Apache Atlas vs DataHub.
Apache Atlas and DataHub both anchor in catalog & discovery — 5 dimensions differ, 4 hold. Below: posture, coverage diff, and capability matrix.
The ASF's Hadoop-native metadata framework — typed entities, classification propagation via lineage, and Ranger-enforced policies.
Apache-2.0 metadata platform with a serious managed counterpart — strongest event-driven architecture and column-level SQL lineage in OSS.
Organisations running Hadoop-era estates — Hive, HBase, Kafka, Impala — especially on Cloudera Data Platform, where Atlas ships as the embedded governance layer.
Engineering-led data platforms that want an open, extensible metadata layer they can shape to their stack — with a credible managed escape hatch (DataHub Cloud) when self-hosting Kafka, Elasticsearch, and the graph store stops being fun.
What each is betting on.
Born inside Hortonworks' Data Governance Initiative (announced December 2014, with enterprise partners), entered the Apache Incubator in May 2015 and graduated to a top-level ASF project in June 2017. Today it survives primarily as the governance layer embedded in Cloudera Data Platform, and as the foundation of Microsoft Purview's Data Map, which is based on Atlas and supports Atlas APIs. Release cadence has slowed markedly: 2.3.0 (Dec 2022), 2.4.0 (Jan 2025), 2.5.0 (Apr 2026).
DataHub originated at LinkedIn (open-sourced February 2020); Acryl Data was founded 2021 by ex-LinkedIn engineers to build the managed product. Series A $21M (2022, 8VC); Series B $35M (2024, Bessemer). 2024–2025 rebrand consolidated the OSS and managed offerings under a single 'DataHub' brand, with 'DataHub Cloud' replacing the older 'Acryl Cloud' name.
Each tool's current strategic narrative, verbatim from its profile.
How each tool describes the other.
Against datahub, Atlas is the prior generation of the same idea — typed entity model, Kafka-borne metadata events — with the stronger security-enforcement story and the far weaker modern-stack coverage. Against openmetadata, the connection is literal: OpenMetadata's founding team includes Atlas veterans, and the project is in many ways Atlas rebuilt for the cloud-warehouse era, with 120+ connectors and a simpler stack. Against amundsen, the contrast is purpose — Amundsen is discovery-first and lightweight, Atlas is governance-first and heavy. Teams leaving Hadoop generally migrate to DataHub or OpenMetadata rather than extending Atlas.
DataHub's page doesn't directly mention Apache Atlas. See the DataHub detail page.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| Apache Atlas | DataHub | |
|---|---|---|
| Vendor | Apache Software Foundation | Acryl Data |
| Deployment | Self-hosted only | SaaS · Self-hosted |
| Pricing | OSS · paid tiers | OSS · free |
| dbt integration | None | Native |
| OpenLineage | None | Consumer |
| Founded | 2015 | 2021 |
| HQ | — | Palo Alto, CA |
Full Apache Atlas pricing → Full DataHub pricing →
Both share Primary cluster: Catalog & discovery · License: Open source · Free tier: Yes · OSS self-host: Yes · Status: ● active
Each tool's center of gravity.
| Cluster | Apache Atlas | DataHub |
|---|---|---|
| Quality & testing | 0/3 | 2/3 |
| Lineage & metadata | 2/3 | 3/3 |
| Catalog & discovery | 3/3primary | 3/3primary |
Scored 0–3 per cluster on the same rubric across all tools. A 0 means the cluster isn't the tool's focus, not that the feature is absent. See the methodology.
Where they cover different ground.
The declared feature set.
3 of 6 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | Apache Atlas | DataHub |
|---|---|---|
| Data Contracts Quality & testing | ||
| Schema Change Detection Quality & testing | ||
| OpenLineage-Native Lineage & metadata | ||
| Business Glossary Catalog & discovery | ||
| Column-Level Lineage Lineage & metadata | ||
| Table-Level Lineage Lineage & metadata |
Where they disagree.
Catalog & discovery
5 of 9 differ| Apache Atlas | DataHub | |
|---|---|---|
| NL search | ||
| Data contracts | ||
| Governance flows | ||
| Access requests | ||
| PII auto-classify |
Lineage & metadata
3 of 7 differ| Apache Atlas | DataHub | |
|---|---|---|
| Reverse impact | ||
| Historical | ||
| BI lineage |
When to pick each.
Organisations running Hadoop-era estates — Hive, HBase, Kafka, Impala — especially on Cloudera Data Platform, where Atlas ships as the embedded governance layer. The pairing with Apache Ranger is the reason to choose it: classify a column PII in Atlas and Ranger enforces masking and access policies on the actual data, with classifications propagating automatically through lineage as data moves. That metadata-driven-security loop is still the strongest in open source. Also relevant for teams building against Microsoft Purview, whose Data Map is based on Atlas and supports Atlas APIs — the type system and REST surface transfer directly. Vendor-neutral ASF governance and a genuinely complete Apache-2.0 codebase (no open-core split) round out the case.
Engineering-led data platforms that want an open, extensible metadata layer they can shape to their stack — with a credible managed escape hatch (DataHub Cloud) when self-hosting Kafka, Elasticsearch, and the graph store stops being fun. Particularly strong for organisations that already think in events: DataHub's Kafka-based Metadata Change Log makes it a natural fit for shops that want metadata to flow the same way data does. The SQL parser is genuinely best-in-class in the OSS catalog space, with SQLGlot-based column-level lineage benchmarked at 97–99% accuracy on standard corpora — materially better than competing parsers. A good fit also for teams wiring DataHub into AI agents via the native MCP server.
What each does best.
Apache Atlas stands out for
- Classification propagation via lineage plus Apache Ranger integration — tag-based access control and data masking enforced on the data itself, not just documented in the catalog
- Rigorous typed metadata model (types, entities, first-class relationships) that handles technical and business metadata in one graph
- Vendor-neutral ASF governance and a complete Apache-2.0 codebase — no managed tier holding features back
- Column-level lineage for Hive and Impala, captured in real time by hooks since the 0.8 line — mature and battle-tested within its ecosystem
DataHub stands out for
- Best-in-class column-level SQL lineage parser (SQLGlot-based, benchmarked at 97–99% accuracy on standard corpora)
- Event-driven Kafka MCL architecture — metadata changes are a stream, not a snapshot, which composes well with downstream consumers
- Native OpenLineage consumer endpoint plus dedicated Spark and Airflow plugins
- Open-core model with a credible managed product (DataHub Cloud) means buyers can start free and graduate without a re-platforming
Tools both also compete with.
All Apache Atlas alternatives, scored →All DataHub alternatives, scored →
A note on this comparison.
Every capability value above traces to Apache Atlas or DataHub's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
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