When should I use Apache Gravitino instead of CKAN?

Unified multi-engine metadata catalog integrating Hive, Iceberg, and Spark metadata in one layer. Teams managing data assets across multiple compute engines who want a single metadata API. Open-source alternative to proprietary metadata management for cloud-native lakehouse architectures

When should I use CKAN instead of Apache Gravitino?

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 Apache Gravitino?

Very new project (Apache incubating); production readiness is still being established. Limited documentation and community resources compared to DataHub or Amundsen. Connector and engine support is still actively growing; gaps exist for less common platforms

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

Apache Gravitino vs CKAN: Key Differences for Python Data Engineering

Data Governance & Metadata

Apache Gravitino

Unified Metadata Management

★ 4.0

Apache-2.0

pip install apache-gravitino

CKAN

Open Data Management System

★ 4.1

AGPL-3.0

pip install ckanapi

Side-by-Side Comparison

Apache Gravitino

CKAN

Apache Gravitino

CKAN

Best For

✓Unified multi-engine metadata catalog integrating Hive, Iceberg, and Spark metadata in one layer
✓Teams managing data assets across multiple compute engines who want a single metadata API
✓Open-source alternative to proprietary metadata management for cloud-native lakehouse architectures

✓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

✓Unified multi-engine metadata catalog integrating Hive, Iceberg, and Spark metadata in one layer
✓Teams managing data assets across multiple compute engines who want a single metadata API
✓Open-source alternative to proprietary metadata management for cloud-native lakehouse architectures

✓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

•Very new project (Apache incubating); production readiness is still being established
•Limited documentation and community resources compared to DataHub or Amundsen
•Connector and engine support is still actively growing; gaps exist for less common platforms

•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

•Very new project (Apache incubating); production readiness is still being established
•Limited documentation and community resources compared to DataHub or Amundsen
•Connector and engine support is still actively growing; gaps exist for less common platforms

•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 apache-gravitino

pip install ckanapi

Install

pip install apache-gravitino

pip install ckanapi

Rating

★ 4.0

★ 4.1

Rating

★ 4.0

★ 4.1

Key Features

Apache Gravitino

1Unified metadata management layer for multi-engine data lake environments
2Single API to manage schemas across Hive, Iceberg, and cloud warehouses
3Column-level access control policies enforced across all connected engines
4REST API for programmatic schema registration and discovery
5Open-source Apache incubator project with active development

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

Apache Gravitino

Python data engineers use Gravitino's REST API to register and discover table schemas centrally when working across multiple compute engines — registering an Iceberg table in Gravitino makes it discoverable to Spark, Trino, and Flink without duplicating schema definitions. Python scripts automate schema registration after new pipeline outputs are created.

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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Apache Atlas vs DataHub

Data Governance & Metadata

Apache Atlas vs Collibra

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Individual Tool Pages

View Apache Gravitino details →View CKAN details →

Side-by-Side Comparison

Apache Gravitino

CKAN

Apache Gravitino

CKAN

Best For

✓Unified multi-engine metadata catalog integrating Hive, Iceberg, and Spark metadata in one layer
✓Teams managing data assets across multiple compute engines who want a single metadata API
✓Open-source alternative to proprietary metadata management for cloud-native lakehouse architectures

✓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

✓Unified multi-engine metadata catalog integrating Hive, Iceberg, and Spark metadata in one layer
✓Teams managing data assets across multiple compute engines who want a single metadata API
✓Open-source alternative to proprietary metadata management for cloud-native lakehouse architectures

✓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

•Very new project (Apache incubating); production readiness is still being established
•Limited documentation and community resources compared to DataHub or Amundsen
•Connector and engine support is still actively growing; gaps exist for less common platforms

•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

•Very new project (Apache incubating); production readiness is still being established
•Limited documentation and community resources compared to DataHub or Amundsen
•Connector and engine support is still actively growing; gaps exist for less common platforms

•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 apache-gravitino

pip install ckanapi

Install

pip install apache-gravitino

pip install ckanapi

Rating

★ 4.0

★ 4.1

Rating

★ 4.0

★ 4.1

Key Features

Apache Gravitino

1Unified metadata management layer for multi-engine data lake environments
2Single API to manage schemas across Hive, Iceberg, and cloud warehouses
3Column-level access control policies enforced across all connected engines
4REST API for programmatic schema registration and discovery
5Open-source Apache incubator project with active development

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