diff --git a/poetry.lock b/poetry.lock
index e8f7cbb97..0a7e6c674 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "agate"
@@ -47,87 +47,87 @@ files = [
 
 [[package]]
 name = "aiohttp"
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+version = "3.11.11"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
@@ -309,19 +309,19 @@ cachetools = ">=5.2.0,<6.0.0"
 
 [[package]]
 name = "attrs"
-version = "24.2.0"
+version = "24.3.0"
 description = "Classes Without Boilerplate"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
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 cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
@@ -940,13 +940,13 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist"
 
 [[package]]
 name = "croniter"
-version = "5.0.1"
+version = "6.0.0"
 description = "croniter provides iteration for datetime object with cron like format"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.6"
 files = [
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 ]
 
 [package.dependencies]
@@ -1305,19 +1305,19 @@ test = ["starlette[full]"]
 
 [[package]]
 name = "dask"
-version = "2024.12.0"
+version = "2024.12.1"
 description = "Parallel PyData with Task Scheduling"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
 click = ">=8.1"
 cloudpickle = ">=3.0.0"
-distributed = {version = "2024.12.0", optional = true, markers = "extra == \"distributed\""}
+distributed = {version = "2024.12.1", optional = true, markers = "extra == \"distributed\""}
 fsspec = ">=2021.09.0"
 packaging = ">=20.0"
 partd = ">=1.4.0"
@@ -1329,7 +1329,7 @@ array = ["numpy (>=1.24)"]
 complete = ["dask[array,dataframe,diagnostics,distributed]", "lz4 (>=4.3.2)", "pyarrow (>=14.0.1)"]
 dataframe = ["dask-expr (>=1.1,<1.2)", "dask[array]", "pandas (>=2.0)"]
 diagnostics = ["bokeh (>=3.1.0)", "jinja2 (>=2.10.3)"]
-distributed = ["distributed (==2024.12.0)"]
+distributed = ["distributed (==2024.12.1)"]
 test = ["pandas[test]", "pre-commit", "pytest", "pytest-cov", "pytest-rerunfailures", "pytest-timeout", "pytest-xdist"]
 
 [[package]]
@@ -1609,19 +1609,19 @@ async = ["httpx (>=0.23.0,<0.24.0)"]
 
 [[package]]
 name = "distributed"
-version = "2024.12.0"
+version = "2024.12.1"
 description = "Distributed scheduler for Dask"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
 click = ">=8.0"
 cloudpickle = ">=3.0.0"
-dask = "2024.12.0"
+dask = "2024.12.1"
 jinja2 = ">=2.10.3"
 locket = ">=1.0.0"
 msgpack = ">=1.0.2"
@@ -1637,13 +1637,13 @@ zict = ">=3.0.0"
 
 [[package]]
 name = "dlt"
-version = "1.4.1"
+version = "1.5.0"
 description = "dlt is an open-source python-first scalable data loading library that does not require any backend to run."
 optional = false
 python-versions = "<3.13,>=3.8.1"
 files = [
-    {file = "dlt-1.4.1-py3-none-any.whl", hash = "sha256:fdfda790084914902579c903882b75d3c683b7545e15f86240cd1005bb3e8e64"},
-    {file = "dlt-1.4.1.tar.gz", hash = "sha256:73e797e8d1c220caa80d464aebdd23c300b1c8385fbb605df04167fc513f8670"},
+    {file = "dlt-1.5.0-py3-none-any.whl", hash = "sha256:b9eaaa4966f3f696b8ba5cb988ce3c1a1030bede0cb92ecfa75f663709a7d296"},
+    {file = "dlt-1.5.0.tar.gz", hash = "sha256:2ece1c96242699630793681f0e3b9ec8bd41c79db3de20266cde427a71869b61"},
 ]
 
 [package.dependencies]
@@ -1664,7 +1664,7 @@ pytz = ">=2022.6"
 PyYAML = ">=5.4.1"
 requests = ">=2.26.0"
 requirements-parser = ">=0.5.0"
-semver = ">=2.13.0"
+semver = ">=3.0.0"
 setuptools = ">=65.6.0"
 simplejson = ">=3.17.5"
 tenacity = ">=8.0.2"
@@ -1675,11 +1675,11 @@ win-precise-time = {version = ">=1.4.2", markers = "os_name == \"nt\""}
 
 [package.extras]
 athena = ["botocore (>=1.28)", "pyarrow (>=12.0.0)", "pyathena (>=2.9.6)", "s3fs (>=2022.4.0)"]
-az = ["adlfs (>=2022.4.0)"]
+az = ["adlfs (>=2024.7.0)"]
 bigquery = ["db-dtypes (>=1.2.0)", "gcsfs (>=2022.4.0)", "google-cloud-bigquery (>=2.26.0)", "grpcio (>=1.50.0)", "pyarrow (>=12.0.0)"]
 cli = ["cron-descriptor (>=1.2.32)", "pipdeptree (>=2.9.0,<2.10)"]
-clickhouse = ["adlfs (>=2022.4.0)", "clickhouse-connect (>=0.7.7)", "clickhouse-driver (>=0.2.7)", "gcsfs (>=2022.4.0)", "pyarrow (>=12.0.0)", "s3fs (>=2022.4.0)"]
-databricks = ["databricks-sql-connector (>=2.9.3)"]
+clickhouse = ["adlfs (>=2024.7.0)", "clickhouse-connect (>=0.7.7)", "clickhouse-driver (>=0.2.7)", "gcsfs (>=2022.4.0)", "pyarrow (>=12.0.0)", "s3fs (>=2022.4.0)"]
+databricks = ["databricks-sdk (>=0.38.0)", "databricks-sql-connector (>=2.9.3)"]
 deltalake = ["deltalake (>=0.21.0)", "pyarrow (>=12.0.0)"]
 dremio = ["pyarrow (>=12.0.0)"]
 duckdb = ["duckdb (>=0.9)"]
@@ -1692,6 +1692,7 @@ mssql = ["pyodbc (>=4.0.39)"]
 parquet = ["pyarrow (>=12.0.0)"]
 postgis = ["psycopg2-binary (>=2.9.1)", "psycopg2cffi (>=2.9.0)"]
 postgres = ["psycopg2-binary (>=2.9.1)", "psycopg2cffi (>=2.9.0)"]
+pyiceberg = ["pyarrow (>=12.0.0)", "pyiceberg (>=0.8.1)", "sqlalchemy (>=1.4)"]
 qdrant = ["qdrant-client[fastembed] (>=1.8)"]
 redshift = ["psycopg2-binary (>=2.9.1)", "psycopg2cffi (>=2.9.0)"]
 s3 = ["botocore (>=1.28)", "s3fs (>=2022.4.0)"]
@@ -1699,7 +1700,7 @@ sftp = ["paramiko (>=3.3.0)"]
 snowflake = ["snowflake-connector-python (>=3.5.0)"]
 sql-database = ["sqlalchemy (>=1.4)"]
 sqlalchemy = ["alembic (>1.10.0)", "sqlalchemy (>=1.4)"]
-synapse = ["adlfs (>=2022.4.0)", "pyarrow (>=12.0.0)", "pyodbc (>=4.0.39)"]
+synapse = ["adlfs (>=2024.7.0)", "pyarrow (>=12.0.0)", "pyodbc (>=4.0.39)"]
 weaviate = ["weaviate-client (>=3.22)"]
 
 [[package]]
@@ -1862,13 +1863,13 @@ standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "htt
 
 [[package]]
 name = "fastapi-cli"
-version = "0.0.6"
+version = "0.0.7"
 description = "Run and manage FastAPI apps from the command line with FastAPI CLI. 🚀"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi_cli-0.0.6-py3-none-any.whl", hash = "sha256:43288efee46338fae8902f9bf4559aed3aed639f9516f5d394a7ff19edcc8faf"},
-    {file = "fastapi_cli-0.0.6.tar.gz", hash = "sha256:2835a8f0c44b68e464d5cafe5ec205265f02dc1ad1d640db33a994ba3338003b"},
+    {file = "fastapi_cli-0.0.7-py3-none-any.whl", hash = "sha256:d549368ff584b2804336c61f192d86ddea080c11255f375959627911944804f4"},
+    {file = "fastapi_cli-0.0.7.tar.gz", hash = "sha256:02b3b65956f526412515907a0793c9094abd4bfb5457b389f645b0ea6ba3605e"},
 ]
 
 [package.dependencies]
@@ -1895,20 +1896,6 @@ docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.
 testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
 typing = ["typing-extensions (>=4.12.2)"]
 
-[[package]]
-name = "freezegun"
-version = "1.5.1"
-description = "Let your Python tests travel through time"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "freezegun-1.5.1-py3-none-any.whl", hash = "sha256:bf111d7138a8abe55ab48a71755673dbaa4ab87f4cff5634a4442dfec34c15f1"},
-    {file = "freezegun-1.5.1.tar.gz", hash = "sha256:b29dedfcda6d5e8e083ce71b2b542753ad48cfec44037b3fc79702e2980a89e9"},
-]
-
-[package.dependencies]
-python-dateutil = ">=2.7"
-
 [[package]]
 name = "frozenlist"
 version = "1.5.0"
@@ -2012,13 +1999,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.10.0"
+version = "2024.12.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
-    {file = "fsspec-2024.10.0.tar.gz", hash = "sha256:eda2d8a4116d4f2429db8550f2457da57279247dd930bb12f821b58391359493"},
+    {file = "fsspec-2024.12.0-py3-none-any.whl", hash = "sha256:b520aed47ad9804237ff878b504267a3b0b441e97508bd6d2d8774e3db85cee2"},
+    {file = "fsspec-2024.12.0.tar.gz", hash = "sha256:670700c977ed2fb51e0d9f9253177ed20cbde4a3e5c0283cc5385b5870c8533f"},
 ]
 
 [package.extras]
@@ -2051,19 +2038,19 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "gcsfs"
-version = "2024.10.0"
+version = "2024.12.0"
 description = "Convenient Filesystem interface over GCS"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "gcsfs-2024.10.0-py2.py3-none-any.whl", hash = "sha256:bb2d23547e61203ea2dda5fa6c4b91a0c34b74ebe8bb6ab1926f6c33381bceb2"},
-    {file = "gcsfs-2024.10.0.tar.gz", hash = "sha256:5df54cfe568e8fdeea5aafa7fed695cdc69a9a674e991ca8c1ce634f5df1d314"},
+    {file = "gcsfs-2024.12.0-py2.py3-none-any.whl", hash = "sha256:ec88e48f77e466723705458af85dda238e43aa69fac071efd98829d06e9f095a"},
+    {file = "gcsfs-2024.12.0.tar.gz", hash = "sha256:e672413922108300ebc1fe78b8f99f3c7c1b94e7e088f5a6dc88de6d5a93d156"},
 ]
 
 [package.dependencies]
 aiohttp = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1"
 decorator = ">4.1.2"
-fsspec = "2024.10.0"
+fsspec = "2024.12.0"
 google-auth = ">=1.2"
 google-auth-oauthlib = "*"
 google-cloud-storage = "*"
@@ -2168,13 +2155,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-api-python-client"
-version = "2.155.0"
+version = "2.156.0"
 description = "Google API Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google_api_python_client-2.155.0-py2.py3-none-any.whl", hash = "sha256:83fe9b5aa4160899079d7c93a37be306546a17e6686e2549bcc9584f1a229747"},
-    {file = "google_api_python_client-2.155.0.tar.gz", hash = "sha256:25529f89f0d13abcf3c05c089c423fb2858ac16e0b3727543393468d0d7af67c"},
+    {file = "google_api_python_client-2.156.0-py2.py3-none-any.whl", hash = "sha256:6352185c505e1f311f11b0b96c1b636dcb0fec82cd04b80ac5a671ac4dcab339"},
+    {file = "google_api_python_client-2.156.0.tar.gz", hash = "sha256:b809c111ded61716a9c1c7936e6899053f13bae3defcdfda904bd2ca68065b9c"},
 ]
 
 [package.dependencies]
@@ -3062,13 +3049,13 @@ files = [
 
 [[package]]
 name = "ipython"
-version = "8.30.0"
+version = "8.31.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.30.0-py3-none-any.whl", hash = "sha256:85ec56a7e20f6c38fce7727dcca699ae4ffc85985aa7b23635a8008f918ae321"},
-    {file = "ipython-8.30.0.tar.gz", hash = "sha256:cb0a405a306d2995a5cbb9901894d240784a9f341394c6ba3f4fe8c6eb89ff6e"},
+    {file = "ipython-8.31.0-py3-none-any.whl", hash = "sha256:46ec58f8d3d076a61d128fe517a51eb730e3aaf0c184ea8c17d16e366660c6a6"},
+    {file = "ipython-8.31.0.tar.gz", hash = "sha256:b6a2274606bec6166405ff05e54932ed6e5cfecaca1fc05f2cacde7bb074d70b"},
 ]
 
 [package.dependencies]
@@ -3214,6 +3201,8 @@ optional = false
 python-versions = "*"
 files = [
     {file = "jsonpath-ng-1.7.0.tar.gz", hash = "sha256:f6f5f7fd4e5ff79c785f1573b394043b39849fb2bb47bcead935d12b00beab3c"},
+    {file = "jsonpath_ng-1.7.0-py2-none-any.whl", hash = "sha256:898c93fc173f0c336784a3fa63d7434297544b7198124a68f9a3ef9597b0ae6e"},
+    {file = "jsonpath_ng-1.7.0-py3-none-any.whl", hash = "sha256:f3d7f9e848cba1b6da28c55b1c26ff915dc9e0b1ba7e752a53d6da8d5cbd00b6"},
 ]
 
 [package.dependencies]
@@ -3343,13 +3332,13 @@ adal = ["adal (>=1.0.2)"]
 
 [[package]]
 name = "kubernetes-asyncio"
-version = "31.1.1"
+version = "32.0.0"
 description = "Kubernetes asynchronous python client"
 optional = false
 python-versions = "*"
 files = [
-    {file = "kubernetes_asyncio-31.1.1-py3-none-any.whl", hash = "sha256:d5e441674fde5dd8d0366cb6a204d7fbaef8d0dce7ca7a34c0791af0d13b6ff1"},
-    {file = "kubernetes_asyncio-31.1.1.tar.gz", hash = "sha256:393bb60a2740d482651d474956296b341348c3e4f883b171740e5a4bf105a87b"},
+    {file = "kubernetes_asyncio-32.0.0-py3-none-any.whl", hash = "sha256:f9baa8f73c182948e063bb9448d8e614f73df4aae52d28f173a3a1c2b77fbfb4"},
+    {file = "kubernetes_asyncio-32.0.0.tar.gz", hash = "sha256:77f28a026aff8a40fd34a7ebad9f3813dcb7e549c7240275fc4bfbff0a6b49d0"},
 ]
 
 [package.dependencies]
@@ -4829,32 +4818,32 @@ files = [
 
 [[package]]
 name = "psutil"
-version = "6.1.0"
+version = "6.1.1"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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-[package.extras]
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+]
+
+[package.extras]
+dev = ["abi3audit", "black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "vulture", "wheel"]
 test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
@@ -5447,13 +5436,13 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "python-json-logger"
-version = "3.2.0"
+version = "3.2.1"
 description = "JSON Log Formatter for the Python Logging Package"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -5472,13 +5461,13 @@ files = [
 
 [[package]]
 name = "python-multipart"
-version = "0.0.19"
+version = "0.0.20"
 description = "A streaming multipart parser for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -6275,25 +6264,25 @@ files = [
 
 [[package]]
 name = "sling"
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+version = "1.3.3"
 description = "Slings data from a source to a target"
 optional = false
 python-versions = "*"
 files = [
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 [package.dependencies]
-sling-linux-amd64 = "1.3.2"
+sling-linux-amd64 = "1.3.3"
 
 [[package]]
 name = "sling-linux-amd64"
-version = "1.3.2"
+version = "1.3.3"
 description = "Sling Binary for Linux (AMD64)"
 optional = false
 python-versions = "*"
 files = [
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 [[package]]
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+version = "26.0.1"
 description = "An easily customizable SQL parser and transpiler"
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 files = [
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 [package.dependencies]
-sqlglotrs = {version = "0.2.12", optional = true, markers = "extra == \"rs\""}
+sqlglotrs = {version = "0.3.0", optional = true, markers = "extra == \"rs\""}
 
 [package.extras]
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-rs = ["sqlglotrs (==0.2.12)"]
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+rs = ["sqlglotrs (==0.3.0)"]
 
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 python-versions = ">=3.7"
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 ]
 
 [[package]]
 name = "sqlmesh"
-version = "0.129.0"
+version = "0.141.1"
 description = ""
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -6570,7 +6559,6 @@ click = "*"
 croniter = "*"
 dateparser = "*"
 duckdb = "!=0.10.3"
-freezegun = "*"
 hyperscript = ">=0.1.0"
 ipywidgets = "*"
 jinja2 = "*"
@@ -6579,18 +6567,20 @@ pydantic = "*"
 requests = "*"
 rich = {version = "*", extras = ["jupyter"]}
 "ruamel.yaml" = "*"
-sqlglot = {version = ">=25.27.0,<25.28.0", extras = ["rs"]}
+setuptools = {version = "*", markers = "python_version >= \"3.12\""}
+sqlglot = {version = ">=26.0.1,<26.1.0", extras = ["rs"]}
 tenacity = "*"
-trino = {version = "*", optional = true, markers = "extra == \"trino\""}
+time-machine = "*"
 
 [package.extras]
 athena = ["PyAthena[pandas]"]
+azuresql = ["pymssql"]
 bigquery = ["google-cloud-bigquery-storage", "google-cloud-bigquery[pandas]"]
-cicdtest = ["dbt-athena-community", "dbt-databricks", "dbt-redshift", "dbt-sqlserver (>=1.7.0)", "dbt-trino"]
+cicdtest = ["dbt-athena-community", "dbt-clickhouse", "dbt-databricks", "dbt-redshift", "dbt-sqlserver (>=1.7.0)", "dbt-trino"]
 clickhouse = ["clickhouse-connect"]
 databricks = ["databricks-sql-connector"]
 dbt = ["dbt-core (<2)"]
-dev = ["Faker", "PyGithub", "agate (==1.7.1)", "apache-airflow (==2.9.1)", "beautifulsoup4", "cryptography (>=42.0.4,<42.1.0)", "custom-materializations", "dbt-bigquery", "dbt-core", "dbt-duckdb (>=1.7.1)", "dbt-snowflake", "google-auth", "google-cloud-bigquery", "google-cloud-bigquery-storage", "mypy (>=1.10.0,<1.11.0)", "pandas-stubs", "pre-commit", "psycopg2-binary", "pydantic (<2.6.0)", "pyspark (>=3.5.0,<3.6.0)", "pytest", "pytest-asyncio (<0.23.0)", "pytest-mock", "pytest-retry", "pytest-xdist", "pytz", "ruff (>=0.4.0,<0.5.0)", "snowflake-connector-python[pandas,secure-local-storage] (>=3.0.2)", "sqlalchemy-stubs", "types-croniter", "types-dateparser", "types-python-dateutil", "types-pytz", "types-requests (==2.28.8)", "typing-extensions"]
+dev = ["Faker", "PyAthena[pandas]", "PyGithub", "agate (==1.7.1)", "apache-airflow (==2.9.1)", "beautifulsoup4", "clickhouse-connect", "cryptography (>=42.0.4,<42.1.0)", "custom-materializations", "databricks-sql-connector", "dbt-bigquery", "dbt-core", "dbt-duckdb (>=1.7.1)", "dbt-snowflake", "google-auth", "google-cloud-bigquery", "google-cloud-bigquery-storage", "mypy (>=1.13.0,<1.14.0)", "opentelemetry-proto (==1.27.0)", "pandas-stubs", "pre-commit", "psycopg2-binary", "pydantic (<2.6.0)", "pyspark (>=3.5.0,<3.6.0)", "pytest", "pytest-asyncio (<0.23.0)", "pytest-mock", "pytest-retry", "pytest-xdist", "pytz", "ruff (>=0.7.0,<0.8.0)", "snowflake-connector-python[pandas,secure-local-storage] (>=3.0.2)", "sqlalchemy-stubs", "types-croniter", "types-dateparser", "types-python-dateutil", "types-pytz", "types-requests (==2.28.8)", "typing-extensions"]
 dlt = ["dlt"]
 gcppostgres = ["cloud-sql-python-connector[pg8000]"]
 github = ["PyGithub"]
@@ -6599,11 +6589,11 @@ mssql = ["pymssql"]
 mwaa = ["boto3"]
 mysql = ["mysql-connector-python"]
 postgres = ["psycopg2"]
-redshift = ["redshift-connector"]
-slack = ["slack-sdk"]
+redshift = ["redshift_connector"]
+slack = ["slack_sdk"]
 snowflake = ["cryptography (>=42.0.4,<42.1.0)", "snowflake-connector-python[pandas,secure-local-storage]", "snowflake-snowpark-python"]
 trino = ["trino"]
-web = ["fastapi (==0.110.2)", "pyarrow", "sse-starlette (>=0.2.2)", "uvicorn[standard] (==0.22.0)", "watchfiles (>=0.19.0)"]
+web = ["fastapi (==0.115.5)", "pyarrow", "sse-starlette (>=0.2.2)", "uvicorn[standard] (==0.22.0)", "watchfiles (>=0.19.0)"]
 
 [[package]]
 name = "sqlparse"
@@ -6775,6 +6765,74 @@ all = ["tornado (>=4.0)", "twisted"]
 tornado = ["tornado (>=4.0)"]
 twisted = ["twisted"]
 
+[[package]]
+name = "time-machine"
+version = "2.16.0"
+description = "Travel through time in your tests."
+optional = false
+python-versions = ">=3.9"
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+    {file = "time_machine-2.16.0-cp39-cp39-win_arm64.whl", hash = "sha256:2e08a4015d5d1aab2cb46c780e85b33efcd5cbe880bb363b282a6972e617b8bb"},
+    {file = "time_machine-2.16.0.tar.gz", hash = "sha256:4a99acc273d2f98add23a89b94d4dd9e14969c01214c8514bfa78e4e9364c7e2"},
+]
+
+[package.dependencies]
+python-dateutil = "*"
+
 [[package]]
 name = "toml"
 version = "0.10.2"
@@ -6916,31 +6974,6 @@ files = [
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
-[[package]]
-name = "trino"
-version = "0.331.0"
-description = "Client for the Trino distributed SQL Engine"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "trino-0.331.0-py3-none-any.whl", hash = "sha256:4f909e6c2966d23917e2538bc7f342d5dcc6e512102811fb1e53bdaf15bd49e3"},
-    {file = "trino-0.331.0.tar.gz", hash = "sha256:2d9acdf7b19d136c97c98e55599fb9a5556e124282b0a40afb93bdfbb5371708"},
-]
-
-[package.dependencies]
-python-dateutil = "*"
-pytz = "*"
-requests = ">=2.31.0"
-tzlocal = "*"
-
-[package.extras]
-all = ["requests_kerberos", "sqlalchemy (>=1.3)"]
-external-authentication-token-cache = ["keyring"]
-gssapi = ["krb5 (==0.5.1)", "requests_gssapi"]
-kerberos = ["requests_kerberos"]
-sqlalchemy = ["sqlalchemy (>=1.3)"]
-tests = ["black", "httpretty (<1.1)", "isort", "keyring", "krb5 (==0.5.1)", "pre-commit", "pytest", "pytest-runner", "requests_gssapi", "requests_kerberos", "sqlalchemy (>=1.3)"]
-
 [[package]]
 name = "typer"
 version = "0.15.1"
@@ -7684,4 +7717,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12,<3.13"
-content-hash = "d34c20fd2b3ef341b2a4fb6d418a4ac512e03eb76ea55210f03322c00477ad83"
+content-hash = "862623b53b4d026ad2cb7bdd8e2409e047593008c8f2cfc455c42e4c558ffae6"
diff --git a/pyproject.toml b/pyproject.toml
index 189b0d2b3..08dd5949f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,7 +59,7 @@ sqlalchemy = "^2.0.25"
 textual = "^0.52.1"
 redis = "^5.0.7"
 githubkit = "^0.11.6"
-sqlmesh = { extras = ["trino"], version = "^0.129.0" }
+sqlmesh = "^0.141.1"
 dagster-duckdb = "^0.24.0"
 dagster-duckdb-polars = "^0.24.0"
 google-cloud-bigquery-storage = "^2.25.0"
diff --git a/warehouse/metrics_mesh/config.py b/warehouse/metrics_mesh/config.py
index f27971ee2..d00deca21 100644
--- a/warehouse/metrics_mesh/config.py
+++ b/warehouse/metrics_mesh/config.py
@@ -21,6 +21,9 @@
             connection=DuckDBConnectionConfig(
                 database=os.environ.get("SQLMESH_DUCKDB_LOCAL_PATH")
             ),
+            test_connection=DuckDBConnectionConfig(
+                database="/Users/raven/testing-duckdb.db",
+            ),
             variables={
                 "oso_source_db": "sources",
             },
diff --git a/warehouse/metrics_mesh/models/events_daily_to_artifact.sql b/warehouse/metrics_mesh/models/events_daily_to_artifact.sql
index ac40d881d..20b472192 100644
--- a/warehouse/metrics_mesh/models/events_daily_to_artifact.sql
+++ b/warehouse/metrics_mesh/models/events_daily_to_artifact.sql
@@ -36,4 +36,4 @@ GROUP BY
   to_artifact_id,
   event_source,
   event_type,
-  DATE_TRUNC('DAY', time::DATE)
\ No newline at end of file
+  DATE_TRUNC('DAY', time::DATE)
diff --git a/warehouse/metrics_mesh/tests/test_events_daily_to_artifact.yml b/warehouse/metrics_mesh/tests/test_events_daily_to_artifact.yml
index d78446d09..3b4951d31 100644
--- a/warehouse/metrics_mesh/tests/test_events_daily_to_artifact.yml
+++ b/warehouse/metrics_mesh/tests/test_events_daily_to_artifact.yml
@@ -11,43 +11,43 @@ test_events_daily_to_artifact:
         from_artifact_id: user_0
         event_source: BLOCKCHAIN
         event_type: CONTRACT_INVOCATION_SUCCESS_DAILY_COUNT
-        time: 2024-01-01T00:00:00Z
+        time: "2024-01-01 00:00:00+00:00"
         amount: 10
       - to_artifact_id: contract_1
         from_artifact_id: user_0
         event_source: BLOCKCHAIN
         event_type: CONTRACT_INVOCATION_SUCCESS_DAILY_COUNT
-        time: 2024-01-01T00:00:00Z
+        time: "2024-01-01 00:00:00+00:00"
         amount: 10
       - to_artifact_id: repo_0
         from_artifact_id: dev_0
         event_source: SOURCE_PROVIDER
         event_type: COMMIT_CODE
-        time: 2024-01-01T02:00:00Z
+        time: "2024-01-01 02:00:00+00:00"
         amount: 1
       - to_artifact_id: repo_0
         from_artifact_id: dev_0
         event_source: SOURCE_PROVIDER
         event_type: COMMIT_CODE
-        time: 2024-01-01T03:00:00Z
+        time: "2024-01-01 03:00:00+00:00"
         amount: 1
       - to_artifact_id: repo_0
         from_artifact_id: dev_0
         event_source: SOURCE_PROVIDER
         event_type: COMMIT_CODE
-        time: 2024-01-01T04:00:00Z
+        time: "2024-01-01 04:00:00+00:00"
         amount: 1
       - to_artifact_id: repo_0
         from_artifact_id: dev_1
         event_source: SOURCE_PROVIDER
         event_type: COMMIT_CODE
-        time: 2024-01-01T04:00:00Z
+        time: "2024-01-01 04:00:00+00:00"
         amount: 1
       - to_artifact_id: repo_0
         from_artifact_id: dev_1
         event_source: SOURCE_PROVIDER
         event_type: COMMIT_CODE
-        time: 2024-01-02T04:00:00Z
+        time: "2024-01-02 04:00:00+00:00"
         amount: 1
   outputs:
     query:
diff --git a/warehouse/metrics_tools/factory/__init__.py b/warehouse/metrics_tools/factory/__init__.py
index f0caa6c32..48978f0c7 100644
--- a/warehouse/metrics_tools/factory/__init__.py
+++ b/warehouse/metrics_tools/factory/__init__.py
@@ -1,4 +1,4 @@
 # ruff: noqa: F403
 
-from .factory import *
 from ..definition import *
+from .factory import *
diff --git a/warehouse/metrics_tools/factory/constants.py b/warehouse/metrics_tools/factory/constants.py
new file mode 100644
index 000000000..0bd1cbb21
--- /dev/null
+++ b/warehouse/metrics_tools/factory/constants.py
@@ -0,0 +1,35 @@
+import typing as t
+
+from sqlglot import exp
+
+TIME_AGGREGATION_TO_CRON = {
+    "daily": "@daily",
+    "monthly": "@monthly",
+    "weekly": "@weekly",
+}
+METRICS_COLUMNS_BY_ENTITY: t.Dict[str, t.Dict[str, exp.DataType]] = {
+    "artifact": {
+        "metrics_sample_date": exp.DataType.build("DATE", dialect="duckdb"),
+        "event_source": exp.DataType.build("STRING", dialect="duckdb"),
+        "to_artifact_id": exp.DataType.build("STRING", dialect="duckdb"),
+        "from_artifact_id": exp.DataType.build("STRING", dialect="duckdb"),
+        "metric": exp.DataType.build("STRING", dialect="duckdb"),
+        "amount": exp.DataType.build("DOUBLE", dialect="duckdb"),
+    },
+    "project": {
+        "metrics_sample_date": exp.DataType.build("DATE", dialect="duckdb"),
+        "event_source": exp.DataType.build("STRING", dialect="duckdb"),
+        "to_project_id": exp.DataType.build("STRING", dialect="duckdb"),
+        "from_artifact_id": exp.DataType.build("STRING", dialect="duckdb"),
+        "metric": exp.DataType.build("STRING", dialect="duckdb"),
+        "amount": exp.DataType.build("DOUBLE", dialect="duckdb"),
+    },
+    "collection": {
+        "metrics_sample_date": exp.DataType.build("DATE", dialect="duckdb"),
+        "event_source": exp.DataType.build("STRING", dialect="duckdb"),
+        "to_collection_id": exp.DataType.build("STRING", dialect="duckdb"),
+        "from_artifact_id": exp.DataType.build("STRING", dialect="duckdb"),
+        "metric": exp.DataType.build("STRING", dialect="duckdb"),
+        "amount": exp.DataType.build("DOUBLE", dialect="duckdb"),
+    },
+}
diff --git a/warehouse/metrics_tools/factory/factory.py b/warehouse/metrics_tools/factory/factory.py
index faed05716..8940fc8a8 100644
--- a/warehouse/metrics_tools/factory/factory.py
+++ b/warehouse/metrics_tools/factory/factory.py
@@ -1,23 +1,19 @@
-import contextlib
 import inspect
 import logging
 import os
 import textwrap
 import typing as t
 from dataclasses import dataclass, field
-from datetime import datetime
+from pathlib import Path
 from queue import PriorityQueue
 
-import pandas as pd
-import sqlglot as sql
-from metrics_tools.compute.client import Client
-from metrics_tools.compute.types import ExportType
 from metrics_tools.definition import (
     MetricQuery,
     PeerMetricDependencyRef,
     TimeseriesMetricsOptions,
     reference_to_str,
 )
+from metrics_tools.factory import constants
 from metrics_tools.joiner import JoinerTransform
 from metrics_tools.macros import (
     metrics_end,
@@ -30,21 +26,14 @@
     metrics_start,
     relative_window_sample_date,
 )
-from metrics_tools.models import GeneratedModel, GeneratedPythonModel
-from metrics_tools.runner import MetricsRunner
+from metrics_tools.models import MacroOverridingModel
 from metrics_tools.transformer import (
     IntermediateMacroEvaluatorTransform,
     SQLTransformer,
 )
 from metrics_tools.transformer.qualify import QualifyTransform
-from metrics_tools.transformer.tables import ExecutionContextTableTransform
-from metrics_tools.utils import env
 from metrics_tools.utils.logging import add_metrics_tools_to_sqlmesh_logging
-from metrics_tools.utils.tables import create_dependent_tables_map
 from sqlglot import exp
-from sqlmesh import ExecutionContext
-from sqlmesh.core.dialect import parse_one
-from sqlmesh.core.macros import MacroEvaluator
 from sqlmesh.core.model import ModelKindName
 
 logger = logging.getLogger(__name__)
@@ -55,60 +44,6 @@
 CURR_DIR = os.path.dirname(__file__)
 QUERIES_DIR = os.path.abspath(os.path.join(CURR_DIR, "../../metrics_mesh/oso_metrics"))
 
-TIME_AGGREGATION_TO_CRON = {
-    "daily": "@daily",
-    "monthly": "@monthly",
-    "weekly": "@weekly",
-}
-METRICS_COLUMNS_BY_ENTITY: t.Dict[str, t.Dict[str, exp.DataType]] = {
-    "artifact": {
-        "metrics_sample_date": exp.DataType.build("DATE", dialect="duckdb"),
-        "event_source": exp.DataType.build("STRING", dialect="duckdb"),
-        "to_artifact_id": exp.DataType.build("STRING", dialect="duckdb"),
-        "from_artifact_id": exp.DataType.build("STRING", dialect="duckdb"),
-        "metric": exp.DataType.build("STRING", dialect="duckdb"),
-        "amount": exp.DataType.build("DOUBLE", dialect="duckdb"),
-    },
-    "project": {
-        "metrics_sample_date": exp.DataType.build("DATE", dialect="duckdb"),
-        "event_source": exp.DataType.build("STRING", dialect="duckdb"),
-        "to_project_id": exp.DataType.build("STRING", dialect="duckdb"),
-        "from_artifact_id": exp.DataType.build("STRING", dialect="duckdb"),
-        "metric": exp.DataType.build("STRING", dialect="duckdb"),
-        "amount": exp.DataType.build("DOUBLE", dialect="duckdb"),
-    },
-    "collection": {
-        "metrics_sample_date": exp.DataType.build("DATE", dialect="duckdb"),
-        "event_source": exp.DataType.build("STRING", dialect="duckdb"),
-        "to_collection_id": exp.DataType.build("STRING", dialect="duckdb"),
-        "from_artifact_id": exp.DataType.build("STRING", dialect="duckdb"),
-        "metric": exp.DataType.build("STRING", dialect="duckdb"),
-        "amount": exp.DataType.build("DOUBLE", dialect="duckdb"),
-    },
-}
-
-
-@contextlib.contextmanager
-def metric_ref_evaluator_context(
-    evaluator: MacroEvaluator,
-    ref: PeerMetricDependencyRef,
-    extra_vars: t.Optional[t.Dict[str, t.Any]] = None,
-):
-    before = evaluator.locals.copy()
-    evaluator.locals.update(extra_vars or {})
-    evaluator.locals.update(
-        {
-            "rolling_window": ref.get("window"),
-            "rolling_unit": ref.get("unit"),
-            "time_aggregation": ref.get("time_aggregation"),
-            "entity_type": ref.get("entity_type"),
-        }
-    )
-    try:
-        yield
-    finally:
-        evaluator.locals = before
-
 
 class MetricQueryConfig(t.TypedDict):
     table_name: str
@@ -351,6 +286,8 @@ def queue_query(name: str):
 
     def generate_models(self, calling_file: str):
         """Generates sqlmesh models for all the configured metrics definitions"""
+        from metrics_tools.factory.proxy.proxies import join_all_of_entity_type
+
         # Generate the models
 
         for _, query_config, dependencies in self.generate_ordered_queries():
@@ -359,28 +296,38 @@ def generate_models(self, calling_file: str):
             )
 
         # Join all of the models of the same entity type into the same view model
+        override_path = Path(inspect.getfile(join_all_of_entity_type))
+        override_module_path = Path(
+            os.path.dirname(inspect.getfile(join_all_of_entity_type))
+        )
         for entity_type, tables in self._marts_tables.items():
-            GeneratedModel.create(
-                func=join_all_of_entity_type,
-                entrypoint_path=calling_file,
-                config={
-                    "db": self.catalog,
-                    "tables": tables,
-                    "columns": list(METRICS_COLUMNS_BY_ENTITY[entity_type].keys()),
-                },
+            MacroOverridingModel(
+                additional_macros=[],
+                override_module_path=override_module_path,
+                override_path=override_path,
+                locals=dict(
+                    config={
+                        "db": self.catalog,
+                        "tables": tables,
+                        "columns": list(
+                            constants.METRICS_COLUMNS_BY_ENTITY[entity_type].keys()
+                        ),
+                    }
+                ),
                 name=f"metrics.timeseries_metrics_to_{entity_type}",
+                is_sql=True,
                 kind="VIEW",
                 dialect="clickhouse",
                 start=self._raw_options["start"],
                 columns={
-                    k: METRICS_COLUMNS_BY_ENTITY[entity_type][k]
+                    k: constants.METRICS_COLUMNS_BY_ENTITY[entity_type][k]
                     for k in filter(
                         lambda col: col not in ["event_source"],
-                        METRICS_COLUMNS_BY_ENTITY[entity_type].keys(),
+                        constants.METRICS_COLUMNS_BY_ENTITY[entity_type].keys(),
                     )
                 },
                 enabled=self._raw_options.get("enabled", True),
-            )
+            )(join_all_of_entity_type)
         logger.info("model generation complete")
 
     def generate_model_for_rendered_query(
@@ -406,6 +353,8 @@ def generate_rolling_python_model_for_rendered_query(
         query_config: MetricQueryConfig,
         dependencies: t.Set[str],
     ):
+        from metrics_tools.factory.proxy.proxies import generated_rolling_query_proxy
+
         depends_on = set()
         for dep in dependencies:
             depends_on.add(f"{self.catalog}.{dep}")
@@ -413,7 +362,7 @@ def generate_rolling_python_model_for_rendered_query(
         ref = query_config["ref"]
         query = query_config["query"]
 
-        columns = METRICS_COLUMNS_BY_ENTITY[ref["entity_type"]]
+        columns = constants.METRICS_COLUMNS_BY_ENTITY[ref["entity_type"]]
 
         kind_common = {
             "batch_size": ref.get("batch_size", 365),
@@ -435,12 +384,15 @@ def generate_rolling_python_model_for_rendered_query(
             "metrics_sample_date",
         ]
 
-        return GeneratedPythonModel.create(
+        # Override the path and module so that sqlmesh generates the
+        # proper python_env for the model
+        override_path = Path(inspect.getfile(generated_rolling_query_proxy))
+        override_module_path = Path(
+            os.path.dirname(inspect.getfile(generated_rolling_query_proxy))
+        )
+        return MacroOverridingModel(
             name=f"{self.catalog}.{query_config['table_name']}",
-            func=generated_rolling_query_proxy,
-            entrypoint_path=calling_file,
-            additional_macros=self.generated_model_additional_macros,
-            variables=self.serializable_config(query_config),
+            is_sql=False,
             depends_on=depends_on,
             columns=columns,
             kind={
@@ -452,9 +404,12 @@ def generate_rolling_python_model_for_rendered_query(
             cron=cron,
             start=self._raw_options["start"],
             grain=grain,
-            imports={"pd": pd, "generated_rolling_query": generated_rolling_query},
             enabled=self._raw_options.get("enabled", True),
-        )
+            additional_macros=self.generated_model_additional_macros,
+            locals=self.serializable_config(query_config),
+            override_module_path=override_module_path,
+            override_path=override_path,
+        )(generated_rolling_query_proxy)
 
     def generate_time_aggregation_model_for_rendered_query(
         self,
@@ -463,12 +418,12 @@ def generate_time_aggregation_model_for_rendered_query(
         dependencies: t.Set[str],
     ):
         """Generate model for time aggregation models"""
-        # Use a simple python sql model to generate the time_aggregation model
-        config = self.serializable_config(query_config)
+        from metrics_tools.factory.proxy.proxies import generated_query
 
+        # Use a simple python sql model to generate the time_aggregation model
         ref = query_config["ref"]
 
-        columns = METRICS_COLUMNS_BY_ENTITY[ref["entity_type"]]
+        columns = constants.METRICS_COLUMNS_BY_ENTITY[ref["entity_type"]]
 
         time_aggregation = ref.get("time_aggregation")
         assert time_aggregation is not None
@@ -493,12 +448,13 @@ def generate_time_aggregation_model_for_rendered_query(
             "event_source",
             "metrics_sample_date",
         ]
-        cron = TIME_AGGREGATION_TO_CRON[time_aggregation]
+        cron = constants.TIME_AGGREGATION_TO_CRON[time_aggregation]
 
-        GeneratedModel.create(
-            func=generated_query,
-            entrypoint_path=calling_file,
-            config=config,
+        # Override the path and module so that sqlmesh generates the
+        # proper python_env for the model
+        override_path = Path(inspect.getfile(generated_query))
+        override_module_path = Path(os.path.dirname(inspect.getfile(generated_query)))
+        return MacroOverridingModel(
             name=f"{self.catalog}.{query_config['table_name']}",
             kind={
                 "name": ModelKindName.INCREMENTAL_BY_TIME_RANGE,
@@ -506,14 +462,18 @@ def generate_time_aggregation_model_for_rendered_query(
                 **kind_options,
             },
             dialect="clickhouse",
+            is_sql=True,
             columns=columns,
             grain=grain,
             cron=cron,
             start=self._raw_options["start"],
-            additional_macros=self.generated_model_additional_macros,
             partitioned_by=partitioned_by,
             enabled=self._raw_options.get("enabled", True),
-        )
+            additional_macros=self.generated_model_additional_macros,
+            locals=self.serializable_config(query_config),
+            override_module_path=override_module_path,
+            override_path=override_path,
+        )(generated_query)
 
     def serializable_config(self, query_config: MetricQueryConfig):
         # Use a simple python sql model to generate the time_aggregation model
@@ -558,195 +518,3 @@ def timeseries_metrics(
         GLOBAL_TIMESERIES_METRICS[calling_file] = timeseries_metrics
 
     return timeseries_metrics.generate_models(calling_file)
-
-
-def join_all_of_entity_type(
-    evaluator: MacroEvaluator, *, db: str, tables: t.List[str], columns: t.List[str]
-):
-    # A bit of a hack but we know we have a "metric" column. We want to
-    # transform this metric id to also include the event_source as a prefix to
-    # that metric id in the joined table
-    transformed_columns = []
-    for column in columns:
-        if column == "event_source":
-            continue
-        if column == "metric":
-            transformed_columns.append(
-                exp.alias_(
-                    exp.Concat(
-                        expressions=[
-                            exp.to_column("event_source"),
-                            exp.Literal(this="_", is_string=True),
-                            exp.to_column(column),
-                        ],
-                        safe=False,
-                        coalesce=False,
-                    ),
-                    alias="metric",
-                )
-            )
-        else:
-            transformed_columns.append(column)
-
-    query = exp.select(*transformed_columns).from_(sql.to_table(f"{db}.{tables[0]}"))
-    for table in tables[1:]:
-        query = query.union(
-            exp.select(*transformed_columns).from_(sql.to_table(f"{db}.{table}")),
-            distinct=False,
-        )
-    # Calculate the correct metric_id for all of the entity types
-    return query
-
-
-def generated_query(
-    evaluator: MacroEvaluator,
-    *,
-    rendered_query_str: str,
-    ref: PeerMetricDependencyRef,
-    table_name: str,
-    vars: t.Dict[str, t.Any],
-):
-    """Simple generated query executor for metrics queries"""
-
-    with metric_ref_evaluator_context(evaluator, ref, vars):
-        result = evaluator.transform(parse_one(rendered_query_str))
-    return result
-
-
-def generated_rolling_query(
-    context: ExecutionContext,
-    start: datetime,
-    end: datetime,
-    execution_time: datetime,
-    ref: PeerMetricDependencyRef,
-    vars: t.Dict[str, t.Any],
-    rendered_query_str: str,
-    table_name: str,
-    sqlmesh_vars: t.Dict[str, t.Any],
-    gateway: str | None,
-    *_ignored,
-):
-    """Generates a rolling query for the given metrics query
-
-    If SQLMESH_MCS_ENABLED is set to true, the query will be sent to the metrics
-    calculation service. Otherwise, the query will be executed as a rolling
-    query using dataframes (this can be very slow on remote data sources of
-    non-trivial size).
-
-    This currently takes advantage of a potential hack in sqlmesh. The snapshot
-    evaluator nor the scheduler in sqlmesh seem to care what the response is
-    from the python model as long as it's either a dataframe OR a sqlglot
-    expression. This means we can return a sqlglot expression that takes the
-    ExportReference from the metrics calculation service and use it as a table
-    in the sqlmesh query.
-
-    If configured correctly, the metrics calculation service will export a trino
-    database in production and if testing, it can export a "LOCALFS" export
-    which gives you a path to parquet file with random data (that satisfies the
-    requested schema).
-    """
-    # Transform the query for the current context
-    transformer = SQLTransformer(transforms=[ExecutionContextTableTransform(context)])
-    query = transformer.transform(rendered_query_str)
-    locals = vars.copy()
-    locals.update(sqlmesh_vars)
-
-    mcs_enabled = env.ensure_bool("SQLMESH_MCS_ENABLED", False)
-    if not mcs_enabled:
-        runner = MetricsRunner.from_sqlmesh_context(context, query, ref, locals)
-        df = runner.run_rolling(start, end)
-        # If the rolling window is empty we need to yield from an empty tuple
-        # otherwise sqlmesh fails. See:
-        # https://sqlmesh.readthedocs.io/en/latest/concepts/models/python_models/#returning-empty-dataframes
-        total = 0
-        if df.empty:
-            yield from ()
-        else:
-            count = len(df)
-            total += count
-            logger.debug(f"table={table_name} yielding rows {count}")
-            yield df
-        logger.debug(f"table={table_name} yielded rows{total}")
-    else:
-        logger.info("metrics calculation service enabled")
-
-        mcs_url = env.required_str("SQLMESH_MCS_URL")
-        mcs_client = Client.from_url(url=mcs_url)
-
-        columns = [
-            (col_name, col_type.sql(dialect="duckdb"))
-            for col_name, col_type in METRICS_COLUMNS_BY_ENTITY[
-                ref["entity_type"]
-            ].items()
-        ]
-
-        response = mcs_client.calculate_metrics(
-            query_str=rendered_query_str,
-            start=start,
-            end=end,
-            dialect="duckdb",
-            batch_size=env.ensure_int("SQLMESH_MCS_BATCH_SIZE", 10),
-            columns=columns,
-            ref=ref,
-            slots=ref.get("slots", env.ensure_int("SQLMESH_DEFAULT_MCS_SLOTS", 2)),
-            locals=sqlmesh_vars,
-            dependent_tables_map=create_dependent_tables_map(
-                context, rendered_query_str
-            ),
-            job_retries=env.ensure_int("SQLMESH_MCS_JOB_RETRIES", 5),
-            cluster_min_size=env.ensure_int("SQLMESH_MCS_CLUSTER_MIN_SIZE", 0),
-            cluster_max_size=env.ensure_int("SQLMESH_MCS_CLUSTER_MAX_SIZE", 30),
-        )
-
-        column_names = list(map(lambda col: col[0], columns))
-        engine_dialect = context.engine_adapter.dialect
-
-        if engine_dialect == "duckdb":
-            if response.type not in [ExportType.GCS, ExportType.LOCALFS]:
-                raise Exception(f"ExportType={response.type} not supported for duckdb")
-            # Create a select query from the exported data
-            path = response.payload.get("local_path", response.payload.get("gcs_path"))
-            select_query = exp.select(*column_names).from_(
-                exp.Anonymous(
-                    this="read_parquet",
-                    expressions=[exp.Literal(this=path, is_string=True)],
-                ),
-            )
-        elif engine_dialect == "trino":
-            if response.type not in [ExportType.TRINO]:
-                raise Exception(f"ExportType={response.type} not supported for trino")
-            select_query = exp.select(*column_names).from_(response.table_fqn())
-        else:
-            raise Exception(f"Dialect={context.engine_adapter.dialect} not supported")
-        yield select_query
-
-
-def generated_rolling_query_proxy(
-    context: ExecutionContext,
-    start: datetime,
-    end: datetime,
-    execution_time: datetime,
-    ref: PeerMetricDependencyRef,
-    vars: t.Dict[str, t.Any],
-    rendered_query_str: str,
-    table_name: str,
-    sqlmesh_vars: t.Dict[str, t.Any],
-    **kwargs,
-) -> t.Iterator[pd.DataFrame | exp.Expression]:
-    """This acts as the proxy to the actual function that we'd call for
-    the metrics model."""
-
-    yield from generated_rolling_query(
-        context,
-        start,
-        end,
-        execution_time,
-        ref,
-        vars,
-        rendered_query_str,
-        table_name,
-        sqlmesh_vars,
-        context.gateway,
-        # Change the following variable to force reevaluation. Hack for now.
-        "version=v5",
-    )
diff --git a/warehouse/metrics_tools/factory/generated.py b/warehouse/metrics_tools/factory/generated.py
new file mode 100644
index 000000000..200447757
--- /dev/null
+++ b/warehouse/metrics_tools/factory/generated.py
@@ -0,0 +1,124 @@
+import logging
+import typing as t
+from datetime import datetime
+
+from metrics_tools.compute.client import Client
+from metrics_tools.compute.types import ExportType
+from metrics_tools.definition import PeerMetricDependencyRef
+from metrics_tools.factory.constants import METRICS_COLUMNS_BY_ENTITY
+from metrics_tools.runner import MetricsRunner
+from metrics_tools.transformer import SQLTransformer
+from metrics_tools.transformer.tables import ExecutionContextTableTransform
+from metrics_tools.utils import env
+from metrics_tools.utils.tables import create_dependent_tables_map
+from sqlglot import exp
+from sqlmesh import ExecutionContext
+
+logger = logging.getLogger(__name__)
+
+
+def generated_rolling_query(
+    context: ExecutionContext,
+    start: datetime,
+    end: datetime,
+    execution_time: datetime,
+    ref: PeerMetricDependencyRef,
+    vars: t.Dict[str, t.Any],
+    rendered_query_str: str,
+    table_name: str,
+    gateway: str | None,
+    *_ignored,
+):
+    """Generates a rolling query for the given metrics query
+
+    If SQLMESH_MCS_ENABLED is set to true, the query will be sent to the metrics
+    calculation service. Otherwise, the query will be executed as a rolling
+    query using dataframes (this can be very slow on remote data sources of
+    non-trivial size).
+
+    This currently takes advantage of a potential hack in sqlmesh. The snapshot
+    evaluator nor the scheduler in sqlmesh seem to care what the response is
+    from the python model as long as it's either a dataframe OR a sqlglot
+    expression. This means we can return a sqlglot expression that takes the
+    ExportReference from the metrics calculation service and use it as a table
+    in the sqlmesh query.
+
+    If configured correctly, the metrics calculation service will export a trino
+    database in production and if testing, it can export a "LOCALFS" export
+    which gives you a path to parquet file with random data (that satisfies the
+    requested schema).
+    """
+    # Transform the query for the current context
+    transformer = SQLTransformer(transforms=[ExecutionContextTableTransform(context)])
+    query = transformer.transform(rendered_query_str)
+
+    mcs_enabled = env.ensure_bool("SQLMESH_MCS_ENABLED", False)
+    if not mcs_enabled:
+        runner = MetricsRunner.from_sqlmesh_context(
+            context, query, ref, context._variables.copy()
+        )
+        df = runner.run_rolling(start, end)
+        # If the rolling window is empty we need to yield from an empty tuple
+        # otherwise sqlmesh fails. See:
+        # https://sqlmesh.readthedocs.io/en/latest/concepts/models/python_models/#returning-empty-dataframes
+        total = 0
+        if df.empty:
+            yield from ()
+        else:
+            count = len(df)
+            total += count
+            logger.debug(f"table={table_name} yielding rows {count}")
+            yield df
+        logger.debug(f"table={table_name} yielded rows{total}")
+    else:
+        logger.info("metrics calculation service enabled")
+
+        mcs_url = env.required_str("SQLMESH_MCS_URL")
+        mcs_client = Client.from_url(url=mcs_url)
+
+        columns = [
+            (col_name, col_type.sql(dialect="duckdb"))
+            for col_name, col_type in METRICS_COLUMNS_BY_ENTITY[
+                ref["entity_type"]
+            ].items()
+        ]
+
+        response = mcs_client.calculate_metrics(
+            query_str=rendered_query_str,
+            start=start,
+            end=end,
+            dialect="duckdb",
+            batch_size=env.ensure_int("SQLMESH_MCS_BATCH_SIZE", 10),
+            columns=columns,
+            ref=ref,
+            slots=ref.get("slots", env.ensure_int("SQLMESH_DEFAULT_MCS_SLOTS", 2)),
+            locals=context._variables,
+            dependent_tables_map=create_dependent_tables_map(
+                context, rendered_query_str
+            ),
+            job_retries=env.ensure_int("SQLMESH_MCS_JOB_RETRIES", 5),
+            cluster_min_size=env.ensure_int("SQLMESH_MCS_CLUSTER_MIN_SIZE", 0),
+            cluster_max_size=env.ensure_int("SQLMESH_MCS_CLUSTER_MAX_SIZE", 30),
+        )
+
+        column_names = list(map(lambda col: col[0], columns))
+        engine_dialect = context.engine_adapter.dialect
+
+        if engine_dialect == "duckdb":
+            if response.type not in [ExportType.GCS, ExportType.LOCALFS]:
+                raise Exception(f"ExportType={response.type} not supported for duckdb")
+            # Create a select query from the exported data
+            path = response.payload.get("local_path", response.payload.get("gcs_path"))
+            select_query = exp.select(*column_names).from_(
+                exp.Anonymous(
+                    this="read_parquet",
+                    expressions=[exp.Literal(this=path, is_string=True)],
+                ),
+            )
+        elif engine_dialect == "trino":
+            if response.type not in [ExportType.TRINO]:
+                raise Exception(f"ExportType={response.type} not supported for trino")
+            select_query = exp.select(*column_names).from_(response.table_fqn())
+        else:
+            raise Exception(f"Dialect={context.engine_adapter.dialect} not supported")
+        yield select_query
diff --git a/warehouse/metrics_tools/factory/proxy/__init__.py b/warehouse/metrics_tools/factory/proxy/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/warehouse/metrics_tools/factory/proxy/proxies.py b/warehouse/metrics_tools/factory/proxy/proxies.py
new file mode 100644
index 000000000..9628129f1
--- /dev/null
+++ b/warehouse/metrics_tools/factory/proxy/proxies.py
@@ -0,0 +1,102 @@
+import typing as t
+from datetime import datetime
+
+import pandas as pd
+import sqlglot as sql
+from metrics_tools.definition import PeerMetricDependencyRef
+from metrics_tools.factory.generated import generated_rolling_query
+from metrics_tools.factory.utils import metric_ref_evaluator_context
+from sqlglot import exp
+from sqlmesh import ExecutionContext
+from sqlmesh.core.dialect import parse_one
+from sqlmesh.core.macros import MacroEvaluator
+
+
+def generated_query(
+    evaluator: MacroEvaluator,
+):
+    """Simple generated query executor for metrics queries"""
+    rendered_query_str = t.cast(str, evaluator.var("rendered_query_str"))
+    ref = t.cast(PeerMetricDependencyRef, evaluator.var("ref"))
+
+    with metric_ref_evaluator_context(evaluator, ref):
+        result = evaluator.transform(parse_one(rendered_query_str))
+    return result
+
+
+def generated_rolling_query_proxy(
+    context: ExecutionContext,
+    start: datetime,
+    end: datetime,
+    execution_time: datetime,
+    # *,
+    # ref: PeerMetricDependencyRef,
+    # vars: t.Dict[str, t.Any],
+    # rendered_query_str: str,
+    # table_name: str,
+    # sqlmesh_vars: t.Dict[str, t.Any],
+    **kwargs,
+) -> t.Iterator[pd.DataFrame | exp.Expression]:
+    """This acts as the proxy to the actual function that we'd call for
+    the metrics model."""
+    ref = t.cast(PeerMetricDependencyRef, context.var("ref"))
+    vars = t.cast(t.Dict[str, t.Any], context.var("vars"))
+    rendered_query_str = t.cast(str, context.var("rendered_query_str"))
+    table_name = t.cast(str, context.var("table_name"))
+
+    yield from generated_rolling_query(
+        context,
+        start,
+        end,
+        execution_time,
+        ref,
+        vars,
+        rendered_query_str,
+        table_name,
+        context.gateway,
+        # Change the following variable to force reevaluation. Hack for now.
+        "version=v5",
+    )
+
+
+def join_all_of_entity_type(
+    evaluator: MacroEvaluator,
+):
+    # A bit of a hack but we know we have a "metric" column. We want to
+    # transform this metric id to also include the event_source as a prefix to
+    # that metric id in the joined table
+
+    db = t.cast(str, evaluator.var("db"))
+    tables: t.List[str] = t.cast(t.List[str], evaluator.var("tables"))
+    columns: t.List[str] = t.cast(t.List[str], evaluator.var("columns"))
+
+    transformed_columns = []
+    for column in columns:
+        if column == "event_source":
+            continue
+        if column == "metric":
+            transformed_columns.append(
+                exp.alias_(
+                    exp.Concat(
+                        expressions=[
+                            exp.to_column("event_source"),
+                            exp.Literal(this="_", is_string=True),
+                            exp.to_column(column),
+                        ],
+                        safe=False,
+                        coalesce=False,
+                    ),
+                    alias="metric",
+                )
+            )
+        else:
+            transformed_columns.append(column)
+
+    query = exp.select(*transformed_columns).from_(sql.to_table(f"{db}.{tables[0]}"))
+    for table in tables[1:]:
+        query = query.union(
+            exp.select(*transformed_columns).from_(sql.to_table(f"{db}.{table}")),
+            distinct=False,
+        )
+    # Calculate the correct metric_id for all of the entity types
+    return query
diff --git a/warehouse/metrics_tools/factory/utils.py b/warehouse/metrics_tools/factory/utils.py
new file mode 100644
index 000000000..3a1d7fd8a
--- /dev/null
+++ b/warehouse/metrics_tools/factory/utils.py
@@ -0,0 +1,27 @@
+import typing as t
+from contextlib import contextmanager
+
+from metrics_tools.definition import PeerMetricDependencyRef
+from sqlmesh.core.macros import MacroEvaluator
+
+
+@contextmanager
+def metric_ref_evaluator_context(
+    evaluator: MacroEvaluator,
+    ref: PeerMetricDependencyRef,
+    extra_vars: t.Optional[t.Dict[str, t.Any]] = None,
+):
+    before = evaluator.locals.copy()
+    evaluator.locals.update(extra_vars or {})
+    evaluator.locals.update(
+        {
+            "rolling_window": ref.get("window"),
+            "rolling_unit": ref.get("unit"),
+            "time_aggregation": ref.get("time_aggregation"),
+            "entity_type": ref.get("entity_type"),
+        }
+    )
+    try:
+        yield
+    finally:
+        evaluator.locals = before
diff --git a/warehouse/metrics_tools/models.py b/warehouse/metrics_tools/models.py
index 22fff8646..f2be3e152 100644
--- a/warehouse/metrics_tools/models.py
+++ b/warehouse/metrics_tools/models.py
@@ -1,19 +1,14 @@
 import inspect
 import json
 import logging
-import re
 import textwrap
 import typing as t
 import uuid
 from pathlib import Path
 
-from sqlglot import exp
 from sqlmesh.core import constants as c
-from sqlmesh.core.dialect import MacroFunc
 from sqlmesh.core.macros import ExecutableOrMacro, MacroRegistry, macro
 from sqlmesh.core.model.decorator import model
-from sqlmesh.core.model.definition import create_python_model, create_sql_model
-from sqlmesh.utils.jinja import JinjaMacroRegistry
 from sqlmesh.utils.metaprogramming import (
     Executable,
     ExecutableKind,
@@ -27,248 +22,39 @@
 CallableAliasList = t.List[t.Callable | t.Tuple[t.Callable, t.List[str]]]
 
 
-class GeneratedPythonModel:
-    @classmethod
-    def create(
-        cls,
-        *,
-        name: str,
-        entrypoint_path: str,
-        func: t.Callable,
-        columns: t.Dict[str, exp.DataType],
-        additional_macros: t.Optional[CallableAliasList] = None,
-        variables: t.Optional[t.Dict[str, t.Any]] = None,
-        imports: t.Optional[t.Dict[str, t.Any]] = None,
-        **kwargs,
-    ):
-        instance = cls(
-            name=name,
-            entrypoint_path=entrypoint_path,
-            func=func,
-            additional_macros=additional_macros or [],
-            variables=variables or {},
-            columns=columns,
-            imports=imports or {},
-            **kwargs,
-        )
-        registry = model.registry()
-        registry[name] = instance
-        return instance
-
+class MacroOverridingModel(model):
     def __init__(
         self,
-        *,
-        name: str,
-        entrypoint_path: str,
-        func: t.Callable,
+        override_module_path: Path,
+        override_path: Path,
+        locals: t.Dict[str, t.Any],
         additional_macros: CallableAliasList,
-        variables: t.Dict[str, t.Any],
-        columns: t.Dict[str, exp.DataType],
-        imports: t.Dict[str, t.Any],
         **kwargs,
     ):
-        self.name = name
-        self._func = func
-        self._entrypoint_path = entrypoint_path
+        super().__init__(**kwargs)
         self._additional_macros = additional_macros
-        self._variables = variables
-        self._kwargs = kwargs
-        self._columns = columns
-        self._imports = imports
+        self._locals = locals
+        self._override_module_path = override_module_path
+        self._override_path = override_path
 
-    def model(
-        self,
-        module_path: Path,
-        path: Path,
-        defaults: t.Optional[t.Dict[str, t.Any]] = None,
-        macros: t.Optional[MacroRegistry] = None,
-        jinja_macros: t.Optional[JinjaMacroRegistry] = None,
-        dialect: t.Optional[str] = None,
-        time_column_format: str = c.DEFAULT_TIME_COLUMN_FORMAT,
-        physical_schema_mapping: t.Optional[t.Dict[re.Pattern, str]] = None,
-        project: str = "",
-        default_catalog: t.Optional[str] = None,
-        variables: t.Optional[t.Dict[str, t.Any]] = None,
-        infer_names: t.Optional[bool] = False,
-    ):
-        fake_module_path = Path(self._entrypoint_path)
-        macros = MacroRegistry(f"macros_for_{self.name}")
-        macros.update(macros or macro.get_registry())
-
-        if self._additional_macros:
-            macros.update(create_macro_registry_from_list(self._additional_macros))
-
-        all_vars = self._variables.copy()
-        global_variables = variables or {}
-        all_vars.update(global_variables)
-        all_vars["sqlmesh_vars"] = global_variables
-
-        common_kwargs: t.Dict[str, t.Any] = dict(
-            defaults=defaults,
-            path=path,
-            time_column_format=time_column_format,
-            physical_schema_mapping=physical_schema_mapping,
-            project=project,
-            default_catalog=default_catalog,
-            variables=all_vars,
-            **self._kwargs,
-        )
+    def model(self, *args, **kwargs):
+        if len(self._additional_macros) > 0:
+            macros = MacroRegistry(f"macros_for_{self.name}")
+            system_macros = kwargs.get("macros", macros)
+            macros.update(system_macros or macro.get_registry())
 
-        env = {}
-        python_env = create_basic_python_env(
-            env,
-            self._entrypoint_path,
-            module_path,
-            macros=macros,
-            callables=[self._func],
-            imports=self._imports,
-        )
+            macros = create_macro_registry_from_list(self._additional_macros)
+            kwargs["macros"] = macros
 
-        return create_python_model(
-            self.name,
-            self._func.__name__,
-            python_env,
-            macros=macros,
-            module_path=fake_module_path,
-            jinja_macros=jinja_macros,
-            columns=self._columns,
-            dialect=dialect,
-            **common_kwargs,
-        )
+        vars = kwargs.get("variables", {})
+        if len(self._locals) > 0:
+            vars.update(self._locals)
 
+        kwargs["variables"] = vars
+        kwargs["module_path"] = self._override_module_path
+        kwargs["path"] = self._override_path
 
-class GeneratedModel:
-    @classmethod
-    def create(
-        cls,
-        *,
-        func: t.Callable[..., t.Any],
-        config: t.Mapping[str, t.Any],
-        name: str,
-        columns: t.Dict[str, exp.DataType],
-        entrypoint_path: str,
-        source: t.Optional[str] = None,
-        source_loader: t.Optional[t.Callable[[], str]] = None,
-        additional_macros: t.Optional[CallableAliasList] = None,
-        **kwargs,
-    ):
-        if not source and not source_loader:
-            try:
-                source = inspect.getsource(func)
-            except:  # noqa: E722
-                pass
-
-        assert (
-            source is not None or source_loader is not None
-        ), "Must have a way to load the source for state diffs"
-
-        instance = cls(
-            func_name=func.__name__,
-            import_module=func.__module__,
-            config=config,
-            name=name,
-            columns=columns,
-            entrypoint_path=entrypoint_path,
-            source=source,
-            source_loader=source_loader,
-            additional_macros=additional_macros,
-            **kwargs,
-        )
-        registry = model.registry()
-        registry[name] = instance
-        return instance
-
-    def __init__(
-        self,
-        *,
-        func_name: str,
-        import_module: str,
-        config: t.Mapping[str, t.Any],
-        name: str,
-        columns: t.Dict[str, exp.DataType],
-        entrypoint_path: str,
-        source: t.Optional[str] = None,
-        source_loader: t.Optional[t.Callable[[], str]] = None,
-        additional_macros: t.Optional[CallableAliasList] = None,
-        **kwargs,
-    ):
-        self.kwargs = kwargs
-        self.func_name = func_name
-        self.import_module = import_module
-        self.config = config
-        self.name = name
-        self.columns = columns
-        self.entrypoint_path = entrypoint_path
-        self.source_loader = source_loader
-        self.source = source
-        self.additional_macros = additional_macros or []
-
-    def model(
-        self,
-        *,
-        module_path: Path,
-        path: Path,
-        defaults: t.Optional[t.Dict[str, t.Any]] = None,
-        macros: t.Optional[MacroRegistry] = None,
-        jinja_macros: t.Optional[JinjaMacroRegistry] = None,
-        dialect: t.Optional[str] = None,
-        time_column_format: str = c.DEFAULT_TIME_COLUMN_FORMAT,
-        physical_schema_mapping: t.Optional[t.Dict[re.Pattern, str]] = None,
-        project: str = "",
-        default_catalog: t.Optional[str] = None,
-        variables: t.Optional[t.Dict[str, t.Any]] = None,
-        infer_names: t.Optional[bool] = False,
-    ):
-        macros = macros or macro.get_registry()
-        fake_module_path = Path(self.entrypoint_path)
-
-        if self.additional_macros:
-            macros = t.cast(MacroRegistry, macros.copy())
-            macros.update(create_macro_registry_from_list(self.additional_macros))
-
-        common_kwargs: t.Dict[str, t.Any] = dict(
-            defaults=defaults,
-            path=fake_module_path,
-            time_column_format=time_column_format,
-            physical_schema_mapping=physical_schema_mapping,
-            project=project,
-            default_catalog=default_catalog,
-            variables=variables,
-            **self.kwargs,
-        )
-
-        source = self.source
-        if not source:
-            if self.source_loader:
-                source = self.source_loader()
-        assert source is not None, "source cannot be empty"
-
-        env = {}
-
-        entrypoint_name, env = create_import_call_env(
-            self.func_name,
-            self.import_module,
-            self.config,
-            source,
-            env,
-            fake_module_path,
-            project_path=module_path,
-            macros=macros,
-            variables=variables,
-        )
-        common_kwargs["python_env"] = env
-
-        query = MacroFunc(this=exp.Anonymous(this=entrypoint_name))
-        if self.columns:
-            common_kwargs["columns"] = self.columns
-
-        return create_sql_model(
-            self.name,
-            query,
-            module_path=fake_module_path,
-            jinja_macros=jinja_macros,
-            **common_kwargs,
-        )
+        return super().model(*args, **kwargs)
 
 
 def escape_triple_quotes(input_string: str) -> str:
diff --git a/warehouse/metrics_tools/transformer/tables.py b/warehouse/metrics_tools/transformer/tables.py
index 09cf39c43..300fcab72 100644
--- a/warehouse/metrics_tools/transformer/tables.py
+++ b/warehouse/metrics_tools/transformer/tables.py
@@ -6,6 +6,7 @@
 
 from sqlglot import exp
 from sqlmesh.core.context import ExecutionContext
+
 from .base import Transform
 
 logger = logging.getLogger(__name__)
@@ -56,8 +57,8 @@ def transform_table_name(self, table: exp.Table) -> exp.Table | None:
         table_name = f"{table.db}.{table.this.this}"
         try:
             logger.debug(
-                f"Transforming tables for query {self._context.table(table_name)}"
+                f"Transforming tables for query {self._context.resolve_table(table_name)}"
             )
-            return exp.to_table(self._context.table(table_name))
+            return exp.to_table(self._context.resolve_table(table_name))
         except KeyError:
             return None
diff --git a/warehouse/metrics_tools/utils/tables.py b/warehouse/metrics_tools/utils/tables.py
index 1226f39da..856552067 100644
--- a/warehouse/metrics_tools/utils/tables.py
+++ b/warehouse/metrics_tools/utils/tables.py
@@ -26,7 +26,7 @@ def resolve_table_name(
 ) -> t.Tuple[str, str]:
     table_fqn = resolve_table_fqn(table)
 
-    return (table_fqn, context.table(table_fqn))
+    return (table_fqn, context.resolve_table(table_fqn))
 
 
 def list_query_table_dependencies(
@@ -92,6 +92,6 @@ def create_dependent_tables_map(
         raise ValueError("Failed to build scope")
     # tables_map = resolve_table_map_from_scope(context, scope)
     tables = list_query_table_dependencies(query, {})
-    tables_map = {table: context.table(table) for table in tables}
+    tables_map = {table: context.resolve_table(table) for table in tables}
 
     return tables_map
diff --git a/warehouse/metrics_tools/utils/test_tables.py b/warehouse/metrics_tools/utils/test_tables.py
index 4db0b6a81..7415465cb 100644
--- a/warehouse/metrics_tools/utils/test_tables.py
+++ b/warehouse/metrics_tools/utils/test_tables.py
@@ -7,7 +7,7 @@
 
 def test_create_dependent_tables_map():
     mock = MagicMock(name="context")
-    mock.table.return_value = "test_table"
+    mock.resolve_table.return_value = "test_table"
 
     actual_tables_map = create_dependent_tables_map(mock, "select * from foo")
     expected_tables_map = {
@@ -125,7 +125,7 @@ def test_create_dependent_tables_map_parameterized(
     input: str, expected: t.Dict[str, str]
 ):
     mock = MagicMock(name="context")
-    mock.table.return_value = "test_table"
+    mock.resolve_table.return_value = "test_table"
 
     actual_tables_map = create_dependent_tables_map(mock, input)
     assert actual_tables_map == expected
diff --git a/warehouse/oso_dagster/cbt/transforms/test_transforms.py b/warehouse/oso_dagster/cbt/transforms/test_transforms.py
index 5aa4dac7d..ff58af0bc 100644
--- a/warehouse/oso_dagster/cbt/transforms/test_transforms.py
+++ b/warehouse/oso_dagster/cbt/transforms/test_transforms.py
@@ -39,18 +39,18 @@ def db():
         {
             "time_and_id": [
                 ["time", "id"],
-                [arrow.get("2024-01-01").datetime, 1],
-                [arrow.get("2024-02-01").datetime, 2],
-                [arrow.get("2024-03-01").datetime, 3],
-                [arrow.get("2024-04-01").datetime, 4],
+                [arrow.get("2024-01-01", tzinfo="UTC").datetime, 1],
+                [arrow.get("2024-02-01", tzinfo="UTC").datetime, 2],
+                [arrow.get("2024-03-01", tzinfo="UTC").datetime, 3],
+                [arrow.get("2024-04-01", tzinfo="UTC").datetime, 4],
             ],
             "time_id_and_name": [
                 ["time", "id", "name"],
-                [arrow.get("2024-01-01").datetime, 1, "alpha"],
-                [arrow.get("2024-02-01").datetime, 2, "bravo"],
-                [arrow.get("2024-03-01").datetime, 3, "charlie"],
-                [arrow.get("2024-04-01").datetime, 4, "delta"],
-                [arrow.get("2024-02-01").datetime, 5, "foxtrot"],
+                [arrow.get("2024-01-01", tzinfo="UTC").datetime, 1, "alpha"],
+                [arrow.get("2024-02-01", tzinfo="UTC").datetime, 2, "bravo"],
+                [arrow.get("2024-03-01", tzinfo="UTC").datetime, 3, "charlie"],
+                [arrow.get("2024-04-01", tzinfo="UTC").datetime, 4, "delta"],
+                [arrow.get("2024-02-01", tzinfo="UTC").datetime, 5, "foxtrot"],
             ],
         }
     )
@@ -63,12 +63,15 @@ def db():
     [
         (
             SELECT_NO_CTE,
-            dict(start=arrow.get("2024-02-01"), end=arrow.get("2024-04-01")),
+            dict(
+                start=arrow.get("2024-02-01", tzinfo="UTC"),
+                end=arrow.get("2024-04-01", tzinfo="UTC"),
+            ),
             2,
         ),
         (
             SELECT_WITH_CTE,
-            dict(start=arrow.get("2024-02-01")),
+            dict(start=arrow.get("2024-02-01", tzinfo="UTC")),
             3,
         ),
     ],
@@ -93,7 +96,10 @@ def test_time_constrain_succeed(
         (
             SELECT_WITH_JOIN,
             "time_id_and_name",
-            dict(start=arrow.get("2024-02-01"), end=arrow.get("2024-04-01")),
+            dict(
+                start=arrow.get("2024-02-01", tzinfo="UTC"),
+                end=arrow.get("2024-04-01", tzinfo="UTC"),
+            ),
             2,
         ),
     ],
diff --git a/warehouse/oso_dagster/utils/testing/duckdb.py b/warehouse/oso_dagster/utils/testing/duckdb.py
index 06d8e1cf5..353997c50 100644
--- a/warehouse/oso_dagster/utils/testing/duckdb.py
+++ b/warehouse/oso_dagster/utils/testing/duckdb.py
@@ -1,7 +1,7 @@
-from typing import Optional, Dict, List
+from typing import Dict, List, Optional
 
-import pandas
 import duckdb
+import pandas
 
 
 class DuckDbFixture:
@@ -12,6 +12,7 @@ def setup(cls, tables: Optional[Dict[str, List[List]]] = None):
         tables = tables or dict()
         db = duckdb.connect()
         fixture = cls(db)
+        fixture.set_timezone()
         fixture.add_tables(tables)
         fixture.set_memory_limit("2MB")
         return fixture
@@ -36,6 +37,9 @@ def add_tables(self, tables: Dict[str, List]):
     def set_memory_limit(self, limit: str):
         self._db.sql(f"SET memory_limit = '{limit}';")
 
+    def set_timezone(self):
+        self._db.sql("SET TimeZone = 'UTC';")
+
     @property
     def db(self):
         return self._db