Dimensional Modeling

Dimensional modeling is a database design technique developed by Ralph Kimball, specifically optimized for data warehousing and business intelligence. It serves as the foundational structure for the Data Presentation Area in a DW/BI architecture (covered later).

Unlike the highly normalized models (like 3rd Normal Form) used in operational systems — which prioritize fast data entry and minimize redundancy — dimensional models are intentionally denormalized. Their primary goals are fast query performance and making the data intuitive for business users to understand.

At its core, dimensional modeling categorizes all business data into two distinct types: Facts and Dimensions. These are covered in detail on the next page.

Why use Dimensional Modeling?

• Understandability: The database schema closely mirrors how business users naturally think about and discuss their processes.
• Query Performance: A denormalized structure requires fewer complex table joins, which drastically reduces query execution time for large datasets.
• Extensibility: As business requirements evolve, new data sources, dimensions, and facts can typically be integrated without breaking existing reports or queries.