When should I use Amundsen instead of DataHub?

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 DataHub instead of Amundsen?

Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform. Large organizations needing hundreds of pre-built ingestion connectors across all major data sources. Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

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 DataHub?

Complex to self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum. DataHub Cloud is the managed path; self-hosting requires significant DevOps investment. Feature breadth means initial configuration and onboarding can be overwhelming

Amundsen vs DataHub: Key Differences for Python Data Engineering

Data Governance & Metadata

Amundsen

Data Discovery & Metadata Engine

★ 4.5

Apache-2.0

pip install amundsen-common

DataHub

Modern Metadata Platform

★ 4.6

Apache-2.0

pip install acryl-datahub

Side-by-Side Comparison

Amundsen

DataHub

Amundsen

DataHub

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

✓Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform
✓Large organizations needing hundreds of pre-built ingestion connectors across all major data sources
✓Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

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

✓Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform
✓Large organizations needing hundreds of pre-built ingestion connectors across all major data sources
✓Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

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 self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum
•DataHub Cloud is the managed path; self-hosting requires significant DevOps investment
•Feature breadth means initial configuration and onboarding can be overwhelming

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 self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum
•DataHub Cloud is the managed path; self-hosting requires significant DevOps investment
•Feature breadth means initial configuration and onboarding can be overwhelming

License

Apache-2.0

License

Apache-2.0

Install

pip install amundsen-common

pip install acryl-datahub

Install

pip install amundsen-common

pip install acryl-datahub

Rating

★ 4.5

★ 4.6

Rating

★ 4.5

★ 4.6

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

DataHub

1Extensible metadata platform with a graph-based metadata model
2Automated ingestion connectors for 50+ sources via Python recipes
3Column-level lineage tracking across transformations and queries
4Data contracts for defining and enforcing schema and freshness expectations
5Browser-based search, governance workflows, and ownership management

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.

DataHub

Python data engineers use DataHub's Python SDK and ingestion framework to crawl metadata from databases, dbt projects, and Airflow — writing YAML recipe files that the `datahub` CLI ingests on a schedule. Custom Python emitters push metadata about internal pipeline assets that built-in connectors don't cover.

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Data Governance & Metadata

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Data Governance & Metadata

Apache Atlas vs Collibra

Data Governance & Metadata

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Individual Tool Pages

View Amundsen details →View DataHub details →

Side-by-Side Comparison

Amundsen

DataHub

Amundsen

DataHub

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

✓Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform
✓Large organizations needing hundreds of pre-built ingestion connectors across all major data sources
✓Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

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

✓Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform
✓Large organizations needing hundreds of pre-built ingestion connectors across all major data sources
✓Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

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 self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum
•DataHub Cloud is the managed path; self-hosting requires significant DevOps investment
•Feature breadth means initial configuration and onboarding can be overwhelming

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 self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum
•DataHub Cloud is the managed path; self-hosting requires significant DevOps investment
•Feature breadth means initial configuration and onboarding can be overwhelming

License

Apache-2.0

License

Apache-2.0

Install

pip install amundsen-common

pip install acryl-datahub

Install

pip install amundsen-common

pip install acryl-datahub

Rating

★ 4.5

★ 4.6

Rating

★ 4.5

★ 4.6

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

DataHub

1Extensible metadata platform with a graph-based metadata model
2Automated ingestion connectors for 50+ sources via Python recipes
3Column-level lineage tracking across transformations and queries
4Data contracts for defining and enforcing schema and freshness expectations
5Browser-based search, governance workflows, and ownership management

How Python Data Engineers Use These Tools