When should I use Amundsen instead of Apache Atlas?

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 Atlas instead of Amundsen?

Data governance, classification, and lineage tracking within the Hadoop and HBase ecosystem. Enterprises needing centralized metadata management with policy enforcement via Ranger integration. Teams building a data catalog on Hadoop who want native Atlas-Ranger security integration

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 Atlas?

Heavy Hadoop dependency — difficult and impractical to deploy outside the Hadoop ecosystem. UI and REST API are dated compared to modern data catalogs like DataHub or Atlan. Very limited connectors outside the Hadoop ecosystem for non-HDFS data sources

Amundsen vs Apache Atlas: Key Differences for Python Data Engineering

Data Governance & Metadata

Amundsen

Data Discovery & Metadata Engine

★ 4.5

Apache-2.0

pip install amundsen-common

Apache Atlas

Enterprise Data Governance

★ 4.2

Apache-2.0

pip install apache-atlas

Side-by-Side Comparison

Amundsen

Apache Atlas

Amundsen

Apache Atlas

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

✓Data governance, classification, and lineage tracking within the Hadoop and HBase ecosystem
✓Enterprises needing centralized metadata management with policy enforcement via Ranger integration
✓Teams building a data catalog on Hadoop who want native Atlas-Ranger security integration

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

✓Data governance, classification, and lineage tracking within the Hadoop and HBase ecosystem
✓Enterprises needing centralized metadata management with policy enforcement via Ranger integration
✓Teams building a data catalog on Hadoop who want native Atlas-Ranger security integration

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

•Heavy Hadoop dependency — difficult and impractical to deploy outside the Hadoop ecosystem
•UI and REST API are dated compared to modern data catalogs like DataHub or Atlan
•Very limited connectors outside the Hadoop ecosystem for non-HDFS data sources

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

•Heavy Hadoop dependency — difficult and impractical to deploy outside the Hadoop ecosystem
•UI and REST API are dated compared to modern data catalogs like DataHub or Atlan
•Very limited connectors outside the Hadoop ecosystem for non-HDFS data sources

License

Apache-2.0

License

Apache-2.0

Install

pip install amundsen-common

pip install apache-atlas

Install

pip install amundsen-common

pip install apache-atlas

Rating

★ 4.5

★ 4.2

Rating

★ 4.5

★ 4.2

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 Atlas

1Metadata management with a type system for custom entity definitions
2Automated data lineage tracking across Hive, Spark, Sqoop, and Kafka
3Tag-based data classification for PII and sensitive data governance
4REST API for programmatic metadata querying and entity management
5Integration with Apache Ranger for policy-based access control

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 Atlas

Python data engineers integrate with Apache Atlas via its REST API to register custom data assets, query lineage graphs, and enforce data classification policies. Post-ingestion scripts tag newly created tables with PII labels, and lineage queries trace how specific columns flow from source systems through transformations to the final warehouse tables.

More Data Governance & Metadata Comparisons

Data Governance & Metadata

Apache Atlas vs CKAN

Data Governance & Metadata

Apache Atlas vs Marquez

Data Governance & Metadata

Apache Atlas vs DataHub

Data Governance & Metadata

Apache Atlas vs Collibra

Data Governance & Metadata

Apache Atlas vs Apache Gravitino

Data Governance & Metadata

Apache Atlas vs PACE

Individual Tool Pages

View Amundsen details →View Apache Atlas details →

Side-by-Side Comparison

Amundsen

Apache Atlas

Amundsen

Apache Atlas

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

✓Data governance, classification, and lineage tracking within the Hadoop and HBase ecosystem
✓Enterprises needing centralized metadata management with policy enforcement via Ranger integration
✓Teams building a data catalog on Hadoop who want native Atlas-Ranger security integration

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

✓Data governance, classification, and lineage tracking within the Hadoop and HBase ecosystem
✓Enterprises needing centralized metadata management with policy enforcement via Ranger integration
✓Teams building a data catalog on Hadoop who want native Atlas-Ranger security integration

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

•Heavy Hadoop dependency — difficult and impractical to deploy outside the Hadoop ecosystem
•UI and REST API are dated compared to modern data catalogs like DataHub or Atlan
•Very limited connectors outside the Hadoop ecosystem for non-HDFS data sources

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

•Heavy Hadoop dependency — difficult and impractical to deploy outside the Hadoop ecosystem
•UI and REST API are dated compared to modern data catalogs like DataHub or Atlan
•Very limited connectors outside the Hadoop ecosystem for non-HDFS data sources

License

Apache-2.0

License

Apache-2.0

Install

pip install amundsen-common

pip install apache-atlas

Install

pip install amundsen-common

pip install apache-atlas

Rating

★ 4.5

★ 4.2

Rating

★ 4.5

★ 4.2

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 Atlas

1Metadata management with a type system for custom entity definitions
2Automated data lineage tracking across Hive, Spark, Sqoop, and Kafka
3Tag-based data classification for PII and sensitive data governance
4REST API for programmatic metadata querying and entity management
5Integration with Apache Ranger for policy-based access control

How Python Data Engineers Use These Tools