Fact tables in Power BI are denormalized to optimize query performance.

TLDR

In dimensional modeling for Power BI, fact queries are denormalized to optimize query performance and enhance user understanding. This approach contrasts with highly normalized transactional systems (OLTP) that prioritize data integrity and efficient data entry/updates.

Reasons for Denormalization in Power BI Dimensional Models:

• Improved Query Performance:

  • Reduced Joins: Denormalization minimizes the number of joins required to retrieve data, as descriptive attributes are often directly included in dimension tables or, in some cases, even within the fact table itself. This significantly speeds up query execution.

  • Optimized for Read Operations: Dimensional models are designed for analytical queries and reporting, which primarily involve reading data. Denormalization structures the data in a way that facilitates fast retrieval for aggregations and filtering.

• Enhanced User Understanding:

  • Simplified Data Model: By grouping related attributes into dimension tables, the data model becomes more intuitive and easier for business users to navigate and understand.

  • Contextual Information: Dimension tables provide rich descriptive context to the quantitative measures stored in fact tables, making it simpler for users to analyze and interpret business events.

• Facilitating Star Schema Implementation:

  • The star schema, a common dimensional model design, relies on denormalized dimension tables radiating from a central fact table. This structure inherently benefits from the performance advantages of denormalization for analytical workloads.

While denormalization introduces some data redundancy, the benefits in terms of query performance and usability for analytical purposes in Power BI typically outweigh the drawbacks, especially when compared to attempting to report directly from a highly normalized OLTP system.