
Imagine it’s a typical Monday morning in a bustling data science team. A critical Python pipeline, responsible for processing customer data, crashes without warning. Hours are spent debugging, only to discover that a subtle change in the upstream data format—say, a column name tweak or an unexpected negative value—has derailed everything. This scenario is all too common, and it underscores










