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.
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Enforce fine-grained access policies across every data asset
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Column-level lineage across 60+ sources, captured automatically
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Data quality assertions that catch failures before they reach prod
Talk to a DataHub engineer, not a sales script, about your environment.
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
Peer reviews
Trusted by modern data teams
Gartner Peer Insights
Verified review
Overall rating
4.5 / 5.0 across 12 reviews
"New-joiners are always impressed by the lineage features. Experienced developers appreciate the data quality checks."
Gartner Peer Insights
Verified review
Key outcome
Centralized data operations
"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."
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.
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.
Apache 2.0 open source
60+ pre-built connectors
Self-hosted or managed deployment