Data Diff provides easy 1-click regression testing for ETL

“When everything is correct, Datafold clearly saves time on testing; but when something is wrong or there’s an error, it saves unimaginable amounts of time that would go into finding and fixing bad data.”
Ezgi Ozcan
Product Analyst
REGRESSION TESTING

Data QA in minutes, not hours

Data Diff automates regression testing with integration into the CI process through GitHub and GitLab. Validate every source code change so that you can easily see how changes in your code impact the data produced across all rows and columns.

DATA QUALITY PEACE OF MIND

Less stress when merging to production

Data Diff checks every change to a data pipeline and highlights how the change in source code will affect the data produced by the pipeline. Save hours of time on manual testing while you avoid regressions in ETL. Feel confident that you won’t break anything downstream.

VISUAL COMPARISON 

Effortless code reviews and approvals

Instead of digging through code when reviewing a coworker’s hotfix, the Data Diff report card appears in the Github pull request discussion. Let code reviewers, team leadership, and all stakeholders evaluate the impact of a change at a glance.

SOLUTIONS

Data Diff use cases

Proactive
Data Testing

Test changes to code in development or before merging and use column-level lineage to track the impact across your data.

Validate
Data Migrations

Combine with Datafold’s profiler and lineage tools to validate migrations between data warehouses or ETL tools.

Automatic
Data Monitoring

Detect anomalies in data in production using Data Diff, Lineage, and Alerting for continuous data observability and quality.

integrations

Data reliability platform for the modern data stack

Trusted by high-growth data teams

"Datafold makes it a lot easier to understand the impact of your change on downstream data. The tool is super easy to use and does a great job highlighting exactly where there are differences in your data in a digestible way."
Zachary Baustein
Lead Data Analyst
“Datafold's 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 If my stakeholders ask “why is this dashboard out of date?”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 pull requests 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
“When everything is correct, Datafold clearly saves time on testing; but when something is wrong or there’s an error, it saves unimaginable amounts of time that would go into finding and fixing bad data.”
Ezgi Ozcan
Product Analyst
"While Datafold is still young and the tool is in its early stage, the foundation of the business is super sound. The core platform is so valuable. Datafold is solving a problem that no one else is trying to solve."
David Wallace
Sr. Data Engineer
"Datafold is a game-changer— there is so much value in actually understanding the effect of your pull request. It gives me the confidence that my code does what I expect it to do."
Josh Devlin
Analytics Engineer

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