DataHub Python Builds

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

Build context

Built at 2026-07-08T09:23:03.484111+00:00.

{
  "timestamp": "2026-07-08T09:23:03.484111+00:00",
  "branch": "fix-snowflake-profiler-memory-issue",
  "commit": {
    "hash": "bb062bfebb39c3dbd4bf1424aaab4f75a5622cdf",
    "message": "fix(ingestion): implement temp table sampling in Snowflake SQLAlchemy profiler\n\nRoot cause: SQLAlchemy profiler queries massive production tables directly,\ncausing memory exhaustion. A 102B row table triggered 40-CTE cross-join queries\nthat hung the process and spiked memory to 10GB+.\n\nFix: Implement temp table sampling matching GE profiler's behavior:\n- Create temporary tables with TABLESAMPLE for large tables (>sample_size rows)\n- Use conditional sampling strategy matching GE exactly:\n  * BLOCK + BERNOULLI for very large tables (>50M rows)\n  * BERNOULLI only for smaller tables\n- Cache row counts to avoid redundant COUNT(*) queries\n- Clean up temp tables after profiling\n\nConfig-driven via existing use_sampling (default: True) and sample_size\n(default: 10,000 rows) settings.\n\nCo-Authored-By: Claude Sonnet 4.5 "
  },
  "base": {
    "hash": "00c042c598767669104b7d526ad6000c0b8e63d0",
    "message": "feat(zdu): Create end-to-end Zero-Downtime Upgrade testing framework. (#17512)\n\nCo-authored-by: Claude Opus 4.7 (1M context) "
  },
  "pr": {
    "number": 18253,
    "title": "fix(ingestion): implement temp table sampling in Snowflake SQLAlchemy profiler",
    "url": "https://github.com/datahub-project/datahub/pull/18253"
  }
}

Usage

Current base URL: unknown

Package	Size	Install command
acryl-datahub	4.403 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	0.011 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'