To optimize a complex semantic model by reducing joins, which tables should you denormalize?

To optimize a complex semantic model by reducing joins, which tables should you denormalize?

• A. Staging tables
• B. Snowflaked dimension tables
• C. Fact tables
• D. Reference tables

Answer: (B)

Flattening snowflaked dimension tables reduces the number of joins you need to perform when assembling a semantic model. Snowflake dimensions are normalized into multiple related tables, so queries often join the fact table to several dimension tables to pull all attributes. By denormalizing these snowflaked dimensions into single wide dimension tables, you cut out those extra joins, making queries faster and the semantic layer simpler to query. This is a common performance tuning technique in dimensional design, trading some redundancy for quicker reads. Staging tables are for ETL preparation and aren’t part of the final semantic model, so denormalizing them doesn’t address query performance in the model. Denormalizing fact tables would disrupt grain consistency and inflate size, harming scalability, and reference tables are typically small lookups where denormalization offers limited impact compared to flattening dimension hierarchies.

Flattening snowflaked dimension tables reduces the number of joins you need to perform when assembling a semantic model. Snowflake dimensions are normalized into multiple related tables, so queries often join the fact table to several dimension tables to pull all attributes. By denormalizing these snowflaked dimensions into single wide dimension tables, you cut out those extra joins, making queries faster and the semantic layer simpler to query. This is a common performance tuning technique in dimensional design, trading some redundancy for quicker reads. Staging tables are for ETL preparation and aren’t part of the final semantic model, so denormalizing them doesn’t address query performance in the model. Denormalizing fact tables would disrupt grain consistency and inflate size, harming scalability, and reference tables are typically small lookups where denormalization offers limited impact compared to flattening dimension hierarchies.