Why Silent Data Corruption in Pipelines Goes Undetected for Weeks

Short summary

An analysis of the most dangerous category of data pipeline failure: silent data corruption from schema drift, upstream renames, and vendor swaps that don't trigger any alerts. The article explains why pipeline monitoring built around availability rather than correctness misses these issues, and how null rates, field cardinality, and value distribution can catch drift early. It highlights the retrospective damage problem where corrupted data bakes into history before anyone notices.

• •Silent data corruption from schema drift is the most common and dangerous pipeline failure
• •Most pipeline monitoring checks availability not semantic correctness of data
• •Null rates, field cardinality, and value distribution are the signals that catch drift early

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