acryl-datahub 1.6.0.10rc1

DataHub ingestion framework and CLI — connect, extract, and push metadata from 50+ data sources into your DataHub catalog

DataHub SDK and CLI

DataHub's ingestion framework and CLI — pull metadata from 50+ data sources into your catalog, or push it programmatically from your own pipelines and applications.

Pull-based integrations crawl your data systems on a schedule: Snowflake, BigQuery, dbt, Looker, Airflow, and many more.

Push-based integrations let you emit metadata directly from code as it happens: Python SDK, Java SDK, Spark, Great Expectations, and others.

What you can do

• Pull metadata from databases, warehouses, BI tools, and orchestrators using 50+ ready-made connectors
• Push metadata programmatically using the Python or Java SDK
• Transform and filter metadata in transit using built-in transformers
• Schedule and manage ingestion pipelines via CLI or the DataHub UI
• Automate lineage, ownership, tags, and documentation across your data assets

Supported sources

Snowflake · BigQuery · Redshift · dbt · Databricks · Looker · Tableau · Power BI · Airflow · Spark · Kafka · PostgreSQL · MySQL · Hive · Glue · S3 · Iceberg · Unity Catalog · Sigma · Mode · Superset · Metabase · and many more

Installation

pip install acryl-datahub
datahub version

Quickstart

Pull metadata from a source (recipe)

# snowflake_recipe.yml
source:
  type: snowflake
  config:
    account_id: my_account
    username: my_user
    password: my_password
    role: DATAHUB_ROLE
    warehouse: COMPUTE_WH

sink:
  type: datahub-rest
  config:
    server: http://localhost:8080

datahub ingest -c snowflake_recipe.yml

Emit metadata from code (SDK)

from datahub.sdk import DataHubClient, Dataset

client = DataHubClient.from_env()

dataset = Dataset(platform="snowflake", name="mydb.schema.table")
dataset.set_description("My table description")
dataset.set_owners(["urn:li:corpuser:jane"])

client.entities.upsert(dataset)

Ingest via the DataHub UI

No CLI required — configure and run ingestion directly from the DataHub UI under Ingestion → Create new source.

Ingestion methods

Method	Best for
CLI + YAML recipe	Scheduled batch ingestion, CI/CD pipelines
Python SDK	Programmatic or event-driven metadata emission
Java SDK	JVM-based integrations (Spark, Flink, etc.)
UI Ingestion	One-click setup, no code required

Key CLI commands

datahub init                          # Connect to a DataHub instance (interactive)
datahub init --username datahub --password datahub  # Quickstart with local defaults
datahub ingest -c recipe.yml          # Run an ingestion pipeline
datahub search "my table"             # Search for entities by keyword
datahub search "*" --filter platform=snowflake --filter entity_type=dataset
datahub graphql --list-operations     # Explore all available GraphQL operations
datahub graphql --query "{ me { corpUser { urn } } }"  # Run a GraphQL query
datahub get --urn <urn>               # Fetch metadata for an entity
datahub delete --urn <urn>            # Delete a metadata entity
datahub timeline --urn <urn>          # View metadata change history

Links

• Documentation
• Integrations catalog
• Quickstart guide
• Developing a custom source
• GitHub
• Slack community