Column-level lineage
Enterprise Data Lineage Software Built for Scale
Your pipeline passed. Your dashboard broke anyway. Enterprise data lineage software that traces every column, every hop, across every platform.
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Trxace column-level transformations from source to BI dashboard
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Analyze downstream impact across up to 1,000 hops before you deploy
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Connect 80+ sources: Snowflake, dbt, Airflow, Tableau, and more
See DataHub lineage in your environment
A DataHub engineer will scope the demo to your stack.
Trusted by modern data teams
The real cost
What does a lineage gap actually cost you?
Table-level tracking tells you a pipeline failed. It does not tell you which columns broke, which dashboards are wrong, or who is affected.
The standup you dread
A dashboard breaks. You spend two hours tracing it upstream. The answer was a column rename three hops back.
Schema changes with no warning
A source table changes. Downstream models fail silently. You find out when a stakeholder files a ticket.
Compliance questions you cannot answer
An auditor asks where a field originated. You have table-level lineage. That is not enough.
Impact analysis done manually
Every schema change requires a manual dependency audit. At scale, that is days of work per change.
How DataHub solves it
Enterprise data lineage software built for your stack
Column-level precision, cross-platform coverage, and impact analysis at enterprise scale. Built on open standards, extensible by design.
Trace every field, not just tables
DataHub tracks column-level transformations through SQL, dbt models, and BI layers. You see exactly how each field moves, joins, and aggregates from source to dashboard.
- Visualize field-level paths through joins and aggregations
- Trace provenance from raw source through transformation to report
- Navigate column lineage visually in the DataHub UI
One graph across your stack
80+ production connectors unify lineage from warehouses, orchestration tools, and BI platforms into a single graph. No stitching, no gaps between systems.
- Warehouses: Snowflake, BigQuery, Redshift, Databricks
- Transformation: dbt, Spark, Airflow, and more
- BI: Tableau, Power BI, Looker, end-to-end visibility
Know the blast radius first
DataHub's impact analysis traverses up to 1,000 hops and 40,000 relationships per query. See every downstream dependency before a schema change reaches production.
- Multi-hop traversal: up to 1,000 hops, 40,000 relations per query
- Identify all downstream assets affected by a schema change
- Run impact reports before deprecations or pipeline changes
API-first, open by design
DataHub exposes lineage via GraphQL and REST APIs. Integrate lineage data into your own tooling, automate governance workflows, and extend the catalog platform to fit your architecture.
- GraphQL and OpenAPI endpoints for lineage queries
- Apache 2.0 licensed, 12,000+ GitHub stars
- Emit lineage events via OpenLineage standard
How it works
How data lineage software connects your stack
Three steps from your existing infrastructure to a complete, queryable lineage graph. No rebuilding pipelines, no forklift migration.
Connect your sources
- Ingest from Snowflake, dbt, Airflow, Tableau, and 80+ more
- Works with your existing pipelines, no rebuilding required
- Emit lineage events via OpenLineage or native connectors
Contextualize your graph
- Column-level lineage stitched across all connected platforms
- Ownership, tags, and documentation attached to each asset
- Relationships resolved across warehouse, transform, and BI layers
Activate lineage at scale
- Run impact analysis before any schema change or deprecation
- Query lineage programmatically via GraphQL or REST API
- Teams like yours have already done this in production environments
Enterprise ready
A data catalog platform built for enterprise scale
Deployment flexibility, fine-grained access control, and open standards support for organizations with complex infrastructure requirements.
Access control and RBAC
- Role-based access control at the asset and metadata level
- SSO integration via SAML and OIDC
- Audit logs for compliance and governance reporting
Deployment and scale
- Self-hosted or DataHub Cloud managed deployment options
- Scales to millions of metadata entities and relationships
- OpenLineage standard support for interoperability
Peer validation
Enterprise data lineage trusted by modern data teams
Gartner Peer Insights
Verified Review
Recognized for
Data lineage and governance depth
"DataHub gave us column-level lineage across Snowflake, dbt, and Tableau in a single view. We went from hours of manual tracing to answering impact questions in minutes."
Frequently asked questions about enterprise data lineage software
DataHub parses SQL at ingestion time using dialect-aware parsers for
Snowflake, BigQuery, Redshift, Spark SQL, and others. Column-level lineage is extracted from SELECT
statements, CTEs, joins, and aggregations. For dbt, DataHub reads the compiled manifest directly, so
lineage reflects the actual transformation logic rather than raw SQL. If your environment uses a
dialect that is not yet supported, the OpenLineage integration allows you to emit lineage events from
any custom pipeline.
Self-hosted DataHub is the open source distribution, licensed under Apache
2.0. You deploy it on your own infrastructure, manage upgrades, and retain full control over data
residency. DataHub Cloud is a managed SaaS offering that handles infrastructure, upgrades, and
availability. Both options support the same connector set, lineage capabilities, and API surface. The
right choice depends on your team's operational capacity and your organization's data residency
requirements.
DataHub's lineage graph is stored in a purpose-built metadata store
optimized for graph traversal. Impact analysis queries traverse up to 1,000 hops and resolve up to
40,000 relationships per query. For very large graphs, traversal depth and direction are configurable
so you can scope queries to the relevant portion of the dependency tree. Performance depends on your
deployment configuration and hardware, but the architecture is designed for enterprise-scale metadata
volumes.
DataHub supports the OpenLineage standard as both an emitter and a
receiver. You can configure DataHub as an OpenLineage backend, so any tool that emits OpenLineage
events (Airflow with the OpenLineage provider, Spark, Flink, and others) will automatically populate
lineage in DataHub. This means you are not locked into DataHub-specific instrumentation for custom
pipelines. The integration is documented and production-tested across multiple enterprise deployments.
It depends on the complexity of your stack and how many sources you are
connecting. For a standard environment with Snowflake, dbt, and one BI tool, most teams have lineage
populated within a day or two of initial configuration. Broader rollouts across many sources take
longer, particularly if custom pipelines require OpenLineage instrumentation. The demo is scoped to
your specific environment so you can get a realistic estimate based on what you are actually running.
Ready to see your full lineage graph?
Column-level lineage across your entire stack, with impact analysis before anything reaches production. A DataHub engineer will scope the demo to your environment.
No generic walkthrough
Scoped to your stack
Apache 2.0 open source