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.

Trace column-level transformations from source to BI dashboard
Analyze downstream impact across up to 1,000 hops before you deploy
Connect 80+ sources: Snowflake, dbt, Airflow, Tableau, and more

Request a Demo See all capabilities

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

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."

Senior Data Engineer

Financial Services, Enterprise

Common questions

Frequently asked questions about enterprise data lineage software

How does DataHub handle column-level lineage across different SQL dialects?

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.

What is the difference between self-hosted and DataHub Cloud?

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.

How does impact analysis work at scale with large dependency graphs?

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.

Does DataHub support OpenLineage, and how does that integration work?

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.

How long does it take to get lineage working across an existing stack?

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.