When should I use Apache Atlas instead of DataHub?

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

When should I use DataHub instead of Apache Atlas?

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 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

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

Apache Atlas vs DataHub: Key Differences for Python Data Engineering

Data Governance & Metadata

Apache Atlas

Enterprise Data Governance

★ 4.2

Apache-2.0

pip install apache-atlas

DataHub

Modern Metadata Platform

★ 4.6

Apache-2.0

pip install acryl-datahub

Side-by-Side Comparison

Apache Atlas

DataHub

Apache Atlas

DataHub

Best For

✓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

✓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 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

✓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

•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

•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

•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

•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 apache-atlas

pip install acryl-datahub

Install

pip install apache-atlas

pip install acryl-datahub

Rating

★ 4.2

★ 4.6

Rating

★ 4.2

★ 4.6

Key Features

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

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

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.

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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Individual Tool Pages

View Apache Atlas details →View DataHub details →

Side-by-Side Comparison

Apache Atlas

DataHub

Apache Atlas

DataHub

Best For

✓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

✓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 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

✓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

•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

•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

•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

•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 apache-atlas

pip install acryl-datahub

Install

pip install apache-atlas

pip install acryl-datahub

Rating

★ 4.2

★ 4.6

Rating

★ 4.2

★ 4.6

Key Features

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

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