column-level lineage

Plug and play column-level lineage for the modern data stack

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See exactly where your data is coming from and going toin seconds

Get big picture perspective and granular understanding

Trusted by the most data-driven companies

SQL Compiler

Get same-day column-level lineage. No developer resources needed.

Simply connect your data warehouse and you can explore your lineage graph. SOC 2 compliant, Datafold analyzes every SQL statement in your data warehouse and produces the graph of dependencies. See how data is produced and consumed - even correlated subqueries, CASE WHEN statements, and other complex queries are covered.

fast DATA Diagnosis

Troubleshoot in seconds, not hours

“Column-level lineage gives confidence in the whole system. If my stakeholders ask “why is this dashboard out of date?” I can answer in 25 seconds instead of digging through PRs for hours. As a product owner, I can understand how the rest of the company makes decisions based on the data we produce. It brings data confidence and visibility to the company.”

Maura Church

Director of Data Science

intuitive ux

Explore dependencies across thousands of tables and columns with ease

Get a high-level overview of your pipelines, zoom in on particular tables, trace flow on a columnal level and see the SQL statements for each step

graphql api

Bring the lineage where you need it

Using Datafold's GraphQL Metadata API, you can query and export lineage into other systems and data catalogs such as Amundsen & DataHub.

complete data observability

Keep the right person in the loop and avoid nasty surprises

With column-level lineage you can see exactly how your changes could impact downstream tables or dashboards. Know exactly which stakeholders to inform about potential shifts.

use cases

Build confidently with column-level lineage

Pipeline observability
  • Easily trace upstream & downstream dependencies for any dataset, column, and BI asset in your warehouse.

  • Ensure data privacy by tracking columns that contain PII data throughout the pipeline.

Improve change management
  • See the downstream impact of any change to the raw data or transformations

  • Align data producers and data consumers

Accelerate migrations
  • Identify and prioritize migrations based on usage and dependencies

  • Deprecate unused and stale data