In a dimensional model, what distinguishes a dimension table from a fact table?

In a dimensional model, what distinguishes a dimension table from a fact table?

• A. Dimension tables store numerical metrics
• B. Facts describe descriptive attributes used for context
• C. Dimension tables describe attributes used for context; fact tables hold measurable events with numeric metrics
• D. They are the same

Answer: (C)

In dimensional modeling, you separate data into descriptive attributes (dimensions) and measurements (facts). Dimension tables provide the context for analysis, storing descriptive, textual, or categorical attributes such as product name, category, customer region, or date. Fact tables capture the actual measurable events or transactions and hold numeric metrics like sales amount, quantity sold, or profit, typically alongside keys that link to the related dimensions. This structure is designed around a defined grain, so each fact row represents a single measured event tied to specific dimension values. That’s why the statement describing dimension tables as containing context-describing attributes and fact tables as holding measurable events with numeric metrics is the best fit. It contrasts with the idea that dimension tables store metrics or that facts describe attributes, and it’s clear they are distinct kinds of tables that work together in a star schema.

In dimensional modeling, you separate data into descriptive attributes (dimensions) and measurements (facts). Dimension tables provide the context for analysis, storing descriptive, textual, or categorical attributes such as product name, category, customer region, or date. Fact tables capture the actual measurable events or transactions and hold numeric metrics like sales amount, quantity sold, or profit, typically alongside keys that link to the related dimensions. This structure is designed around a defined grain, so each fact row represents a single measured event tied to specific dimension values.

That’s why the statement describing dimension tables as containing context-describing attributes and fact tables as holding measurable events with numeric metrics is the best fit. It contrasts with the idea that dimension tables store metrics or that facts describe attributes, and it’s clear they are distinct kinds of tables that work together in a star schema.