Every data engineer has built a data model that looked perfect on paper-until it met production.
Real-world systems don't stay still. APIs change, business rules evolve, and upstream data rarely behaves the way we expect. The result? Pipelines that are difficult to maintain and expensive to fix.
In this article, I share three production scenarios I've encountered and the design principles that help build data pipelines that are flexible, resilient, and easier to evolve over time.
Read the full article below.
The Problem with Perfect Data Models (And Why Your Pipeline is Breaking) by Kriti Chauhan
A practical guide to building flexible, resilient pipelines when upstream data refuses to behave.
Read on Substack
