When should I use Amundsen instead of Collibra?

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 Collibra instead of Amundsen?

Enterprise data governance, stewardship, and policy management at large organizational scale. Regulated industries (finance, healthcare) needing certified governance workflows and audit trails. Organizations with complex data ownership hierarchies, lineage requirements, and compliance mandates

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 Collibra?

Very expensive commercial product with significant implementation and licensing costs. Heavy implementation effort — typically requires professional services and months of onboarding. Not suitable for startups or small data teams; geared entirely toward large enterprise scale

Amundsen vs Collibra: Key Differences for Python Data Engineering

Data Governance & Metadata

Amundsen

Data Discovery & Metadata Engine

★ 4.5

Apache-2.0

pip install amundsen-common

Collibra

Enterprise Data Intelligence

★ 4.4

Commercial

N/A — SaaS platform

Side-by-Side Comparison

Amundsen

Collibra

Amundsen

Collibra

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 governance, stewardship, and policy management at large organizational scale
✓Regulated industries (finance, healthcare) needing certified governance workflows and audit trails
✓Organizations with complex data ownership hierarchies, lineage requirements, and compliance mandates

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 governance, stewardship, and policy management at large organizational scale
✓Regulated industries (finance, healthcare) needing certified governance workflows and audit trails
✓Organizations with complex data ownership hierarchies, lineage requirements, and compliance mandates

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 expensive commercial product with significant implementation and licensing costs
•Heavy implementation effort — typically requires professional services and months of onboarding
•Not suitable for startups or small data teams; geared entirely toward large enterprise scale

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 expensive commercial product with significant implementation and licensing costs
•Heavy implementation effort — typically requires professional services and months of onboarding
•Not suitable for startups or small data teams; geared entirely toward large enterprise scale

License

Apache-2.0

Commercial

License

Apache-2.0

Commercial

Install

pip install amundsen-common

N/A — SaaS platform

Install

pip install amundsen-common

N/A — SaaS platform

Rating

★ 4.5

★ 4.4

Rating

★ 4.5

★ 4.4

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

Collibra

1Enterprise data governance platform with policy and workflow management
2Business glossary links technical assets to business terms and definitions
3Data lineage from source to consumption with impact analysis
4REST and GraphQL APIs for integration with data tools and pipelines
5Certification and stewardship workflows for data quality accountability

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.

Collibra

Python data engineers interact with Collibra via its REST API to register new data assets, link them to business terms, and trigger stewardship workflows after pipeline runs. Automated scripts tag newly created warehouse tables with their source system, owner, and data classification label — keeping the Collibra catalog in sync with the evolving data landscape.

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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 Collibra details →

Side-by-Side Comparison

Amundsen

Collibra

Amundsen

Collibra

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 governance, stewardship, and policy management at large organizational scale
✓Regulated industries (finance, healthcare) needing certified governance workflows and audit trails
✓Organizations with complex data ownership hierarchies, lineage requirements, and compliance mandates

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 governance, stewardship, and policy management at large organizational scale
✓Regulated industries (finance, healthcare) needing certified governance workflows and audit trails
✓Organizations with complex data ownership hierarchies, lineage requirements, and compliance mandates

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 expensive commercial product with significant implementation and licensing costs
•Heavy implementation effort — typically requires professional services and months of onboarding
•Not suitable for startups or small data teams; geared entirely toward large enterprise scale

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 expensive commercial product with significant implementation and licensing costs
•Heavy implementation effort — typically requires professional services and months of onboarding
•Not suitable for startups or small data teams; geared entirely toward large enterprise scale

License

Apache-2.0

Commercial

License

Apache-2.0

Commercial

Install

pip install amundsen-common

N/A — SaaS platform

Install

pip install amundsen-common

N/A — SaaS platform

Rating

★ 4.5

★ 4.4

Rating

★ 4.5

★ 4.4

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

Collibra

1Enterprise data governance platform with policy and workflow management
2Business glossary links technical assets to business terms and definitions
3Data lineage from source to consumption with impact analysis
4REST and GraphQL APIs for integration with data tools and pipelines
5Certification and stewardship workflows for data quality accountability

How Python Data Engineers Use These Tools