Data governance platform

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.

  • Fine-grained RBAC down to the column level, enforced at query time
  • Automated data quality monitoring with contract-based SLA enforcement
  • End-to-end lineage captured from your existing stack, no rewrites needed
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See the governance framework in action

A DataHub engineer will walk through your specific environment.

Trusted by modern data teams
The real cost

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.

The platform

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

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
Enterprise ready

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

Run DataHub on AWS, GCP, or Azure. Managed cloud and self-hosted options are available to match your security and compliance requirements.

AWS, GCP, and Azure supported
Managed cloud or self-hosted deployment
Apache 2.0 licensed, no vendor lock-in

Pre-built source connectors

Ingest metadata from Snowflake, BigQuery, Redshift, dbt, Airflow, Looker, and more through maintained connectors with incremental sync support.

Snowflake, BigQuery, Redshift, Databricks
dbt, Airflow, Spark, Looker, Tableau
Incremental sync keeps metadata current

Open APIs and extensibility

GraphQL and REST APIs let your platform team automate governance workflows, push custom metadata, and integrate DataHub into your internal tooling.

GraphQL and REST APIs for full automation
Push custom metadata from internal systems
Extensible for tools without native connectors
Customer voice

Trusted by modern data teams

Financial Services
Senior Data Platform Engineer
Source
Gartner Peer Insights
"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."
Senior Data Platform Engineer
Financial Services, via Gartner Peer Insights
FAQ

Frequently asked questions about data governance frameworks

Most platform teams complete initial ingestion and metadata configuration within a few weeks. The timeline depends on the number of sources and the complexity of your existing access control model. DataHub's pre-built connectors reduce the setup time for common warehouses and pipeline tools. Teams with more complex environments typically phase the rollout by domain.
DataHub enforces RBAC at the platform level, with policies that apply down to the column. Roles can be scoped to domains, asset groups, or individual datasets. Permissions propagate automatically when new assets are ingested, reducing the overhead of manual access management at scale.
Yes. DataHub runs quality assertions against your data sources directly, without requiring modifications to existing pipeline code. You define expectations as contracts, and DataHub monitors them on a schedule you control. Failures are routed to the asset owner, not surfaced as a generic alert.
DataHub includes pre-built connectors for Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow, Spark, Looker, Tableau, and others. Metadata ingestion runs incrementally, and the open API supports custom integrations for tools not covered by a native connector.
DataHub uses a domain model that lets individual teams own and govern their data assets while platform teams retain visibility and policy control across the organization. Ownership, glossary terms, and quality contracts are all scoped and delegated at the domain level. This means domain teams move at their own pace without creating blind spots for the platform team.

Ready to govern your data without slowing your team down?

Talk to a DataHub engineer about your environment. We will show you how the data governance framework fits your existing stack.

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No generic walkthrough Scoped to your environment You speak with an engineer