Know exactly how the data will change with your code updates to identify any unexpected regressionsâbefore deploying to production. Works out of the box with dbt, Airflow, or any git repository containing data transformation code.
Integrate data testing into your development workflow with GitHub, GitLab, Bitbucket, and Azure DevOps integrations. See the impact of your change right in the pull request. Prevent regressions and streamline pull request reviews.
"Datafold is a game-changerâ there is so much value in actually understanding the effect of your pull request.â
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Move your data quality "to the left" as you stop the data incidents before they begin. See how any code change in the ELT or BI code affects the resulting data, all the way down to the value-level.
âDatafold is the missing piece of the puzzle for data quality assurance. When I first heard about Datafold, all I could think was âFinally!â
Assess the effect of any proposed code change on other datasets, BI dashboards and ML models using Datafold's integrated column-level lineage. Avoid unexpected regressions and silent breakages.
"Column-level lineage gives a holistic view of data dependencies and interdependencies. Itâs so powerful - with even more insight than table-level lineage - I get really excited about what it can do!"
Data engineers, analysts, and scientists can all test their code the same way. Streamline code review processes knowing that every code change has been tested and checked for consistent data quality assurance.
âDatafold gives us full confidence when we ship changes into production, which historically I couldnât say about PRs.â
