What to Look for with Data Diffs

In her guest blog, Sarah Krasnik, Lead Data Engineer at Perpay, emphasizes the importance of thorough quality assurance (QA) in analytics engineering to prevent production issues and maintain data integrity. She highlights the need for a programmatic approach to data QA, advocating for the use of "Data Diff" to compare datasets across environments, such as staging and production, to identify discrepancies before deployment. Krasnik discusses two key audit types: assessing the total number of rows and primary key changes, and analyzing value distributions to ensure changes align with business expectations. By conducting these audits, teams can detect potential issues early, communicate changes effectively, and avoid unexpected surprises in dashboards, thus enhancing the reliability of analytics outputs.

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