Post by Prateek Jalgaonkar

Normalize your data before it normalizes your weekend plans 😉 Let’s talk data modeling — and more specifically, normalization. In a world where denormalized, distributed, and cloud-native architectures are all the buzz, it's easy to overlook the foundational importance of normalized data models. But whether you're building ETL pipelines, designing OLTP systems, or modeling staging layers — normalization is still critical. 💡 Of course, there are times to denormalize for performance — especially in read-heavy analytical workloads. But that decision should come after a solid, normalized base is built. Denormalization has its place- especially for analytics, but it should be a conscious trade-off — not the default. What’s your take? How do you balance normalization vs. denormalization in your data pipelines? Let’s talk in the comments ⬇️ #DataEngineering #DataModeling #Normalization #DatabaseDesign #SQL #ETL #DataPipelines #BCNF #OLTP #OLAP #ScalableData #DataIntegrity #TechLeadership