Pre-release
This release is a pre-release and may not be stable for production use.
DataHub Prefect Plugin
Automatic lineage and run metadata from Prefect into DataHub — captures flow structure, task inputs/outputs, and run history with minimal setup.
What you can do
- Emit flow and task metadata to DataHub as pipeline runs
- Capture dataset lineage — declare inputs and outputs per task and see them in DataHub
- Configure via Prefect blocks — store your DataHub connection settings as a reusable block
- Works with any DataHub deployment — self-hosted or DataHub Cloud
Installation
pip install prefect-datahub
Quickstart
1. Save your DataHub connection as a Prefect block
from prefect_datahub.datahub_emitter import DatahubEmitter
DatahubEmitter(
datahub_rest_url="http://localhost:8080",
env="PROD",
).save("my-datahub")
2. Use it in your flows
from prefect import flow, task
from prefect_datahub.datahub_emitter import DatahubEmitter
from prefect_datahub.entities import Dataset
emitter = DatahubEmitter.load("my-datahub")
@task
def transform(data, emitter):
emitter.add_task(
inputs=[Dataset("snowflake", "mydb.schema.source_table")],
outputs=[Dataset("snowflake", "mydb.schema.output_table")],
)
return data
@flow
def my_pipeline():
data = extract()
transform(data, emitter)
emitter.emit_flow() # required — emits all metadata at the end
Configuration options
| Option | Default | Description |
|---|---|---|
datahub_rest_url |
http://localhost:8080 |
DataHub GMS URL |
env |
PROD |
Environment tag for assets |
platform_instance |
None |
Platform instance for assets |
token |
None |
Auth token (if GMS auth is enabled) |
Links
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