How does denormalization help analytics workloads?

• A. It increases data redundancy to speed up writes.
• B. It eliminates the need for a fact table.
• C. It improves query performance for analytics by duplicating data where necessary.
• D. It ensures all dimensions are fully normalized across the schema.

Answer: (C)

Denormalization improves analytics workloads by reducing the number of joins needed to answer queries. In analytics, you often perform broad scans and aggregations across large volumes of data, pulling attributes from both facts and dimensions. When data is denormalized, the necessary attributes are duplicated and stored together in wider, flatter structures. This means queries can read data from fewer tables and perform joins less often, which lowers I/O and speeds up aggregation and filtering operations. The trade-off is increased storage and more care needed to keep duplicated data consistent, but for analytics where read performance is paramount, this approach delivers faster query results.