In data warehousing, which table type is optimized for quantitative measurements and analytics?

In data warehousing, which table type is optimized for quantitative measurements and analytics?

• A. Dimension table.
• B. Staging table.
• C. Lookup table.
• D. Fact table.

Answer: (D)

In data warehousing, quantitative measurements and analytics hinge on the fact table. A fact table stores numeric measures—such as sales_amount, units_sold, or total_cost—at a defined grain (for example, one row per product per day). It is designed to support fast aggregation and complex queries by linking to dimension tables that provide descriptive context (like product, customer, date, and store) through foreign keys. The typical structure uses a composite key made from the surrounding dimension keys and holds the measured values. Dimension tables hold the descriptive attributes used for filtering and grouping, staging tables are used for ETL processing of raw data, and lookup tables provide small reference data. So, for analytics on quantitative data, the fact table is the optimized choice.

In data warehousing, quantitative measurements and analytics hinge on the fact table. A fact table stores numeric measures—such as sales_amount, units_sold, or total_cost—at a defined grain (for example, one row per product per day). It is designed to support fast aggregation and complex queries by linking to dimension tables that provide descriptive context (like product, customer, date, and store) through foreign keys. The typical structure uses a composite key made from the surrounding dimension keys and holds the measured values. Dimension tables hold the descriptive attributes used for filtering and grouping, staging tables are used for ETL processing of raw data, and lookup tables provide small reference data. So, for analytics on quantitative data, the fact table is the optimized choice.