When should I use Apache Gravitino instead of Marquez?

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

When should I use Marquez instead of Apache Gravitino?

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 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

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

Apache Gravitino vs Marquez: Key Differences for Python Data Engineering

Data Governance & Metadata

Apache Gravitino

Unified Metadata Management

★ 4.0

Apache-2.0

pip install apache-gravitino

Marquez

Metadata Service for Data Lineage

★ 4.3

Apache-2.0

pip install marquez-client

Side-by-Side Comparison

Apache Gravitino

Marquez

Apache Gravitino

Marquez

Best For

✓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

✓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

✓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

✓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

•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

•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

•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

•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 apache-gravitino

pip install marquez-client

Install

pip install apache-gravitino

pip install marquez-client

Rating

★ 4.0

★ 4.3

Rating

★ 4.0

★ 4.3

Key Features

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

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

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.

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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Apache Atlas vs Collibra

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Individual Tool Pages

View Apache Gravitino details →View Marquez details →

Side-by-Side Comparison

Apache Gravitino

Marquez

Apache Gravitino

Marquez

Best For

✓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

✓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

✓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

✓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

•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

•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

•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

•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 apache-gravitino

pip install marquez-client

Install

pip install apache-gravitino

pip install marquez-client

Rating

★ 4.0

★ 4.3

Rating

★ 4.0

★ 4.3

Key Features

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

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