Why Do Data Quality Checks Fail Only After a Source Schema Changes?

A source system can add, rename, or change a field while continuing to send data. Pipelines may ingest the new shape but silently lose values, miscast records, or skip quality rules that were written for the previous schema.

What to check

Compare schema versions, null and type distributions, rejected rows, transformation warnings, and downstream field mappings. Identify the first job that diverged and inspect representative records without exposing sensitive content.

Recommended next step

Add schema-change detection and a controlled compatibility process. Update transformations and quality rules together, then replay a limited period and reconcile downstream totals before restoring normal publication.

How Informatix Systems can help

Informatix Systems provides data engineering and analytics services. We can assess the affected setup, trace dependencies, and help plan a practical improvement. Contact us with the platform involved, when the issue began, and non-sensitive error details.