acryl-datahub
DataHub ingestion framework and CLI — connect, extract, and push metadata from 50+ data sources into your DataHub catalog
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. Install it if you need to ingest metadata from multiple data sources into DataHub or emit metadata from your own applications. It is the canonical way to connect external data systems to DataHub and is suitable for production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- DataHub server instance must be running and accessible (local or remote) for ingestion to succeed.
- Low friction install with a pure-wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions.
last release 2026-08-11 (3 days) · last repo commit 2026-08-13 · 34 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 4,304,751 downloads/mo, #2,338 on PyPI
Alternatives
Verify before relying
pip install acryl-datahub
# CLI usage
datahub init --username datahub --password datahub
datahub ingest -c recipe.yml
# SDK usage
from datahub.sdk import DataHubClient, Dataset
client = DataHubClient.from_env()
dataset = Dataset(platform="snowflake", name="mydb.schema.table")
client.entities.upsert(dataset)- Whether all 50+ listed source connectors are included in the base package or require separate plugin installation.
- Performance characteristics and scalability limits for large metadata volumes or frequent push operations.
- Whether the Python SDK supports all metadata entity types and transformations available in the CLI.
What it is and what it does
Acryl-datahub is DataHub's official ingestion framework and command-line tool for metadata management. It bridges your data infrastructure and a centralized metadata catalog by supporting both pull-based connectors (scheduled crawls of databases, warehouses, BI tools, and orchestrators) and push-based APIs (programmatic metadata emission from applications and data pipelines). The package includes 50+ ready-made source connectors, built-in metadata transformers, and a Python SDK for direct integration.
You use it either as a CLI tool with YAML recipe files for scheduled batch ingestion in CI/CD pipelines, or as a Python library to emit metadata events directly from your code as data flows through your systems. It automates the capture and propagation of lineage, ownership, tags, and documentation across your data assets, feeding them into a DataHub instance for discovery and governance.
Use it for
- Schedule automated metadata extraction from Snowflake, BigQuery, or Redshift warehouses on a daily or hourly cadence via YAML recipe.
- Emit dataset lineage and ownership metadata directly from a Python data pipeline or Spark job as it executes.
- Transform and filter metadata in transit using built-in transformers before ingestion into the catalog.
- Integrate dbt, Looker, or Airflow metadata into a centralized DataHub instance for cross-tool lineage visibility.
- Query and search the DataHub catalog programmatically or via CLI to find datasets, owners, and documentation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. Install it if you need to ingest metadata from multiple data sources into DataHub or emit metadata from your own applications. It is the canonical way to connect external data systems to DataHub and is suitable for production use.
Install
acryl-datahub on PyPI
Before you install
Low friction install with a pure-wheel distribution. Actively maintained with a release 3 days old and recent commits. Supports Python 3.10, 3.11, and 3.12.
Requires Python 3.10 or later. DataHub server instance must be running and accessible (local or remote) for ingestion to succeed.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install acryl-datahub
# CLI usage
datahub init --username datahub --password datahub
datahub ingest -c recipe.yml
# SDK usage
from datahub.sdk import DataHubClient, Dataset
client = DataHubClient.from_env()
dataset = Dataset(platform="snowflake", name="mydb.schema.table")
client.entities.upsert(dataset)Verify before relying
- Whether all 50+ listed source connectors are included in the base package or require separate plugin installation.
- Performance characteristics and scalability limits for large metadata volumes or frequent push operations.
- Whether the Python SDK supports all metadata entity types and transformations available in the CLI.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 33 packagesaiohttpavro-gen3avrocached_propertyclick-default-groupclick-spinnerclickcramjamDeprecateddockerexpandvarshumanfriendlyijsonjsonrefjsonschemamixpanelpackagingpipprogressbar2psutilpydanticpython-dateutilpython-json-loggerPyYAMLrequests_fileruamel.yamlsentry-sdksetuptoolstabulatetoml |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 4,304,751 / month, #2,338 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 :: 3.11Programming Language :: Python :: 3.12Topic :: Software Development |
Evidence: acryl_datahub-1.7.0.3-py3-none-any.whl
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See also acryl-datahub-actions · acryl-datahub-classify · acryl-executor · datahub · openmetadata-ingestion · acryl-datahub-dagster-plugin · acryl-datahub-airflow-plugin · ingestr · intake · abx-plugins