Data Quality As Code

How to Get Started with Data Quality as Code in Your Pipelines

In Part 1, we explored why traditional data quality testing happens too late—after bad data has already reached production. Data Quality as Code shifts validation left, letting you catch issues during transformation before they propagate downstream. ...

Introducing Data Quality as Code: Validate Data Before It Lands, Not After

Every data team knows the scenario: By the time quality tests catch errors, bad data has already reached production and hit user dashboards and business reports. The problem isn't that organizations don't test their data; it's that they test it too l...