acryl-datahub-dagster-plugin
DataHub Dagster plugin — automatically capture asset lineage, run history, and job metadata from Dagster pipelines
Decision gist · record as of 2026-08-14
Yes—if you run Dagster and use acryl-datahub for data catalog and lineage, this plugin eliminates manual metadata instrumentation and keeps lineage synchronized automatically. Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a straightforward addition. Install only if you have both Dagster and acryl-datahub in your stack.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; requires a running Dagster instance and a DataHub deployment (self-hosted or Cloud).
- Low install friction with a pure-Python wheel.
- Active maintenance as of 2026-08-14 with no known vulnerabilities.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing use in commercial and private projects with minimal restrictions.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 34 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 126,808 downloads/mo, #11,765 on PyPI
Alternatives
Verify before relying
pip install acryl-datahub-dagster-plugin
from datahub.ingestion.graph.config import DatahubClientConfig
from datahub_dagster_plugin.sensors.datahub_sensors import DatahubDagsterSourceConfig, make_datahub_sensor
config = DatahubDagsterSourceConfig(
datahub_client_config=DatahubClientConfig(server="http://localhost:8080"),
dagster_url="http://localhost:3000",
)
datahub_sensor = make_datahub_sensor(config=config)- Whether the sensor handles all Dagster asset types and external dataset mappings without gaps.
- Performance impact on Dagster job execution when the sensor is active.
- Compatibility with specific DataHub versions or deployment configurations.
What it is and what it does
This package bridges Dagster and acryl-datahub by automatically extracting and forwarding pipeline metadata without manual instrumentation. It runs as a sensor that listens to job executions and captures asset definitions, upstream/downstream relationships, and run status, then pushes that lineage and metadata into acryl-datahub so your assets appear in the catalog alongside their execution history.
The plugin works with any acryl-datahub deployment—self-hosted or Cloud—and requires only configuration of the server endpoint and Dagster URL. Once registered in your Definitions, it operates transparently: every job run triggers metadata emission to acryl-datahub, building a continuous record of lineage and run outcomes without code changes to your existing Dagster jobs or assets.
Use it for
- Track data lineage across Dagster pipelines in a centralized catalog for compliance and impact analysis.
- Monitor job execution history and task outcomes without adding instrumentation to each Dagster job.
- Map Dagster assets to external datasets to understand cross-system data dependencies.
- Integrate Dagster metadata into an existing acryl-datahub deployment for unified data governance.
- Automatically capture upstream/downstream relationships between Dagster assets for data discovery.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes—if you run Dagster and use acryl-datahub for data catalog and lineage, this plugin eliminates manual metadata instrumentation and keeps lineage synchronized automatically.
Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a straightforward addition. Install only if you have both Dagster and acryl-datahub in your stack.
Install
acryl-datahub-dagster-plugin on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance as of 2026-08-14 with no known vulnerabilities. Requires three runtime dependencies: dagster, dagit, and acryl-datahub.
Requires Python 3.10 or later; requires a running Dagster instance and a DataHub deployment (self-hosted or Cloud).
License in practice
Licensed under Apache-2.0 (permissive), allowing use in commercial and private projects with minimal restrictions.
Quickstart
pip install acryl-datahub-dagster-plugin
from datahub.ingestion.graph.config import DatahubClientConfig
from datahub_dagster_plugin.sensors.datahub_sensors import DatahubDagsterSourceConfig, make_datahub_sensor
config = DatahubDagsterSourceConfig(
datahub_client_config=DatahubClientConfig(server="http://localhost:8080"),
dagster_url="http://localhost:3000",
)
datahub_sensor = make_datahub_sensor(config=config)Verify before relying
- Whether the sensor handles all Dagster asset types and external dataset mappings without gaps.
- Performance impact on Dagster job execution when the sensor is active.
- Compatibility with specific DataHub versions or deployment configurations.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesdagitdagsteracryl-datahub |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 126,808 / month, #11,765 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: MacOS XIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: System AdministratorsOperating System :: POSIX :: LinuxOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Topic :: Software Development |
Evidence: acryl_datahub_dagster_plugin-1.7.0.4-py3-none-any.whl
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See also acryl-datahub-airflow-plugin · acryl-datahub · acryl-datahub-actions · dagster · dagster-dbt · datahub · acryl-datahub-classify · dagster-webserver · acryl-executor · dagster-rest-resources