DataHub Python Builds

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

Build context

Built at 2026-05-14T17:07:56.505012+00:00.

{
  "timestamp": "2026-05-14T17:07:56.505012+00:00",
  "branch": "feat/pgqueue-messaging",
  "commit": {
    "hash": "680567f1e6a9832791746f9f3d57f134ac26c190",
    "message": "feat(messaging): add pgQueue as alternative messaging transport to Kafka\n\nIntroduce a PostgreSQL-based queue (pgQueue) as an alternative messaging\ntransport, allowing DataHub to operate without Kafka dependencies for\nsmaller deployments.\n\nKey changes:\n- pgQueue store with partitioned message tables, consumer offsets, and\n  retention via pg_partman\n- Messaging transport abstraction layer (MessagingTransport conditions,\n  KafkaMessagingEnabled/Disabled) for seamless switching between Kafka\n  and pgQueue\n- Consumer/processor refactoring: split Kafka listeners from processors\n  to enable pgQueue poll-based consumption\n- SqlSetup framework with pgQueue schema steps, pg_cron maintenance,\n  and pg_partman integration\n- pgQueue event producer and usage event publisher abstraction\n- Consumer lag monitoring abstraction (ConsumerLagPort) for both\n  Kafka and pgQueue\n- Trace service pgQueue integration (pending/failed trace ports)\n- OpenAPI messaging operations controller for consumer registration\n  and lag inspection\n- External events service pgQueue support\n- Python pgQueue client, ingestion sink, and DataHub Actions event source\n- Docker postgres image with pg_cron and pg_partman extensions\n\nCo-authored-by: Cursor "
  },
  "base": {
    "hash": "23ba3a44b09cd53bee39b25e2ae0ab427093f85b",
    "message": "build(python): Organize the pipMirrorUrl overrides (#17442)"
  },
  "pr": {
    "number": 17446,
    "title": "feat(messaging): add pgQueue as alternative messaging transport to Kafka",
    "url": "https://github.com/datahub-project/datahub/pull/17446"
  }
}

Usage

Current base URL: unknown

Package	Size	Install command
acryl-datahub	3.847 MB	uv pip install 'acryl-datahub @ <base-url>/artifacts/wheels/acryl_datahub-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-actions	0.108 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	0.109 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	0.020 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'