Denormalization: When and Why to Flatten Your Data

Normalization is the first rule taught in database design. Eliminate redundancy. Store each fact once. Use foreign keys. It's the right rule for transactional systems. And it's the wrong rule for most analytics workloads. Denormalization is the deliberate introduction of redundancy into your data model to reduce joins and speed up queries. Done poorly, it creates a maintenance nightmare. Done well, it turns slow dashboards into fast ones and makes your data accessible to analysts and AI agents who can't write 12-table joins. What Normalization Gives You (and What It Costs) Normalization (Third Normal Form and beyond) organizes data so that each piece of information exists in exactly one place. A customer's city lives in the customers table. An order's product lives in the order_items table joined to the products table. What normalization gives you: No update anomalies (change a city in one row, not thousands) Smaller storage footprint (no duplicated data) Strong data integrity (constra