Built for platform teams

The Data Governance Solution Built for Scale

Your pipelines pass. Your dashboards break anyway. DataHub gives platform engineers policy enforcement, lineage, and data quality across every source.

  • Enforce fine-grained access policies across every data asset
  • Column-level lineage across 60+ sources, captured automatically
  • Data quality assertions that catch failures before they reach prod
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See DataHub govern your data stack

Talk to a DataHub engineer about your specific environment.

Trusted by modern data teams
The real cost

What does a broken data governance solution cost?

Governance failures don't announce themselves. They surface in standups, audits, and incidents that trace back to gaps your tools never caught.

Policies with no enforcement

Access rules live in a spreadsheet. No one knows who can see what until an audit asks.

Lineage that stops mid-pipeline

You know a dashboard broke. You don't know which upstream table caused it or who owns it.

Quality checks that run too late

Bad data reaches production. The data team finds out when a stakeholder files a ticket.

Governance work that doesn't scale

Every new source means manual tagging, manual ownership, and another backlog item no one clears.

There's a better way to build governance that holds at scale.

The platform

A better way to run a data governance platform

DataHub connects policy enforcement, data quality, lineage, and catalog into one open platform your team controls.

Policy enforcement

Governance framework tools that enforce

DataHub's policy engine applies fine-grained RBAC across every asset in your catalog. Rules are version-controlled, auditable, and enforced at query time, not after the fact.

  • Actor, group, and role-based policies with conditional logic
  • Policy violations trigger alerts via Slack, Teams, or email
  • Audit logs ready for compliance and security reviews
Data quality

Data governance and data quality tools

Five assertion types run continuously against your sources. Data contracts formalize producer-consumer SLAs before incidents happen.

  • Freshness and volume assertions with configurable thresholds
  • Data contracts with schema, quality, and SLA guarantees
  • Quality trend dashboards surfaced per domain or asset
Data catalog

A data governance catalog teams use

60+ pre-built connectors ingest metadata from warehouses, lakes, BI tools, and orchestrators automatically. Business glossary and domain assignment give every asset an owner and a context.

  • Connectors for Snowflake, BigQuery, dbt, Looker, and 56 more
  • Business glossary with domain and ownership assignment
  • Search and discovery across 100M+ assets at sub-second speed
Lineage and analytics

Data governance analytics, column to dashboard

Column-level lineage tracks every transformation from raw ingestion to production report. Impact analysis shows downstream dependencies before you make a change that breaks something.

  • Column-level lineage across dbt, Airflow, Spark, and ETL tools
  • Impact analysis before schema changes reach downstream consumers
  • Governance analytics: coverage, compliance rates, and SLA trends

See exactly how DataHub fits into the stack you already run.

The process

How it works

Three steps from your existing infrastructure to governed, observable data.

Connect your sources

Ingest from Snowflake, BigQuery, dbt, Airflow, Kafka, and 55 more
Metadata arrives automatically, no manual entry required
Existing pipelines stay intact, DataHub reads what's already there

Apply data governance automation tools

Set access policies, quality assertions, and data contracts in code
Automated alerts fire when freshness SLAs breach or schemas change
Ownership and domain assignment propagate across related assets

Activate lineage and governance analytics

Column-level lineage is queryable from day one of ingestion
Impact analysis runs before any schema change reaches downstream
Governance dashboards show coverage, compliance, and quality trends

Teams running Snowflake, dbt, and Looker have been through this exact path.

Enterprise ready

Built for enterprise-grade governance at any scale

DataHub deploys where your data lives, integrates with how your team works, and exposes every governance action through open APIs.

A data governance operating model your team controls

Self-hosted on Kubernetes with Helm charts provided
AWS, GCP, or Azure deployments with your own infrastructure
SaaS option for teams that prefer managed infrastructure

GraphQL and REST APIs for every governance workflow

Programmatic governance workflows via GraphQL API
Python SDK for data contract automation and CI/CD integration
Webhook integrations for pipeline-triggered governance checks

Observability built into the platform

Prometheus and Grafana integration for infrastructure monitoring
Slack, Teams, and email notifications for policy and quality events
Access audit logs exportable for compliance and security reviews

See what data teams running this infrastructure say about it in production.

Peer reviews

Trusted by modern data teams

DataHub

Gartner Peer Insights

Overall rating

4.5 / 5.0 across 12 reviews

Metadata Management Solutions market, Gartner Peer Insights

"New-joiners are always impressed by the lineage features. Experienced developers appreciate the data quality checks."

Engineer, Services Industry

Company size: 1B-10B USD

"DataHub brings a centralized single source of truth to all of your data operations. You can version your KPIs and align on them with the team."

Data Analyst, Software Industry

Company size: 50M-1B USD

You've seen the evidence. Here's what happens when you request a demo.

FAQ

Frequently asked questions about data governance solutions

DataHub's policy engine evaluates actor, group, and role-based rules at query time using attribute-based access control. Policies are defined in code, version-controlled, and applied across every asset in the catalog. When a violation occurs, automated alerts fire via Slack, Teams, or email. Audit logs capture every access event for compliance reporting.
Yes. DataHub's assertion framework runs independently of your pipeline execution. It connects to your sources, Snowflake, BigQuery, dbt, and others, and evaluates freshness, volume, field-level, and custom SQL assertions on a schedule you configure. Your pipelines don't change. DataHub observes and alerts on what's already running.
For sources like dbt, Snowflake, and Looker, lineage is captured automatically during ingestion with no additional configuration. For custom transformations, DataHub provides a Python SDK and REST API for programmatic lineage submission. Column-level granularity is available from day one for supported connectors. The complexity depends on your stack, and DataHub engineers will map that out with you during onboarding.
DataHub supports domain-based ownership, so governance responsibilities map to the teams that own the data. Each domain gets its own policies, quality thresholds, and glossary terms. Workflow automation handles cross-team approval requests and escalations. The governance analytics layer tracks coverage and compliance per domain, so platform teams can report on adoption without chasing spreadsheets.
DataHub supports self-hosted Kubernetes deployments with Helm charts, cloud deployments on AWS, GCP, or Azure, and a managed SaaS option. Docker Compose is available for local development. All deployment options expose the same GraphQL and REST APIs, so governance workflows are portable regardless of where DataHub runs. Security and compliance requirements, including audit logging and RBAC, are consistent across all deployment types.
Get started

Ready to build a data governance solution that holds at scale?

DataHub gives platform engineers the policy enforcement, lineage, and data quality automation to govern every asset across your stack.

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No generic walkthrough. You'll speak with a DataHub engineer about your specific stack.

Policy enforcement from day one
60+ connectors, no pipeline rebuilds
Self-hosted or managed SaaS