When should I use Amundsen instead of Apache Gravitino?

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 Apache Gravitino instead of Amundsen?

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

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 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

Amundsen vs Apache Gravitino: Key Differences for Python Data Engineering

Data Governance & Metadata

Amundsen

Data Discovery & Metadata Engine

★ 4.5

Apache-2.0

pip install amundsen-common

Apache Gravitino

Unified Metadata Management

★ 4.0

Apache-2.0

pip install apache-gravitino

Side-by-Side Comparison

Amundsen

Apache Gravitino

Amundsen

Apache Gravitino

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

✓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

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

✓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

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

•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

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

•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

License

Apache-2.0

License

Apache-2.0

Install

pip install amundsen-common

pip install apache-gravitino

Install

pip install amundsen-common

pip install apache-gravitino

Rating

★ 4.5

★ 4.0

Rating

★ 4.5

★ 4.0

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

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

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.

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.

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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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Data Governance & Metadata

Apache Atlas vs DataHub

Data Governance & Metadata

Apache Atlas vs Collibra

Data Governance & Metadata

Apache Atlas vs Apache Gravitino

Individual Tool Pages

View Amundsen details →View Apache Gravitino details →

Side-by-Side Comparison

Amundsen

Apache Gravitino

Amundsen

Apache Gravitino

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

✓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

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

✓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

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

•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

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

•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

License

Apache-2.0

License

Apache-2.0

Install

pip install amundsen-common

pip install apache-gravitino

Install

pip install amundsen-common

pip install apache-gravitino

Rating

★ 4.5

★ 4.0

Rating

★ 4.5

★ 4.0

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

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

How Python Data Engineers Use These Tools