DataHub Python Builds

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

Build context

Built at 2026-08-27T18:16:56.592833+00:00.

{
  "timestamp": "2026-08-27T18:16:56.592833+00:00",
  "branch": "bq-profiling/08-profiler-core",
  "commit": {
    "hash": "eb0158025b94b88c0831bf2467bebb014ba40c05",
    "message": "feat(ingest/bigquery): profiler core rewrite + BigQueryProfilingConfig\n\nRewrite the BigQuery profiler to support partition-aware batch kwargs,\ncustom-SQL profiling, row sampling, staleness skipping, date-window\npruning, and a per-dataset partition-metadata cache. Introduce\nBigQueryProfilingConfig (subclass of GEProfilingConfig) carrying the new\nprofiling knobs and add query_executor.py (timeout-bounded, injection-\nguarded query execution) as a profiler dependency.\n\nDefaults are opt-in: skip_stale_tables defaults to false and\npartition_datetime_window_days defaults to None, so profiling behaviour\nis unchanged on upgrade unless explicitly enabled. updating-datahub.md\ndocuments these as new opt-in options.\n\nExternal-table profiling logic ships here but stays inert behind\nprofile_external_tables (default false); it is wired up in the next PR.\n\nPart of the PR #12825 split (8/9).\n\nCo-authored-by: Cursor "
  },
  "base": {
    "hash": "c085f237880a93a0683b36ca091d4e2b93f9ad9c",
    "message": "refactor(ingest/bigquery): partition discovery scan/probe + max_partition_id (stack 7/9)\n\nFill in the scan/probe branches of PartitionDiscovery: column probing,\nsampling-based partition value discovery, per-partition data verification,\nreal-value search, the max_partition_id zero-scan fast path, and the\nattempt-logging/error-extraction helpers. The orchestrator now routes into\nthese real implementations; only the external-table branch remains a stub\n(PR 9/9).\n\nQuery/bytes behavior (reviewer question): the scan/probe path issues small,\nbounded metadata/probe queries per table (INFORMATION_SCHEMA reads, a\nGROUP BY on the partition column with a LIMIT, and SELECT 1 existence probes),\nand prefers the max_partition_id fast path (zero table-data scan) when\navailable before falling back to sampling. Each value fetch is bounded by\npartition_fetch_timeout; on timeout the table is skipped or profiled\nwithout a filter rather than scanning unboundedly.\n\nTests: the full PartitionDiscovery orchestrator scenario set now passes\n(max_partition_id, query-error detection, IS NOT NULL fallback, non-date\nmost-frequent value, compound partitions, INFORMATION_SCHEMA path).\n\nPart of the stack splitting #12825.\n\nCo-authored-by: Cursor "
  },
  "pr": {
    "number": 19486,
    "title": "feat(ingest/bigquery): profiler core rewrite + BigQueryProfilingConfig",
    "url": "https://github.com/datahub-project/datahub/pull/19486"
  }
}

Usage

Current base URL: unknown

Package	Size	Install command
acryl-datahub	5.092 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'