Data Warehousing Concepts — Stars, Snowflakes, and Dimensional Modeling
Star schema, fact and dimension tables, Kimball vs Inmon, and why transactional databases aren't enough for analytics.
Your application database is optimized for one thing: processing transactions quickly. Insert a row. Update a status. Read a single user's profile. It's great at that. But when the CEO asks "what was our revenue by product category by region for the last 12 months compared to the prior year," your transactional database starts sweating.
Data warehousing is the practice of organizing data specifically for analytical queries. The structures look different from what you're used to, and there's a reason for every design choice. Understanding these concepts is essential for building BI systems that actually perform well.
Why Transactional Databases Struggle With Analytics
Your application database (PostgreSQL, MySQL, SQL Server) uses a normalized schema — data is split across many ta
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