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acryl-datahub-airflow-plugin
PyPIDataHub Airflow plugin — automatically capture pipeline lineage, run history, and task metadata from Apache Airflow
Versions collected1
Direct dependencies34From release 1.7.0.14
Last observed20 Oct, 18:27 UTC
README
Version 1.7.0.14 documentationDataHub Airflow Plugin
Automatic lineage and run metadata from Apache Airflow into DataHub — captures DAG structure, task inputs/outputs, and run history with no manual instrumentation.
What you can do
- Capture pipeline lineage — automatically extract dataset-level and column-level lineage from SQL operators
- Track run history — record task execution status, duration, and failures in DataHub
- Enhance OpenLineage — patches Airflow's OpenLineage extractors with DataHub's advanced SQL parser for richer lineage
- Support multiple emitters — send metadata via REST, Kafka, or file
Version compatibility
| Airflow Version | Support |
|---|---|
| 3.0+ | ✅ Fully supported |
| 2.x | ❌ Use acryl-datahub-airflow-plugin <= 1.6.0 |
Installation
pip install acryl-datahub-airflow-plugin
# With Kafka emitter
pip install 'acryl-datahub-airflow-plugin[datahub-kafka]'
Configuration
Add to airflow.cfg:
[datahub]
enabled = True
conn_id = datahub_rest_default # Airflow connection pointing to your DataHub GMS
Set up the Airflow connection:
airflow connections add datahub_rest_default \
--conn-type HTTP \
--conn-host http://localhost:8080
The plugin activates automatically — no changes to your DAG code required.
Key configuration options
| Option | Default | Description |
|---|---|---|
enable_extractors | True | Enhance OpenLineage extractors |
patch_sql_parser | True | Use DataHub's SQL parser for column-level lineage |
enable_multi_statement_sql_parsing | False | Resolve temp tables across multi-statement tasks |
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Versions 1
Collected releases| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 1.7.0.14 | 29 Sept 2026 | 34 |
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package:72e735af6d5b2e92f0868fee57228c4aac8b17883956eace888e1f83a7db7009