Comparisons anchored on DataHub, the Apache-2.0 metadata platform for discovery, lineage and governance, which its maker also sells as the managed DataHub Cloud.
Whether to run open-source DataHub Core on Kubernetes or buy DataHub Cloud: the Cloud-only features (AI documentation, observability monitors, access request workflows, compliance forms, SLA), the Kafka, database, search and graph services you run yourself, and how SSO setup differs between the two.
DataHub against Apache Atlas for organizations with Hadoop, Hive and Ranger estates: metadata models, lineage, integrations beyond Hadoop, the services each needs (Kafka, Elasticsearch and a database versus JanusGraph on HBase and Solr) and release cadence, with what a move from Atlas to DataHub involves.
For teams still on Amundsen, archived in September 2026: how DataHub compares on discovery, lineage, glossary and governance, the Neo4j and Elasticsearch stack being retired against DataHub's services, and what moving metadata off Amundsen involves.
Which DataHub install fits which stage: the datahub docker quickstart for evaluation (8 GB of RAM, Python 3.10) against the Helm prerequisites and main charts for production, with the system-update job that runs migrations on upgrade and the external Kafka, database and search services a production install should use.
Tell us what you're working on, whether it's a deployment, an audit, a security test or a cyber range. You'll speak with an engineer who can help you scope it.