Why should you choose a star schema instead of a snowflake schema?

A star schema is a denormalized data model where each dimension table is directly linked to the fact table. This design offers several advantages over a snowflake schema, which normalizes dimensions into multiple related tables. First, query performance is typically faster in a star schema because it requires fewer joins; BI tools often execute large, read‑heavy queries that benefit from simple joins. Second, the denormalized structure is easier for analysts to understand and navigate, reducing the learning curve for reporting. Third, ETL processes are simpler: loading data into a star schema involves fewer transformations and less dependency on complex join logic. The trade‑off is that star schemas can consume more storage due to duplicated dimension data, but modern columnar storage and compression mitigate this cost. Therefore, when the primary goal is quick, ad‑hoc reporting and ease of use for business users, a star schema is preferred. In contrast, a snowflake schema is chosen when storage efficiency and strict data integrity are paramount, and the added complexity of joins is acceptable.