New: Cookbooks and AI ExplanationsStep-by-Step recipes to solve problems connected to Roadmaps and Cheat Sheets. Need more details? Use AI buttons for structured and simple explanations with concrete examples throughout the whole platform.Take a look
The transform looks right on the ten rows you checked by eye, but you have no idea if it still works after the next change.
What you'll have at the end
A pytest test that fails the moment the transform's logic breaks
You need
A transform function already running in the pipeline, checked so far only by eyeballing a handful of rows it produced, with nowhere written down for what it's actually supposed to do at every boundary.
Not covered
Catching bad rows a source itself sends downstream, or a column that changes shape without warning; this recipe only covers proving a transform's own logic keeps producing the right answer as the code around it changes.
Leans on
Give every load a data quality gate
use that one to stop a bad row a source sent from reaching a report at load time; this recipe stops a bad transform from reaching production in the first place, before any row ever runs through it.
Catch schema drift early
reach for that one when a source's own columns change shape; this recipe is for when the columns stay the same and the transform's own logic quietly stops matching its own rules.
Checked 25 Aug 2026
Part of the ETL (Extract, Transform, Load) cookbook