Enterprise Data Catalog Built for Scale
Your pipelines pass. Your dashboards break anyway. DataHub gives platform engineers unified metadata, AI anomaly detection, and column-level lineage across 87+ sources.
- Semantic search finds datasets without exact keyword matches
- ML anomaly detection catches volume and freshness issues early
- Column-level lineage maps every upstream dependency
See DataHub in your environment
Request a scoped demo with a DataHub engineer.
What does data catalog failure actually cost?
Bad metadata compounds. One undocumented table becomes a broken dashboard, a missed SLA, and an audit you cannot answer.
Discovery debt
Engineers spend hours finding the right dataset, slowing delivery and increasing the cost of every data request.
Silent pipeline failures
Bad data reaches production undetected, eroding trust in dashboards and triggering costly incident reviews.
Governance gaps
No audit trail when compliance asks who owns what, leaving teams exposed during reviews and regulatory inquiries.
Lineage blind spots
Downstream impact stays unknown until something breaks, turning routine changes into unplanned incidents.
A modern data catalog built for your stack
DataHub connects to your existing infrastructure and gives every team the metadata context they need to move faster and govern confidently.
AI-powered search and discovery
DataHub indexes metadata from 87+ sources and surfaces the right dataset using semantic understanding, not just keyword matching.
- Natural language queries return ranked, relevant results
- Descriptions and tags generated from existing metadata
- Search scope filtered by domain, owner, or data tier
Proactive AI data anomaly detection
ML models monitor volume, schema, and freshness across your pipelines and surface deviations before they reach downstream consumers.
- Volume and freshness checks run on a defined schedule
- Alerts route to Slack, PagerDuty, or your incident tool
- Anomaly history tracked per dataset for trend review
Column-level lineage and impact analysis
Trace data from source to dashboard at the column level. Understand which pipelines, models, and reports are affected before you make a change.
- Column-level lineage across SQL, dbt, and Spark jobs
- Impact analysis shows affected dashboards and models
- Lineage graph exportable for audit and review purposes
Fine-grained access control and governance
Define ownership, apply data classifications, and enforce access policies at the domain or dataset level without rebuilding your existing stack.
- Role-based access policies applied at the domain level
- Ownership and stewardship assigned per dataset or schema
- Audit logs capture every metadata change with attribution
How it works
Three steps from connection to full catalog governance. No pipeline rebuilds required.
01. Connect your sources
02. Contextualize your assets
03. Activate governance platform-wide
Built for enterprise-grade cloud data governance and catalog
DataHub fits the infrastructure model your security and platform teams already operate. No rearchitecting required.
Flexible deployment options
Security and compliance controls
Open API and extensibility
Trusted by modern data teams
Platform engineers and data leaders across financial services, healthcare, and technology rely on DataHub to govern their data platforms.
"DataHub gave us the lineage visibility we needed to stop guessing about downstream impact when schemas change."
"The search experience is the first one our analysts actually use without asking the data engineering team for help."
"We passed our SOC 2 audit with a complete ownership and access history that DataHub maintained automatically."
Frequently asked questions about enterprise data catalog
Ready to bring order to your data platform?
Talk to a DataHub engineer about your environment. We will scope a demo around the problems your team is actually facing.