DataHub and OpenMetadata are the two most credible general-purpose open-source data catalog shortlists for many modern data teams. Both provide discovery, lineage, ownership, glossary and APIs. Both have managed commercial options. The decision turns on product shape, integration fit and which platform your team is prepared to operate.
Short answer: Start with DataHub when an event-oriented metadata graph and engineering automation lead the requirement. Start with OpenMetadata when an integrated catalog, quality and governance experience is the stronger centre of gravity. Prove the distinction with your connectors and workflows.
Comparison at a glance
| Dimension | DataHub | OpenMetadata |
|---|---|---|
| Core licence | Apache License 2.0 | Apache License 2.0 |
| Product emphasis | Extensible metadata graph and activation | Integrated catalog, lineage, quality and governance |
| Ingestion | Broad push/pull framework and APIs | Broad connector framework, APIs and schemas |
| Lineage | Graph-based lineage from connectors, parsing and APIs | Table/column lineage from connectors, parsing and APIs |
| Quality | Assertions and quality integrations; confirm distribution-specific capabilities | Profiling and test concepts integrated with catalog assets |
| Managed option | DataHub Cloud | Collate |
| Self-hosting | Multi-service production platform | Multi-service production platform |
The table summarises documented capabilities, not measured performance. The Snowflake and dbt scenario below makes the integration and distribution boundaries more concrete.
Open-source and commercial boundaries
The DataHub v1.7.0.1 licence and OpenMetadata 2.0.5-release licence state Apache License 2.0. That permits broad use and modification of the core projects.
Do not stop the licence review there. Managed services, enterprise features, AI extensions and deployment repositories may have separate terms. Inventory the exact artifacts and services in your architecture.
Architecture and metadata model
DataHub's architecture guide describes a stream-oriented metadata platform with REST, GraphQL and Kafka APIs. Its lineage and relationship model is well suited to teams that want metadata to drive automation.
OpenMetadata's documentation presents discovery, lineage, quality and governance together. Its appeal is an integrated product surface around that model.
These are tendencies, not absolute boundaries. Prototype the integrations you intend to build and the objects your users need to understand.
Ingestion and connector depth
Both projects document broad connector frameworks. Connector totals use different scopes and say little about whether a specific source supplies useful lineage or quality context. Compare the actual objects and workflows instead.
For each priority connector, record objects, descriptions, ownership, query usage, profiles, quality tests, lineage granularity, incremental behaviour and version support. Run the connector under realistic permissions and measure recovery from failure.
The best catalog is the one that produces useful metadata from your estate without creating an unsustainable custom-integration backlog.
Practical comparison: Snowflake plus dbt Core
Consider a Snowflake source table transformed by dbt into an analytics model, with descriptions and a failing dbt test that consumers must see. This follows the warehouse-and-dbt evaluation path in our evaluation guide. It is a documentation comparison; we did not connect a warehouse or execute either product.
Evidence scope, checked 16 October 2026: we reviewed the release notes for DataHub Core v1.7.0.1 and OpenMetadata 2.0.5-release, alongside DataHub's documentation labelled 1.7.0 and OpenMetadata's v2.0.x documentation. These are version-family guides, not immutable specifications for those patch releases. DataHub Cloud and Collate instructions are identified separately; no managed tenant or plan was evaluated. OpenMetadata lists dbt Core 1.2–1.9; compatibility with newer artifacts is unconfirmed by that list. We did not establish a complete dbt version compatibility range for DataHub.
| Requirement | DataHub Core route | OpenMetadata OSS route |
|---|---|---|
| Warehouse metadata | Snowflake connector ingests databases, schemas, tables/views, fields and descriptions; usage and optional profiling are separate capabilities. Connector details. | Snowflake metadata workflow ingests schemas, tables, columns and views; usage, profiler and lineage workflows have additional configuration. Connector details. |
| dbt metadata | The dbt source reads model/source metadata, SQL, descriptions, owners and tags; manifest_path and target_platform are required. catalog.json supplies column metadata. dbt source. |
dbt artifacts enrich warehouse tables with model definitions, descriptions, owners and tags. manifest.json is required; catalog.json is recommended for database column types. dbt guide. |
| Lineage | dbt dependencies supply table edges; parsed SQL can supply column edges with include_column_lineage and infer_dbt_schemas enabled. Snowflake lineage has separate grants and edition requirements: the connector documents Enterprise or above for include_table_lineage and include_column_lineage. |
The dbt guide requires compiled_code in the manifest for a node's lineage. Snowflake lineage defaults to ACCESS_HISTORY, with query-log parsing as fallback when access is unavailable. This does not guarantee identical coverage. |
| Quality context | dbt definitions become assertions; run_results_paths supplies execution results. Snowflake profiling is optional. Importing results does not run dbt tests. |
Supply run_results.json to import dbt tests and their outcomes. Separately, native table/column tests can run in configured quality workflows. Artifact import itself does not run dbt tests. |
| Setup prerequisites | An ingestion runner needs warehouse credentials, source grants and access to the artifacts, plus a DataHub destination. Configure the Snowflake target identity consistently between the warehouse and dbt recipes. | Create the Snowflake service and ingest its tables before dbt enrichment; the ingestion runtime needs artifact access. The Snowflake guide lists warehouse/database/schema USAGE and table SELECT, with additional system-view access for lineage and usage. The external runner guide documents the Python ingestion package. |
| Concrete limits | Without catalog.json, column information can be incomplete; without run results, test execution outcomes are absent. SQL parsing and mismatched target names can leave gaps. |
Without compiled code, a dbt node's lineage is absent; without run results, tests are not imported according to the guide's tests section. Artifact freshness and supported dbt versions constrain what consumers see. |
The lineage, quality and prerequisite rows use the same linked connector guides. For both routes, Snowflake's Access History requires Enterprise Edition or higher. Warehouse permissions and available history still matter when the catalog is managed. Do not infer that every edge is available on Snowflake Standard or that parser fallback reproduces native history.
OSS versus managed: DataHub's Core/Cloud comparison lists column lineage and quality status in both, but managed freshness/volume/schema/column monitoring, custom SQL checks and assertion notifications in Cloud. Core's dbt-result ingestion is therefore not equivalent to Cloud's monitoring service. OpenMetadata documents native quality workflows, while its dbt guide also contains Collate SaaS/BYOC/Hybrid instructions and describes direct dbt Cloud API ingestion in Collate terms. We have not established those managed setup paths as OSS features; this comparison uses the dbt Core artifact route. The guide also describes manifest test definitions but later says tests require run results: supply both files and verify the resulting test cases rather than assuming manifest-only coverage.
For this scenario, either product is a documented candidate if dbt already executes the tests and the catalog needs to display outcomes. OpenMetadata deserves a closer look if running native catalog quality workflows is required; DataHub Core remains a candidate when the execution system stays external. This is a fit judgment from the documented boundaries, not a benchmark winner.
Before selecting one, use a disposable deployment to check a source-to-model column edge and a failed test, then remove compiled code or a history grant and inspect the failure reporting. Retain the dbt test run results before later commands replace artifacts, and check that warehouse and dbt records resolve to the same physical table. These are proposed acceptance checks, not tests we performed.
Discovery and business context
Both products support search, ownership, domains, tags and glossary concepts. OpenMetadata’s interface presents a broad set of catalog, quality and collaboration capabilities together. DataHub’s graph and platform orientation may appeal to teams that distribute catalog context through APIs and development workflows.
Test with analysts and stewards as well as engineers. Search relevance, explanation, status and business language determine whether the catalog changes behaviour.
Lineage
Both products document table- and column-oriented lineage through connectors and supplied metadata. See the DataHub dbt connector and OpenMetadata lineage ingestion guide for the mechanisms behind the Snowflake/dbt comparison above.
Use the same lineage test: a source table through transformation and orchestration into a BI asset. Add a rename, dynamic transformation and failed job. Compare missing-edge diagnosis and the provenance of relationships.
Data quality
OpenMetadata gives profiling, tests and quality signals a prominent role in the core product narrative. This can be useful when teams want one interface for discovery and quality context.
DataHub supports assertions, incidents and quality-related metadata, with some monitoring capabilities differentiated by distribution. Confirm which execution and monitoring features exist in the exact open-source release or cloud proposal.
Do not let integrated quality become an unchecked compute workload. Clarify where tests run, who owns them and how cost scales.
Governance and automation
DataHub’s APIs, policies, actions and metadata events support engineering automation. OpenMetadata’s schemas, policies, domains, glossary, contracts and product concepts support an integrated governance model.
Prototype one real lifecycle: a model is deployed, metadata arrives, ownership is assigned, a quality failure appears and downstream users are warned. Compare how much custom code and manual work each route needs.
Operating burden
Neither self-hosted product is a single static binary. Review DataHub's architecture and OpenMetadata's deployment requirements for the services, storage and ingestion infrastructure your chosen deployment needs.
Compare upgrades, backups, high availability, monitoring, identity, secrets, vulnerability response and connector workers. If those are not core team capabilities, price the managed services alongside commercial alternatives.
Choose DataHub when
- Metadata activation, APIs and event-driven workflows are central.
- A platform engineering team owns the service.
- The target connectors and lineage paths are stronger in your proof of concept.
- You want DataHub’s graph and ecosystem as an internal metadata foundation.
Choose OpenMetadata when
- An integrated catalog-plus-quality experience is a priority.
- Its metadata model and connector results better match your estate.
- Stewards and analysts prefer its out-of-box product workflow.
- The team accepts the self-hosted architecture or prefers Collate’s managed path.
Final recommendation
Install both in disposable environments and run identical tests before designing a large platform programme. Local setup speed is not the decision; compare connector output, user comprehension, automation effort and a rehearsed upgrade. Then calculate three-year engineering and infrastructure cost.
Read our full DataHub review and OpenMetadata review for the evidence and caveats behind this comparison.