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Install & Compatibility
Where this runs
tested against v1.6.0.4 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
build_error
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 31.0s · import 0.000s · 348MB
349MB installed
● package 349MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
DatahubDagsterHook
✓ from datahub_dagster_plugin.hooks import DatahubDagsterHook
✗ from datahub_dagster_plugin.hooks import DatahubDagsterHook
Define a simple Dagster job with a DatahubDagsterResource to emit metadata. Uses environment variable for GMS host.
import os
from dagster import job, op, OpExecutionContext
from datahub_dagster_plugin.resources import DatahubDagsterResource
from datahub.emitter.rest_emitter import DatahubRestEmitter
@op(required_resource_keys={'datahub'})
def my_op(context: OpExecutionContext):
context.log.info("Running op")
return 1
@job(resource_defs={
'datahub': DatahubDagsterResource(
emitter=DatahubRestEmitter(gms_server=os.environ.get('DATAHUB_GMS_HOST', 'http://localhost:8080'))
)
})
def my_job():
my_op()
if __name__ == '__main__':
result = my_job.execute_in_process()
Upgrade
Version history
1.6.0.4latest on PyPI · released Jun 18, 2026
Audit
Dependencies
datahubrequiredThe plugin depends on the core datahub library for emitting metadata to DataHub.
dagsterrequiredRequired for integrating with Dagster pipelines.
acryl-datahub-restoptionalOften used for REST-based ingestion to DataHub.