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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

Metadata

Release files for prefect-datahub 1.7.0.5rc1

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Source distribution for prefect-datahub 1.7.0.5rc1
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Table of built distributions (wheels) for prefect-datahub 1.7.0.5rc1
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prefect_datahub-1.7.0.5rc1-py3-none-any.whl Python 3 none any Details

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