DataHub Python Builds

These prebuilt wheel files can be used to install our Python packages as of a specific commit.

Build context

Built at 2026-09-12T19:39:36.255262+00:00.

{
  "timestamp": "2026-09-12T19:39:36.255262+00:00",
  "branch": "databricks-pipeline-expectations",
  "commit": {
    "hash": "43483d4edfd6d8511b2236b6614590cd7f632ae7",
    "message": "fix(unity): harden pipeline expectation extraction (PR review)\n\nAddress review feedback on the Lakeflow expectations extractor:\n\n- Per-pipeline target lookup failures are caught and reported, so one bad\n  pipeline no longer aborts extraction for the rest.\n- Read the pipeline event log to page-token exhaustion via a lazy generator,\n  stopping once the newest update is consumed. Removes the fixed 20-page cap\n  that could silently truncate events and undercount failures.\n- Include the metastore id in the assertion's dataset URN when\n  include_metastore is enabled, so it resolves to the published dataset.\n- Split dataset identifiers on unquoted dots (handles backtick-quoted parts).\n- Extract expectations before the warehouse-gated profiling block, so the\n  REST-only path still runs when the SQL warehouse fails to start.\n- Add tests for schema-qualified/quoted/metastore URN resolution, per-pipeline\n  target errors, and graceful degradation on malformed event payloads.\n\nCo-authored-by: Cursor "
  },
  "base": {
    "hash": "f965a174bad9c1026b36e9563a2d7d7a43c5ae66",
    "message": "refactor(unity): structure DQ completeness assertion like dbt tests\n\nEmit the Databricks data-quality completeness check as a structured custom\nassertion (scope/aggregation/operator/parameters/field) mirroring the dbt\nconnector, instead of a hand-written \"Completeness\" description that omits\nthe column. DataHub now renders \"Null count for column  is equal to 0\",\ngroups it under the \"Databricks\" provider, and surfaces \"completeness\" as\nthe native check type.\n\nCo-authored-by: Cursor "
  },
  "pr": {
    "number": 19755,
    "title": "feat(ingestion/unity): ingest Lakeflow pipeline expectations as assertions",
    "url": "https://github.com/datahub-project/datahub/pull/19755"
  }
}

Usage

Current base URL: unknown

Package	Size	Install command
acryl-datahub	5.192 MB	uv pip install 'acryl-datahub @ <base-url>/artifacts/wheels/acryl_datahub-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-actions	0.117 MB	uv pip install 'acryl-datahub-actions @ <base-url>/artifacts/wheels/acryl_datahub_actions-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-airflow-plugin	0.072 MB	uv pip install 'acryl-datahub-airflow-plugin @ <base-url>/artifacts/wheels/acryl_datahub_airflow_plugin-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-dagster-plugin	0.021 MB	uv pip install 'acryl-datahub-dagster-plugin @ <base-url>/artifacts/wheels/acryl_datahub_dagster_plugin-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-gx-plugin	0.019 MB	uv pip install 'acryl-datahub-gx-plugin @ <base-url>/artifacts/wheels/acryl_datahub_gx_plugin-0.0.0.dev1-py3-none-any.whl'
prefect-datahub	0.011 MB	uv pip install 'prefect-datahub @ <base-url>/artifacts/wheels/prefect_datahub-0.0.0.dev1-py3-none-any.whl'