Platform engineers
Enterprise Data Governance at Scale
Your pipelines pass. Your audit report breaks anyway. DataHub gives platform engineers policy-driven enterprise data governance that works across every source.
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Enforce fine-grained access policies across 50+ data platforms
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Trace column-level lineage from source to dashboard, automatically
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Automate compliance workflows before the audit question arrives
See DataHub govern your stack
We scope every demo to your environment, not a generic walkthrough.
Trusted by modern data teams
The real cost
What does governance failure actually cost?
Platform engineers absorb the invisible costs of governance gaps: the late-night incident, the audit scramble, the trust that erodes one broken pipeline at a time.
Policy drift
Access rules diverge from intent as teams grow. Permissions accumulate silently until a breach or audit surfaces what no one was tracking.
Lineage gaps
A dashboard breaks and no one knows which upstream table changed. Tracing the root cause costs hours of context switching across tools and teams.
Compliance scrambles
Audit requests arrive without warning. Pulling evidence manually from scattered systems takes days your team does not have to spare.
Trust erosion
Analysts stop trusting data they cannot verify. Decisions slow down. The platform team gets blamed for problems that started upstream.
How DataHub helps
A better way to govern your data at enterprise scale
DataHub gives platform engineers the enterprise data governance tools to enforce policy, trace lineage, and automate compliance across every source in your stack.
Fine-grained access control
Define RBAC policies at the resource level with actor filters and ownership rules. Access decisions are enforced at query time, not patched after the fact.
- Role-based and resource-level policy enforcement
- Actor filters scoped to teams, domains, and owners
- Audit-ready access logs across every platform
End-to-end column-level lineage
Trace data from ingestion to consumption at the column level. When something breaks, you know exactly where it started and what it touched downstream.
- Automated lineage across 50+ source connectors
- Column-level granularity from source to dashboard
- Impact analysis before schema changes ship
Data contracts and quality assertions
Define expectations at the schema level and surface violations before they reach consumers. Quality assertions run continuously and feed directly into lineage context.
- Schema-level contracts with automated validation
- Quality assertions surfaced in lineage graph
- Violations routed to owning teams automatically
Domain-driven governance workflows
Assign ownership, classify assets, and route compliance workflows by domain. Governance scales with your org structure, not against it.
- Domain ownership mapped to real org structure
- Classification tags applied at ingestion time
- Compliance workflows triggered by policy rules
How it works
Three steps to governed data
DataHub works with the infrastructure you already have. No rebuilding pipelines. No replacing tools.
Connect your sources
- Ingest metadata from Snowflake, dbt, Looker, and 50+ more
- Push-based and pull-based ingestion, scheduled or event-driven
- No changes required to existing pipelines or warehouse configs
Contextualize your assets
- Assign ownership, domains, and classification tags at ingestion
- Build column-level lineage graphs across every connected source
- Define data contracts and quality assertions per dataset
Activate governance at scale
- Enforce access policies across every platform from one control plane
- Trigger compliance workflows automatically when policy rules fire
- Pull audit evidence on demand, not the night before a review
Enterprise platform
Built for enterprise-grade scale and security
DataHub is an enterprise data governance platform designed to run in your environment, on your terms, with the security posture your organization requires.
Deployment options
- Self-hosted on Kubernetes or managed cloud deployment
- Apache 2.0 open source with enterprise support tiers
- Air-gapped deployment available for regulated environments
Security and compliance
- SSO integration via SAML, OIDC, and LDAP
- Immutable audit trail for every metadata change and access event
- Role separation between platform admins and domain stewards
API-first architecture
- GraphQL and OpenAPI for full programmatic control
- Webhook and event-stream integrations for real-time workflows
- Extend the metadata model without forking the platform
Social proof
Trusted by modern data teams
Platform engineers at enterprise organizations use DataHub to govern data at scale, without rebuilding their stack.
Gartner Peer Insights
Engineer, Services, 1B-10B revenue
Verified review
Gartner Peer Insights, 2024
"DataHub has transformed how we manage data governance across our enterprise. The lineage tracking and access control features have been invaluable for our compliance requirements."
Frequently asked questions
DataHub connects to your existing sources through pre-built ingestion
connectors for Snowflake, BigQuery, Databricks, dbt, Looker, Airflow, and 50+ others. Ingestion is
configured through YAML or the UI and runs on a schedule or event trigger. You do not need to change
your pipelines, warehouse configurations, or transformation logic. The platform reads metadata from
your existing systems and builds a unified graph on top of them.
DataHub supports self-hosted deployment on Kubernetes, managed cloud
deployment through DataHub Cloud, and air-gapped deployment for regulated environments where data
cannot leave your network perimeter. The open source version is Apache 2.0 licensed, which means you
can run it on your own infrastructure without vendor dependency. Enterprise support tiers are
available for teams that need SLA-backed assistance and dedicated engineering access.
DataHub does not certify compliance on your behalf, but it provides the
infrastructure that makes compliance auditable. Every metadata change and access event is recorded in
an immutable audit trail. Classification tags let you mark sensitive assets at ingestion time. Access
policies enforce who can see what, and those decisions are logged. When an auditor asks which systems
touched a specific dataset, you can answer that question from DataHub rather than reconstructing it
manually from logs across five tools.
It depends on the complexity of your environment and how many sources you
are connecting. Teams with a focused initial scope, such as a single warehouse and a dbt project, can
have metadata flowing within a day or two. Broader rollouts that include access policy configuration,
domain ownership mapping, and compliance workflow setup typically take several weeks. DataHub's
professional services team works with enterprise customers to scope implementation based on their
specific environment and governance maturity.
The clearest signals are operational: time spent on incident root cause
analysis, time spent preparing audit evidence, and the number of access-related support tickets your
platform team handles. Teams that implement DataHub typically track these before and after deployment.
Governance also reduces the risk of data incidents that carry regulatory or reputational cost, though
those are harder to quantify in advance. The most honest answer is that ROI depends on how much
governance failure is currently costing your team in engineering hours and organizational trust.
Ready to govern your data with confidence?
DataHub gives platform engineers the enterprise data governance model to enforce policy, trace lineage, and answer compliance questions without the scramble. You will speak with a DataHub engineer about your specific environment.
No generic walkthrough
Scoped to your environment
Apache 2.0 open source