Data Governance Framework Built for Platform Engineers
Your pipelines pass. Your dashboards break anyway. DataHub gives platform engineers a governance framework that enforces access, quality, and lineage automatically.
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Fine-grained RBAC down to the column level, enforced at query time
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Automated data quality monitoring with contract-based SLA enforcement
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End-to-end lineage captured from your existing stack, no rewrites needed
See the governance framework in action
A DataHub engineer will walk through your specific environment.
What does governance failure actually cost your team?
Platform engineers carry the blame when data breaks downstream. The problem is rarely the pipeline. It is the absence of a reliable data governance framework around it.
Access control gaps at scale
When permissions are managed manually, sensitive columns reach the wrong consumers and audits become a fire drill.
Lineage blind spots in pipelines
Without column-level lineage, a single schema change can silently corrupt reports across multiple downstream teams.
Data quality with no accountability
Undocumented quality expectations mean failures surface in dashboards, not at the source where they can be fixed.
Governance that slows teams down
Policy enforcement bolted onto existing tools creates friction without coverage, leaving gaps and slowing delivery.
A better way to govern your data platform
DataHub gives platform engineers the controls they need without rebuilding what already works. Policy, lineage, quality, and context in one governance layer.
Policy-based access control
Define and enforce RBAC policies at the dataset, schema, and column level. Permissions propagate automatically as your catalog grows, without manual intervention per asset.
- Column-level masking enforced at query time
- Role inheritance across domains and asset groups
- Audit logs tied to every access policy change
End-to-end column-level lineage
DataHub captures lineage across pipelines, transformations, and BI tools automatically. Trace any field from its source to every downstream consumer without modifying your code.
- Column-level lineage across SQL, dbt, and Spark
- Impact analysis before schema changes ship
- Lineage visible in the catalog and via API
Automated data quality contracts
Define quality expectations as code-based contracts. DataHub monitors assertions continuously and routes failures to the right owner before they reach downstream consumers.
- Assertion monitoring on freshness, volume, and schema
- Contract violations routed to dataset owners
- Quality scores surfaced in search and lineage views
Federated ownership and context
Attach owners, glossary terms, and domain classifications to every asset. Domain teams govern their own data while platform teams retain visibility and policy control across the org.
- Business glossary linked to physical data assets
- Domain-scoped ownership with org-wide visibility
- Propagated tags and terms across related assets
How it works
Three steps to a working data governance framework. No changes to your existing infrastructure required.
Connect your data sources
- Pre-built connectors for warehouses, lakes, and BI tools
- Incremental metadata sync keeps the catalog current
- No changes to existing pipelines or infrastructure
Contextualize assets with owners
- Attach glossary terms and domain classifications
- Assign ownership at the dataset and column level
- Automated propagation keeps context current at scale
Activate policies across your platform
- Enforce access control and quality contracts together
- Lineage tracking active from day one, no rewrites
- Changes apply across connected systems automatically
Built for enterprise-grade cloud data governance
DataHub deploys in your cloud environment and connects to the tools your teams already use, with APIs that fit your existing automation workflows.
Flexible deployment options
- AWS, GCP, and Azure supported
- Managed cloud or self-hosted deployment
- Apache 2.0 licensed, no vendor lock-in
Pre-built source connectors
- Snowflake, BigQuery, Redshift, Databricks
- dbt, Airflow, Spark, Looker, Tableau
- Incremental sync keeps metadata current
Open APIs and extensibility
- GraphQL and REST APIs for full automation
- Push custom metadata from internal systems
- Extensible for tools without native connectors
Trusted by modern data teams
"DataHub gave our platform team a single place to enforce access policies and trace lineage across every data source. Governance went from a quarterly audit exercise to something we manage continuously."