Data Drift Explained: Causes, Detection & Fixes

This post details the causes, detection methods, and response strategies for data drift. It elaborates on silent schema changes, SaaS platform modifications, third-party format changes, upstream pipeline rewrites, and semantic drift as causes. For detection, it introduces statistical tests like Kolmogorov-Smirnov (KS), Wasserstein distance, Population Stability Index (PSI), and chi-squared tests, alongside schema change detection and integration with dbt, Airflow, and Dagster. The post also outlines a governed response, emphasizing immediate flagging of affected tables and the importance of feedback loops.