When should I use Amundsen instead of CKAN?

Data discovery and metadata catalog with a search-first UI and social features like table popularity. Organizations wanting a Lyft-style data catalog with usage signals and human-curated descriptions. Teams needing a graph-backed metadata store with Neptune or Atlas as the metadata backend

When should I use CKAN instead of Amundsen?

Open data portals where government and research organizations publish public datasets. Hosting a self-managed data catalog with rich metadata, API access, and dataset versioning. Public data publishing workflows with citation, licensing, and provenance metadata

What are the main weaknesses of Amundsen?

Complex multi-component deployment: metadata service, search service, and frontend all required. Requires significant engineering effort to maintain connectors and keep metadata fresh. Smaller default connector set than DataHub; custom connectors require more implementation work

What are the main weaknesses of CKAN?

Complex to deploy and maintain — Python 2-era codebase with slow modernization. Not designed for internal enterprise data discovery; better suited to public open data portals. Limited lineage and governance features compared to modern enterprise data catalogs

Amundsen vs CKAN: Key Differences for Python Data Engineering

Data Governance & Metadata

Amundsen

Data Discovery & Metadata Engine

★ 4.5

Apache-2.0

pip install amundsen-common

CKAN

Open Data Management System

★ 4.1

AGPL-3.0

pip install ckanapi

Side-by-Side Comparison

Amundsen

CKAN

Amundsen

CKAN

Best For

✓Data discovery and metadata catalog with a search-first UI and social features like table popularity
✓Organizations wanting a Lyft-style data catalog with usage signals and human-curated descriptions
✓Teams needing a graph-backed metadata store with Neptune or Atlas as the metadata backend

✓Open data portals where government and research organizations publish public datasets
✓Hosting a self-managed data catalog with rich metadata, API access, and dataset versioning
✓Public data publishing workflows with citation, licensing, and provenance metadata

Best For

✓Data discovery and metadata catalog with a search-first UI and social features like table popularity
✓Organizations wanting a Lyft-style data catalog with usage signals and human-curated descriptions
✓Teams needing a graph-backed metadata store with Neptune or Atlas as the metadata backend

✓Open data portals where government and research organizations publish public datasets
✓Hosting a self-managed data catalog with rich metadata, API access, and dataset versioning
✓Public data publishing workflows with citation, licensing, and provenance metadata

Weaknesses

•Complex multi-component deployment: metadata service, search service, and frontend all required
•Requires significant engineering effort to maintain connectors and keep metadata fresh
•Smaller default connector set than DataHub; custom connectors require more implementation work

•Complex to deploy and maintain — Python 2-era codebase with slow modernization
•Not designed for internal enterprise data discovery; better suited to public open data portals
•Limited lineage and governance features compared to modern enterprise data catalogs

Weaknesses

•Complex multi-component deployment: metadata service, search service, and frontend all required
•Requires significant engineering effort to maintain connectors and keep metadata fresh
•Smaller default connector set than DataHub; custom connectors require more implementation work

•Complex to deploy and maintain — Python 2-era codebase with slow modernization
•Not designed for internal enterprise data discovery; better suited to public open data portals
•Limited lineage and governance features compared to modern enterprise data catalogs

License

Apache-2.0

AGPL-3.0

License

Apache-2.0

AGPL-3.0

Install

pip install amundsen-common

pip install ckanapi

Install

pip install amundsen-common

pip install ckanapi

Rating

★ 4.5

★ 4.1

Rating

★ 4.5

★ 4.1

Key Features

Amundsen

1Data discovery search engine built on metadata from multiple sources
2Automated table popularity ranking based on query frequency
3Data lineage graph connecting datasets across tools and systems
4Metadata ingestion connectors for BigQuery, Redshift, Snowflake, and more
5Python-based databuilder framework for custom metadata extraction

CKAN

1Open-source data portal platform used by governments and research institutions
2Dataset catalog with rich metadata, versioning, and resource attachments
3REST API for programmatic dataset discovery and download
4DataStore plugin provides a queryable database backend for tabular resources
5Extension system for custom themes, authentication, and integrations

How Python Data Engineers Use These Tools

Amundsen

Python data engineers use Amundsen's databuilder library to write custom extractor jobs that pull metadata from internal databases and push it to Amundsen's index. Engineers also use the Amundsen API to programmatically tag datasets with ownership, freshness SLAs, and quality tier labels that the search UI surfaces to data consumers.

CKAN

Python data engineers use the `ckanapi` library to programmatically harvest open datasets from CKAN portals — listing available datasets, downloading CSV or JSON resources, and ingesting them into internal pipelines. Government open data platforms (data.gov, data.gov.uk) run on CKAN, making it the standard entry point for public data ingestion workflows.

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Data Governance & Metadata

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Data Governance & Metadata

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Data Governance & Metadata

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Individual Tool Pages

View Amundsen details →View CKAN details →

Side-by-Side Comparison

Amundsen

CKAN

Amundsen

CKAN

Best For

✓Data discovery and metadata catalog with a search-first UI and social features like table popularity
✓Organizations wanting a Lyft-style data catalog with usage signals and human-curated descriptions
✓Teams needing a graph-backed metadata store with Neptune or Atlas as the metadata backend

✓Open data portals where government and research organizations publish public datasets
✓Hosting a self-managed data catalog with rich metadata, API access, and dataset versioning
✓Public data publishing workflows with citation, licensing, and provenance metadata

Best For

✓Data discovery and metadata catalog with a search-first UI and social features like table popularity
✓Organizations wanting a Lyft-style data catalog with usage signals and human-curated descriptions
✓Teams needing a graph-backed metadata store with Neptune or Atlas as the metadata backend

✓Open data portals where government and research organizations publish public datasets
✓Hosting a self-managed data catalog with rich metadata, API access, and dataset versioning
✓Public data publishing workflows with citation, licensing, and provenance metadata

Weaknesses

•Complex multi-component deployment: metadata service, search service, and frontend all required
•Requires significant engineering effort to maintain connectors and keep metadata fresh
•Smaller default connector set than DataHub; custom connectors require more implementation work

•Complex to deploy and maintain — Python 2-era codebase with slow modernization
•Not designed for internal enterprise data discovery; better suited to public open data portals
•Limited lineage and governance features compared to modern enterprise data catalogs

Weaknesses

•Complex multi-component deployment: metadata service, search service, and frontend all required
•Requires significant engineering effort to maintain connectors and keep metadata fresh
•Smaller default connector set than DataHub; custom connectors require more implementation work

•Complex to deploy and maintain — Python 2-era codebase with slow modernization
•Not designed for internal enterprise data discovery; better suited to public open data portals
•Limited lineage and governance features compared to modern enterprise data catalogs

License

Apache-2.0

AGPL-3.0

License

Apache-2.0

AGPL-3.0

Install

pip install amundsen-common

pip install ckanapi

Install

pip install amundsen-common

pip install ckanapi

Rating

★ 4.5

★ 4.1

Rating

★ 4.5

★ 4.1

Key Features

Amundsen

1Data discovery search engine built on metadata from multiple sources
2Automated table popularity ranking based on query frequency
3Data lineage graph connecting datasets across tools and systems
4Metadata ingestion connectors for BigQuery, Redshift, Snowflake, and more
5Python-based databuilder framework for custom metadata extraction

CKAN

1Open-source data portal platform used by governments and research institutions
2Dataset catalog with rich metadata, versioning, and resource attachments
3REST API for programmatic dataset discovery and download
4DataStore plugin provides a queryable database backend for tabular resources
5Extension system for custom themes, authentication, and integrations

How Python Data Engineers Use These Tools