When should I use Amundsen instead of Marquez?

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 Marquez instead of Amundsen?

Open-source data lineage collection using the OpenLineage standard across multiple tools. Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink. Lightweight lineage tracking without the full overhead of a complete data catalog

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 Marquez?

Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage. Requires instrumenting pipelines to emit OpenLineage events for each tool separately. Smaller community than DataHub or Amundsen for full data catalog and governance use cases

Amundsen vs Marquez: Key Differences for Python Data Engineering

Data Governance & Metadata

Amundsen

Data Discovery & Metadata Engine

★ 4.5

Apache-2.0

pip install amundsen-common

Marquez

Metadata Service for Data Lineage

★ 4.3

Apache-2.0

pip install marquez-client

Side-by-Side Comparison

Amundsen

Marquez

Amundsen

Marquez

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

✓Open-source data lineage collection using the OpenLineage standard across multiple tools
✓Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink
✓Lightweight lineage tracking without the full overhead of a complete data catalog

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

✓Open-source data lineage collection using the OpenLineage standard across multiple tools
✓Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink
✓Lightweight lineage tracking without the full overhead of a complete data catalog

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

•Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage
•Requires instrumenting pipelines to emit OpenLineage events for each tool separately
•Smaller community than DataHub or Amundsen for full data catalog and governance use cases

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

•Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage
•Requires instrumenting pipelines to emit OpenLineage events for each tool separately
•Smaller community than DataHub or Amundsen for full data catalog and governance use cases

License

Apache-2.0

License

Apache-2.0

Install

pip install amundsen-common

pip install marquez-client

Install

pip install amundsen-common

pip install marquez-client

Rating

★ 4.5

★ 4.3

Rating

★ 4.5

★ 4.3

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

Marquez

1OpenLineage-compliant metadata service for tracking dataset inputs and outputs
2Namespace and job model links pipeline runs to their data lineage
3REST API for emitting and querying lineage events
4Integrations with Airflow, Spark, dbt, and Great Expectations
5Visual lineage graph in the Marquez UI for impact analysis

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.

Marquez

Python data engineers integrate Marquez with Airflow using the `openlineage-airflow` package, which automatically emits lineage events for each task — capturing which datasets a task reads and writes without any code changes. Engineers query the Marquez API to build impact analysis tools that identify downstream jobs affected by an upstream schema change.

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Individual Tool Pages

View Amundsen details →View Marquez details →

Side-by-Side Comparison

Amundsen

Marquez

Amundsen

Marquez

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

✓Open-source data lineage collection using the OpenLineage standard across multiple tools
✓Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink
✓Lightweight lineage tracking without the full overhead of a complete data catalog

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

✓Open-source data lineage collection using the OpenLineage standard across multiple tools
✓Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink
✓Lightweight lineage tracking without the full overhead of a complete data catalog

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

•Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage
•Requires instrumenting pipelines to emit OpenLineage events for each tool separately
•Smaller community than DataHub or Amundsen for full data catalog and governance use cases

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

•Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage
•Requires instrumenting pipelines to emit OpenLineage events for each tool separately
•Smaller community than DataHub or Amundsen for full data catalog and governance use cases

License

Apache-2.0

License

Apache-2.0

Install

pip install amundsen-common

pip install marquez-client

Install

pip install amundsen-common

pip install marquez-client

Rating

★ 4.5

★ 4.3

Rating

★ 4.5

★ 4.3

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

Marquez

1OpenLineage-compliant metadata service for tracking dataset inputs and outputs
2Namespace and job model links pipeline runs to their data lineage
3REST API for emitting and querying lineage events
4Integrations with Airflow, Spark, dbt, and Great Expectations
5Visual lineage graph in the Marquez UI for impact analysis

How Python Data Engineers Use These Tools