a regular DataFrame by dropping geometry
     df = vacant_properties.gdf.drop(columns=["geometry"], errors="ignore")
diff --git a/data/src/new_etl/database.py b/data/src/new_etl/database.py
deleted file mode 100644
index a8bffb40..00000000
--- a/data/src/new_etl/database.py
+++ /dev/null
@@ -1,134 +0,0 @@
-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():
-            # 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/streetview.py b/data/src/streetview.py
index cd35763b..6e31c356 100644
--- a/data/src/streetview.py
+++ b/data/src/streetview.py
@@ -56,7 +56,7 @@ def upload_image_with_metadata(blob, image_content, metadata):
 print(f"Found {len(properties)} images to fetch")
 
 
-for idx, row in properties.iterrows():
+for _, row in properties.iterrows():
     opa_id = row["opa_id"]
     file_name = f"{opa_id}.jpg"
     blob = bucket.blob(file_name)
diff --git a/data/src/test/data_utils/__init__.py b/data/src/test/data_utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/data/src/test/data_utils/test_access_process.py b/data/src/test/data_utils/test_access_process.py
new file mode 100644
index 00000000..481eaec7
--- /dev/null
+++ b/data/src/test/data_utils/test_access_process.py
@@ -0,0 +1,43 @@
+import unittest
+
+import geopandas as gpd
+
+from new_etl.classes.featurelayer import FeatureLayer
+from new_etl.data_utils.access_process import access_process
+
+
+class TestAccessProcess(unittest.TestCase):
+    def test_access_process(self):
+        """
+        Test the functionality of the access_process service that generates a new column for the dataset
+        on the basis of existing values in the corresponding row for city_owner_agency and market_value.
+        """
+        # Create test data
+        data = {
+            "city_owner_agency": [
+                "Land Bank (PHDC)",
+                "PRA",
+                None,
+                "City of Philadelphia",
+                None,
+            ],
+            "market_value": [1500, 500, 2000, None, None],
+        }
+        gdf = gpd.GeoDataFrame(data)
+        feature_layer = FeatureLayer(name="test", gdf=gdf)
+
+        access_process_feature_layer = access_process(feature_layer)
+        expected_gdf = gdf.copy()
+        expected_gdf["access_process"] = [
+            "Go through Land Bank",
+            "Do Nothing",
+            "Private Land Use Agreement",
+            "Buy Property",
+            "Buy Property",
+        ]
+
+        assert expected_gdf.equals(access_process_feature_layer.gdf)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_city_owned_properties.py b/data/src/test/data_utils/test_city_owned_properties.py
new file mode 100644
index 00000000..d2c42262
--- /dev/null
+++ b/data/src/test/data_utils/test_city_owned_properties.py
@@ -0,0 +1,61 @@
+import unittest
+import geopandas as gpd
+
+from new_etl.data_utils.city_owned_properties import transform_city_owned_properties
+
+
+class TestCityOwnedProperties(unittest.TestCase):
+    def test_transform_city_owned_properties(self):
+        """
+        This tests that the transform_city_owned_properties function correctly
+        transforms the city-owned properties data by renaming columns and updating
+        access information for properties based on ownership.
+        """
+
+        # Sample data
+        data = {
+            "owner_1": [
+                "PHILADELPHIA HOUSING AUTH",
+                "PHILADELPHIA LAND BANK",
+                "CITY OF PHILA",
+                "REDEVELOPMENT AUTHORITY",
+            ],
+            "owner_2": ["PUBLIC PROP", "PUBLC PROP", "PUBLIC PROP", None],
+            "agency": [
+                "PHILADELPHIA HOUSING AUTH",
+                "PLB",
+                "CITY OF PHILA",
+                "REDEVELOPMENT AUTHORITY",
+            ],
+            "sideyardeligible": [None, "Yes", None, "No"],
+        }
+
+        gdf = gpd.GeoDataFrame(data)
+
+        # Call the function under test
+        transform_city_owned_properties(gdf)
+
+        expected_gdf = gpd.GeoDataFrame(
+            {
+                "owner_1": [
+                    "PHILADELPHIA HOUSING AUTH",
+                    "PHILADELPHIA LAND BANK",
+                    "CITY OF PHILA",
+                    "REDEVELOPMENT AUTHORITY",
+                ],
+                "owner_2": ["PUBLIC PROP", "PUBLC PROP", "PUBLIC PROP", None],
+                "city_owner_agency": [
+                    "PHA",
+                    "Land Bank (PHDC)",
+                    "DPP",
+                    "PRA",
+                ],
+                "side_yard_eligible": ["No", "Yes", "No", "No"],
+            }
+        )
+
+        assert expected_gdf.equals(gdf)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_community_gardens.py b/data/src/test/data_utils/test_community_gardens.py
new file mode 100644
index 00000000..8ebe0697
--- /dev/null
+++ b/data/src/test/data_utils/test_community_gardens.py
@@ -0,0 +1,77 @@
+import unittest
+import geopandas as gpd
+from shapely import Point, Polygon
+
+from config.config import USE_CRS
+from new_etl.data_utils.community_gardens import (
+    check_community_gardens_gdf,
+    merge_and_update_with_community_gardens_gdf,
+)
+
+
+class TestCityOwnedProperties(unittest.TestCase):
+    def test_check_community_gardens_gdf(self):
+        """
+        This tests that the check_community_gardens_gdf function correctly transforms
+        non-point geometries to points using centroid and ensures all geometries are valid.
+        """
+
+        # Create a sample GeoDataFrame with mixed geometry types
+        data = {
+            "site_name": ["Garden A", "Garden B", "Garden C"],
+            "zip_code": ["19103", "19104", "19105"],
+            "geometry": [
+                Point(0, 0),
+                Point(1, 1),
+                Point(2, 2),
+            ],
+        }
+        gdf = gpd.GeoDataFrame(data, geometry="geometry", crs="EPSG:4326")
+
+        transformed_gdf = check_community_gardens_gdf(gdf)
+
+        self.assertTrue(all(transformed_gdf.geometry.geom_type == "Point"))
+        self.assertEqual(transformed_gdf.crs, USE_CRS)
+        self.assertEqual(transformed_gdf.columns.tolist(), ["site_name", "geometry"])
+
+    def test_merge_community_gardens_gdf(self):
+        """
+        This tests that the merge_community_gardens_gdf function correctly merges
+        the community gardens GeoDataFrame with the primary feature layer GeoDataFrame and updates its values.
+        """
+
+        # Create a sample primary feature layer GeoDataFrame
+        primary_data = {
+            "opa_id": ["0100", "0101", "0102"],
+            "vacant": [True, False, True],
+            "geometry": [
+                Polygon([[0, 0], [1, 0], [1, 1], [0, 1]]),
+                Polygon([[2, 2], [1, 0], [2, 0], [3, 3]]),
+                Polygon([[1, 1], [0, 2], [1, 2]]),
+            ],
+        }
+        primary_gdf = gpd.GeoDataFrame(
+            primary_data, geometry="geometry", crs="EPSG:4326"
+        )
+
+        # Create a sample community gardens GeoDataFrame
+        community_data = {
+            "site_name": ["Garden A", "Garden B"],
+            "geometry": [
+                Point(0.5, 0.5),
+                Point(3, 3),
+            ],
+        }
+        community_gardens_gdf = gpd.GeoDataFrame(
+            community_data, geometry="geometry", crs="EPSG:4326"
+        )
+
+        updated_primary_gdf = merge_and_update_with_community_gardens_gdf(
+            primary_gdf, community_gardens_gdf
+        )
+
+        self.assertEqual(updated_primary_gdf["vacant"].tolist(), [False, False, True])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_conservatorship.py b/data/src/test/data_utils/test_conservatorship.py
new file mode 100644
index 00000000..fedae937
--- /dev/null
+++ b/data/src/test/data_utils/test_conservatorship.py
@@ -0,0 +1,35 @@
+import unittest
+
+import geopandas as gpd
+
+from new_etl.classes.featurelayer import FeatureLayer
+from new_etl.data_utils.conservatorship import conservatorship
+
+
+class TestConservatorship(unittest.TestCase):
+    def test_conservatorship(self):
+        data = {
+            "city_owner_agency": [
+                "Land Bank (PHDC)",
+                "PRA",
+                None,
+                "City of Philadelphia",
+                "PRA",
+            ],
+            "sheriff_sale": ["Y", "N", "N", "N", "Y"],
+            "market_value": [2000, 500, 800, 200, 1000],
+            "all_violations_past_year": [5, 0, 2, 1, None],
+            "sale_date": ["2020-01-01", "2021-03-10", "2024-09-30", "2004-09-10", None],
+        }
+        gdf = gpd.GeoDataFrame(data)
+        feature_layer = FeatureLayer(name="test", gdf=gdf)
+
+        conservatorship_feature_layer = conservatorship(feature_layer)
+        expected_gdf = gdf.copy()
+        expected_gdf["conservatorship"] = ["No", "No", "Yes", "Yes", "No"]
+
+        assert expected_gdf.equals(conservatorship_feature_layer.gdf)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_contiguous_neighbors.py b/data/src/test/data_utils/test_contiguous_neighbors.py
new file mode 100644
index 00000000..24fee869
--- /dev/null
+++ b/data/src/test/data_utils/test_contiguous_neighbors.py
@@ -0,0 +1,15 @@
+import unittest
+
+import geopandas as gpd
+
+from new_etl.classes.featurelayer import FeatureLayer
+from new_etl.data_utils.contig_neighbors import contig_neighbors
+
+
+class TestContiguousNeighbors(unittest.TestCase):
+    def test_contiguous_neighbors(self):
+        pass
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_council_dists.py b/data/src/test/data_utils/test_council_dists.py
new file mode 100644
index 00000000..44dd4168
--- /dev/null
+++ b/data/src/test/data_utils/test_council_dists.py
@@ -0,0 +1,14 @@
+import unittest
+
+import geopandas as gpd
+
+from new_etl.data_utils import council_dists
+
+
+class TestCouncilDists(unittest.TestCase):
+    def test_council_dists(self):
+        pass
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_delinquencies.py b/data/src/test/data_utils/test_delinquencies.py
new file mode 100644
index 00000000..16edcfc4
--- /dev/null
+++ b/data/src/test/data_utils/test_delinquencies.py
@@ -0,0 +1,44 @@
+import unittest
+
+import geopandas as gpd
+
+from new_etl.data_utils.delinquencies import transform_delinquencies_gdf
+
+
+class TestCouncilDists(unittest.TestCase):
+    def test_transform_delinquencies_gdf(self):
+        """
+        This tests that the relevant deliquency data columns which are missing are filled with the necessary defaults.
+        """
+
+        data = {
+            "total_due": [100, None, 200],
+            "is_actionable": [None, "Y", "N"],
+            "payment_agreement": ["N", None, "Y"],
+            "num_years_owed": [1, 2, None],
+            "most_recent_year_owed": [None, 2020, 2021],
+            "sheriff_sale": ["N", None, "Y"],
+            "total_assessment": [300, 400, None],
+        }
+        gdf = gpd.GeoDataFrame(data)
+
+        expected_data = {
+            "total_due": [100, "NA", 200],
+            "is_actionable": ["NA", "Y", "N"],
+            "payment_agreement": ["N", "NA", "Y"],
+            "num_years_owed": [1, 2, "NA"],
+            "most_recent_year_owed": ["NA", 2020, 2021],
+            "sheriff_sale": ["N", "N", "Y"],
+            "total_assessment": [300, 400, "NA"],
+        }
+        expected_gdf = gpd.GeoDataFrame(expected_data)
+
+        transform_delinquencies_gdf(gdf)
+
+        assert gdf.equals(expected_gdf), (
+            "The transformed GeoDataFrame does not match the expected output."
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_negligent_devs.py b/data/src/test/data_utils/test_negligent_devs.py
new file mode 100644
index 00000000..1f1d224d
--- /dev/null
+++ b/data/src/test/data_utils/test_negligent_devs.py
@@ -0,0 +1,52 @@
+import unittest
+
+import geopandas as gpd
+
+from new_etl.classes.featurelayer import FeatureLayer
+from new_etl.data_utils.negligent_devs import negligent_devs
+
+
+class TestNegligentDevs(unittest.TestCase):
+    def test_negligent_devs(self):
+        """
+        Test the functionality of the negligent_devs service that generates new columns on the dataset relating to the number of properties owned
+        and vacant properties owned under one standardized address as well as a classification of the owner type as negligent based on the vacant number.
+        """
+        data = {
+            "opa_id": ["0011", "0012", "0013", "0014", "0015", "0000", "0001", "0032"],
+            "vacant": [True, True, True, True, True, False, True, True],
+            "city_owner_agency": [
+                None,
+                None,
+                None,
+                None,
+                None,
+                "City of Philadelphia",
+                "City of Philadelphia",
+                "PRA",
+            ],
+            "standardized_address": ["1", "1", "1", "1", "1", "6", "6", "8"],
+        }
+        gdf = gpd.GeoDataFrame(data)
+        feature_layer = FeatureLayer(name="test", gdf=gdf)
+        # need to add primary merge data
+        negligent_devs_feature_layer = negligent_devs(feature_layer)
+        expected_gdf = gdf.copy()
+        expected_gdf["n_total_properties_owned"] = [5, 5, 5, 5, 5, 2, 2, 1]
+        expected_gdf["n_vacant_properties_owned"] = [5, 5, 5, 5, 5, 1, 1, 1]
+        expected_gdf["negligent_dev"] = [
+            True,
+            True,
+            True,
+            True,
+            True,
+            False,
+            False,
+            False,
+        ]
+
+        assert expected_gdf.equals(negligent_devs_feature_layer.gdf)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_neighborhoods.py b/data/src/test/data_utils/test_neighborhoods.py
new file mode 100644
index 00000000..540e3e28
--- /dev/null
+++ b/data/src/test/data_utils/test_neighborhoods.py
@@ -0,0 +1,43 @@
+import unittest
+
+import geopandas as gpd
+from shapely.geometry import Polygon
+
+from config.config import USE_CRS
+from new_etl.data_utils.nbhoods import transform_neighborhoods_gdf
+
+
+class TestNeighborhoods(unittest.TestCase):
+    def test_transform_neighborhoods_gdf(self):
+        data = {
+            "MAPNAME": ["Callowhill", "Bartram's Garden", "Fishtown"],
+            "removed_field": ["First", "Second", "Third"],
+            "geometry": [
+                Polygon([(0, 0), (1, 0), (1, 1)]),
+                Polygon([(3, 1), (3, 5), (0, 2), (0, 0)]),
+                Polygon([(2, 2), (2, 1), (0, 0)]),
+            ],
+        }
+        gdf = gpd.GeoDataFrame()
+        gdf = gpd.GeoDataFrame(data, geometry="geometry")
+
+        # Run transformation which mutates result in place
+        result = gdf.copy()
+        result.crs = USE_CRS
+        transformed_gdf = transform_neighborhoods_gdf(result)
+
+        self.assertEquals(
+            ["neighborhood", "geometry"], transformed_gdf.columns.tolist()
+        )
+        self.assertEquals(
+            transformed_gdf["neighborhood"].tolist(),
+            ["Callowhill", "Bartram's Garden", "Fishtown"],
+        )
+        self.assertTrue(all(transformed_gdf.geometry.is_valid))
+        self.assertTrue(
+            all(transformed_gdf.geometry.type.isin(["Polygon", "Multipolygon"]))
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_owner_type.py b/data/src/test/data_utils/test_owner_type.py
new file mode 100644
index 00000000..568c1f46
--- /dev/null
+++ b/data/src/test/data_utils/test_owner_type.py
@@ -0,0 +1,36 @@
+import unittest
+
+import geopandas as gpd
+
+from new_etl.classes.featurelayer import FeatureLayer
+from new_etl.data_utils.owner_type import owner_type
+
+
+class TestOwnerType(unittest.TestCase):
+    def test_owner_type(self):
+        """
+        Test the functionality of the owner_type service that generates a new column for the dataset
+        on the basis of existing ownership and opa_id data in the corresponding row.
+        """
+        data = {
+            "owner_1": ["John Smith", None, "Costco llc", "Jeff Roe"],
+            "owner_2": ["Jane Doe", "Tom Hink", "Sixers", "Jefferson llc"],
+            "city_owner_agency": [None, "City of Philadelphia", "PRA", None],
+        }
+        gdf = gpd.GeoDataFrame(data)
+        feature_layer = FeatureLayer(name="test", gdf=gdf)
+
+        owner_type_feature_layer = owner_type(feature_layer)
+        expected_gdf = gdf.copy()
+        expected_gdf["owner_type"] = [
+            "Individual",
+            "Public",
+            "Public",
+            "Business (LLC)",
+        ]
+
+        assert expected_gdf.equals(owner_type_feature_layer.gdf)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_priority_level.py b/data/src/test/data_utils/test_priority_level.py
new file mode 100644
index 00000000..69bce4be
--- /dev/null
+++ b/data/src/test/data_utils/test_priority_level.py
@@ -0,0 +1,48 @@
+import unittest
+
+import geopandas as gpd
+
+from new_etl.classes.featurelayer import FeatureLayer
+from new_etl.data_utils.priority_level import priority_level
+
+
+class TestPriorityLevel(unittest.TestCase):
+    def test_priority_level(self):
+        pass
+        """
+            Test the functionality of the priority_level service that generates a new column for the 
+            dataset on the basis of some branching logic in existing column values in the corresponding row 
+            (see priority_level function for more details).
+            """
+        data = {
+            "gun_crimes_density_zscore": [-0.5, 1.1, 0.5, 2.1, 0.4],
+            "all_violations_past_year": [3, 2, 0, 0, 5],
+            "l_and_i_complaints_density_zscore": [0, 0.1, 2.1, -1, 0.3],
+            "tree_canopy_gap": [0.1, 0, 0.4, 0.5, 0.2],
+            "phs_care_program": [
+                None,
+                "Trees for Watersheds",
+                "Rain Check",
+                "Trees for Watersheds",
+                None,
+            ],
+        }
+
+        gdf = gpd.GeoDataFrame(data)
+        feature_layer = FeatureLayer(name="test", gdf=gdf)
+
+        priority_level_feature_layer = priority_level(feature_layer)
+        expected_gdf = gdf.copy()
+        expected_gdf["priority_level"] = [
+            "Low",
+            "High",
+            "Medium",
+            "High",
+            "Medium",
+        ]
+
+        assert expected_gdf.equals(priority_level_feature_layer.gdf)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_pwd_parcels.py b/data/src/test/data_utils/test_pwd_parcels.py
new file mode 100644
index 00000000..9077eb11
--- /dev/null
+++ b/data/src/test/data_utils/test_pwd_parcels.py
@@ -0,0 +1,134 @@
+import unittest
+
+import numpy as np
+import geopandas as gpd
+from shapely.geometry import Point, Polygon, MultiPolygon, LineString
+
+from new_etl.data_utils.pwd_parcels import (
+    merge_pwd_parcels_gdf,
+    transform_pwd_parcels_gdf,
+)
+
+
+class TestPwcParcels(unittest.TestCase):
+    def test_pwd_parcels_merge(self):
+        """
+        This tests that the merge_pwd_parcels_gdf function correctly retains
+        existing point geometries in the primary GeoDataFrame when no better
+        geometry is available in the PWD parcels GeoDataFrame.
+        """
+
+        parcel_type_sample_data = ["Land", "Building", "Land", "Building"]
+        primary_data = {
+            "opa_id": ["0100", "0101", "0102", "0103"],
+            "geometry": [Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3)],
+            "parcel_type": parcel_type_sample_data,
+        }
+
+        # CRS we don't use is chosen since we are just checking it doesn't change:
+        test_crs = "EPSG:2002"
+        primary_gdf = gpd.GeoDataFrame(primary_data, geometry="geometry", crs=test_crs)
+
+        # Simulate dropped rows by having a gapped index.
+        primary_gdf.index = [0, 2, 3, 5]
+
+        # Create pwd_parcels_gdf with a continuous index.
+        pwd_data = {
+            "opa_id": ["0100", "0101", "0102", "0103"],
+            "geometry": [
+                np.nan,  # For opa 0100, no better geometry
+                MultiPolygon(
+                    [Polygon([(10, 10), (20, 10), (20, 20), (10, 20)])]
+                ),  # For opa 0101, a multipolygon available
+                np.nan,
+                np.nan,
+            ],
+        }
+        pwd_gdf = gpd.GeoDataFrame(pwd_data, geometry="geometry")
+        assert list(pwd_gdf.index) == list(range(4))  # Continuous index: 0,1,2,3
+
+        # Call the merge function under test.
+        merged_gdf = merge_pwd_parcels_gdf(primary_gdf, pwd_gdf)
+
+        # Expected geometries:
+        # For opa_id "0010", we expect the multipolygon from pwd_gdf.
+        # For the other opa_id's we expect the point geometries from primary_gdf.
+        expected_geometries = {
+            "0100": primary_gdf.loc[0, "geometry"],
+            "0101": pwd_gdf.loc[1, "geometry"],  # pwd geometry used when available
+            "0102": primary_gdf.loc[3, "geometry"],
+            "0103": primary_gdf.loc[5, "geometry"],
+        }
+
+        assert merged_gdf.crs == primary_gdf.crs
+        assert merged_gdf.crs == test_crs
+
+        # Verify each row of the merged GeoDataFrame.
+        for _, row in merged_gdf.iterrows():
+            opa_id = row["opa_id"]
+            actual_geom = row["geometry"]
+            expected_geom = expected_geometries[opa_id]
+            # Use shapely's equals() to check geometry equivalence.
+            assert actual_geom.equals(expected_geom), (
+                f"Mismatch for opa_id {opa_id}: "
+                f"expected {expected_geom}, got {actual_geom}"
+            )
+
+        opa_id_values = ["0100", "0101", "0102", "0103"]
+        data = {
+            "opa_id": opa_id_values,
+            "geometry": [expected_geometries[opa_id] for opa_id in opa_id_values],
+            "parcel_type": parcel_type_sample_data,
+        }
+        expected_df = gpd.GeoDataFrame(
+            data=data,
+            geometry="geometry",
+        )
+
+        assert expected_df.equals(merged_gdf)
+
+    def test_transform_pwd_parcels_gdf_basic(self):
+        """
+        Check the basic functionality of the transform_pwd_parcels_gdf function.
+        This function is expected to mutate the input GeoDataFrame in place.
+        It should drop rows with null 'brt_id', rename 'brt_id' to 'opa_id',
+        and ensure all geometries are valid polygons or multipolygons.
+        """
+        # Create test data
+        data = {
+            "brt_id": [None, "1234", "5678"],
+            "geometry": [
+                Polygon(
+                    [(0, 0), (1, 0), (1, 1), (0, 1)]
+                ),  # Will be dropped (null brt_id)
+                Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]),  # Valid polygon
+                LineString([(0, 0), (1, 1)]),  # Invalid type (not a polygon)
+            ],
+        }
+        gdf = gpd.GeoDataFrame()
+        gdf = gpd.GeoDataFrame(data, geometry="geometry")
+
+        # Expect a ValueError because LineString is not allowed
+        with self.assertRaises(ValueError) as context:
+            transform_pwd_parcels_gdf(gdf.copy())
+
+        self.assertIn("not polygons or multipolygons", str(context.exception))
+
+        # Now fix the invalid geometry to test normal flow
+        gdf.loc[2, "geometry"] = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])
+
+        # Run transformation which mutates result in place
+        result = gdf.copy()
+        transform_pwd_parcels_gdf(result)
+
+        # Check the rows were filtered
+        self.assertEqual(len(result), 2)  # One row was dropped
+        self.assertNotIn("brt_id", result.columns)
+        self.assertIn("opa_id", result.columns)
+        self.assertListEqual(sorted(result["opa_id"].tolist()), ["1234", "5678"])
+        self.assertTrue(all(result.geometry.is_valid))
+        self.assertTrue(all(result.geometry.type.isin(["Polygon", "MultiPolygon"])))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_rco_geoms.py b/data/src/test/data_utils/test_rco_geoms.py
new file mode 100644
index 00000000..fc5434fc
--- /dev/null
+++ b/data/src/test/data_utils/test_rco_geoms.py
@@ -0,0 +1,76 @@
+import unittest
+
+import geopandas as gpd
+from shapely import Polygon
+
+from new_etl.data_utils.rco_geoms import (
+    transform_rco_geoms_gdf,
+    transform_merged_rco_geoms_gdf,
+)
+
+
+class TestRcoGeoms(unittest.TestCase):
+    def test_transform_rco_geoms_gdf(self):
+        data = {
+            "organization_name": ["Org 1", "Org 2", "Org 3"],
+            "organization_address": ["31", "34", "56"],
+            "primary_email": ["org1@gmail.com", "org2@yahoo.com", "org3@gmail.com"],
+            "primary_phone": ["2136758831", "3546754431", "3045678812"],
+            "geometry": [
+                Polygon([(0, 0), (1, 0), (0, 1)]),
+                Polygon([(1, 0), (1, 1), (2, 1)]),
+                Polygon([(2, 2), (1, 2), (1, 3)]),
+            ],
+        }
+        rco_geoms_gdf = gpd.GeoDataFrame(data, geometry="geometry")
+
+        rco_aggregate_cols = [
+            "organization_name",
+            "organization_address",
+            "primary_email",
+            "primary_phone",
+        ]
+
+        rco_use_cols = ["rco_info", "rco_names", "geometry"]
+
+        transformed_gdf = transform_rco_geoms_gdf(
+            rco_geoms_gdf, rco_aggregate_cols, rco_use_cols
+        )
+
+        self.assertEqual(transformed_gdf.columns.tolist(), rco_use_cols)
+        self.assertTrue(
+            all([type(entry) is str for entry in transformed_gdf["rco_info"].tolist()])
+        )
+
+    def test_transform_merged_rco_geom_gdf(self):
+        rco_use_cols = ["rco_info", "rco_names", "geometry"]
+
+        data = {
+            "opa_id": ["001123", "004435", "04531", None, "001123"],
+            "rco_info": ["Info 1", "Info 2", "Info 3", "Info 4", "Info 5"],
+            "rco_names": ["Name 1", "Name 2", "Name 3", "Name 4", "Name 5"],
+            "geometry": [
+                Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]),
+                Polygon([(1, 0), (2, 0), (2, 2), (0, 2)]),
+                Polygon([(0, 0), (2, 0), (1, 1)]),
+                Polygon([(3, 4), (3, 2), (5, 4)]),
+                Polygon([(0, 1), (1, 0), (1, 1)]),
+            ],
+        }
+        gdf = gpd.GeoDataFrame(data, geometry="geometry")
+
+        transformed_gdf = transform_merged_rco_geoms_gdf(gdf, rco_use_cols)
+
+        print(transformed_gdf.head())
+
+        self.assertEqual(transformed_gdf.shape[0], 4)
+        self.assertIn("opa_id", transformed_gdf.columns)
+        self.assertListEqual(
+            sorted(transformed_gdf["opa_id"].tolist()),
+            ["", "001123", "004435", "04531"],
+        )
+        # Add check that geometry of repeated opa_id is the first one
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/data_utils/test_tactical_urbanism.py b/data/src/test/data_utils/test_tactical_urbanism.py
new file mode 100644
index 00000000..dacc320c
--- /dev/null
+++ b/data/src/test/data_utils/test_tactical_urbanism.py
@@ -0,0 +1,27 @@
+import unittest
+
+import geopandas as gpd
+
+from new_etl.classes.featurelayer import FeatureLayer
+from new_etl.data_utils.tactical_urbanism import tactical_urbanism
+
+
+class TestTacticalUrbanism(unittest.TestCase):
+    def test_tactical_urbanism(self):
+        data = {
+            "parcel_type": ["Land", "Building", "Land", None],
+            "unsafe_building": ["N", "Y", "N", "N"],
+            "imm_dang_building": ["Y", "N", "N", "Y"],
+        }
+        gdf = gpd.GeoDataFrame(data)
+        feature_layer = FeatureLayer(name="test", gdf=gdf)
+
+        tactical_urbanism_feature_layer = tactical_urbanism(feature_layer)
+        expected_gdf = gdf.copy()
+        expected_gdf["tactical_urbanism"] = ["No", "No", "Yes", "No"]
+
+        assert expected_gdf.equals(tactical_urbanism_feature_layer.gdf)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/data/src/test/test_data_utils.py b/data/src/test/test_data_utils.py
index 5dba0036..6a37ae28 100644
--- a/data/src/test/test_data_utils.py
+++ b/data/src/test/test_data_utils.py
@@ -4,17 +4,12 @@
 from unittest.mock import MagicMock, Mock, patch
 
 import geopandas as gpd
-import numpy as np
 from shapely.geometry import LineString, MultiPolygon, Point, Polygon
 
 from config.config import USE_CRS
 from data_utils.park_priority import get_latest_shapefile_url, park_priority
 from data_utils.ppr_properties import ppr_properties
 from data_utils.vacant_properties import vacant_properties
-from new_etl.data_utils.pwd_parcels import (
-    merge_pwd_parcels_gdf,
-    transform_pwd_parcels_gdf,
-)
 
 
 class TestDataUtils(unittest.TestCase):
@@ -63,14 +58,6 @@ def tearDown(self):
         self.patcher1.stop()
         self.patcher2.stop()
 
-    def test_get_latest_shapefile_url(self):
-        """
-        Test the get_latest_shapefile_url function.
-        """
-        url = get_latest_shapefile_url()
-        self.assertTrue(url.startswith("https://"))
-        self.assertTrue(url.endswith(".zip"))
-
     @patch("data_utils.park_priority.requests.get")
     def test_get_latest_shapefile_url_mock(self, mock_get):
         """
@@ -176,123 +163,6 @@ def test_vacant_properties(self):
         """
         vacant_properties()
 
-    def test_pwd_parcels_merge(self):
-        """
-        This tests that the merge_pwd_parcels_gdf function correctly retains
-        existing point geometries in the primary GeoDataFrame when no better
-        geometry is available in the PWD parcels GeoDataFrame.
-        """
-
-        parcel_type_sample_data = ["Land", "Building", "Land", "Building"]
-        primary_data = {
-            "opa_id": ["0100", "0101", "0102", "0103"],
-            "geometry": [Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3)],
-            "parcel_type": parcel_type_sample_data,
-        }
-
-        # CRS we don't use is chosen since we are just checking it doesn't change:
-        test_crs = "EPSG:2002"
-        primary_gdf = gpd.GeoDataFrame(primary_data, geometry="geometry", crs=test_crs)
-
-        # Simulate dropped rows by having a gapped index.
-        primary_gdf.index = [0, 2, 3, 5]
-
-        # Create pwd_parcels_gdf with a continuous index.
-        pwd_data = {
-            "opa_id": ["0100", "0101", "0102", "0103"],
-            "geometry": [
-                np.nan,  # For opa 0100, no better geometry
-                MultiPolygon(
-                    [Polygon([(10, 10), (20, 10), (20, 20), (10, 20)])]
-                ),  # For opa 0101, a multipolygon available
-                np.nan,
-                np.nan,
-            ],
-        }
-        pwd_gdf = gpd.GeoDataFrame(pwd_data, geometry="geometry")
-        assert list(pwd_gdf.index) == list(range(4))  # Continuous index: 0,1,2,3
-
-        # Call the merge function under test.
-        merged_gdf = merge_pwd_parcels_gdf(primary_gdf, pwd_gdf)
-
-        # Expected geometries:
-        # For opa_id "0010", we expect the multipolygon from pwd_gdf.
-        # For the other opa_id's we expect the point geometries from primary_gdf.
-        expected_geometries = {
-            "0100": primary_gdf.loc[0, "geometry"],
-            "0101": pwd_gdf.loc[1, "geometry"],  # pwd geometry used when available
-            "0102": primary_gdf.loc[3, "geometry"],
-            "0103": primary_gdf.loc[5, "geometry"],
-        }
-
-        assert merged_gdf.crs == primary_gdf.crs
-        assert merged_gdf.crs == test_crs
-
-        # Verify each row of the merged GeoDataFrame.
-        for idx, row in merged_gdf.iterrows():
-            opa_id = row["opa_id"]
-            actual_geom = row["geometry"]
-            expected_geom = expected_geometries[opa_id]
-            # Use shapely's equals() to check geometry equivalence.
-            assert actual_geom.equals(expected_geom), (
-                f"Mismatch for opa_id {opa_id}: "
-                f"expected {expected_geom}, got {actual_geom}"
-            )
-
-        opa_id_values = ["0100", "0101", "0102", "0103"]
-        data = {
-            "opa_id": opa_id_values,
-            "geometry": [expected_geometries[opa_id] for opa_id in opa_id_values],
-            "parcel_type": parcel_type_sample_data,
-        }
-        expected_df = gpd.GeoDataFrame(
-            data=data,
-            geometry="geometry",
-        )
-
-        assert expected_df.equals(merged_gdf)
-
-    def test_transform_pwd_parcels_gdf_basic(self):
-        """
-        Check the basic functionality of the transform_pwd_parcels_gdf function.
-        This function is expected to mutate the input GeoDataFrame in place.
-        It should drop rows with null 'brt_id', rename 'brt_id' to 'opa_id',
-        and ensure all geometries are valid polygons or multipolygons.
-        """
-        # Create test data
-        data = {
-            "brt_id": [None, "1234", "5678"],
-            "geometry": [
-                Polygon(
-                    [(0, 0), (1, 0), (1, 1), (0, 1)]
-                ),  # Will be dropped (null brt_id)
-                Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]),  # Valid polygon
-                LineString([(0, 0), (1, 1)]),  # Invalid type (not a polygon)
-            ],
-        }
-        gdf = gpd.GeoDataFrame(data, geometry="geometry")
-
-        # Expect a ValueError because LineString is not allowed
-        with self.assertRaises(ValueError) as context:
-            transform_pwd_parcels_gdf(gdf.copy())
-
-        self.assertIn("not polygons or multipolygons", str(context.exception))
-
-        # Now fix the invalid geometry to test normal flow
-        gdf.loc[2, "geometry"] = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])
-
-        # Run transformation which mutates result in place
-        result = gdf.copy()
-        transform_pwd_parcels_gdf(result)
-
-        # Check the rows were filtered
-        self.assertEqual(len(result), 2)  # One row was dropped
-        self.assertNotIn("brt_id", result.columns)
-        self.assertIn("opa_id", result.columns)
-        self.assertListEqual(sorted(result["opa_id"].tolist()), ["1234", "5678"])
-        self.assertTrue(all(result.geometry.is_valid))
-        self.assertTrue(all(result.geometry.type.isin(["Polygon", "MultiPolygon"])))
-
 
 if __name__ == "__main__":
     unittest.main()