Denormalization Patterns

There are several standard denormalization patterns, each with specific use cases and tradeoffs. Knowing which pattern fits your situation saves you from reinventing solutions that the industry has already standardized. Pattern 1: Pre-Joined Dimension Columns Copy frequently-queried dimension attributes directly into the fact table. Instead of joining fact_orders to dim_customer to get customer_region, store customer_region on the fact row. This eliminates the JOIN for every query that filters or groups by region. Tradeoff: if a customer changes region, every historical fact row still shows the old region. This may be correct (revenue should be attributed to the region at the time of purchase) or incorrect (customer reports should show current region). Know which case applies. Pattern 2: S

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This section is part of the Beyond 3NF lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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