How are fact tables and dimension tables typically used together?

How are fact tables and dimension tables typically used together?

• A. Fact tables hold the metadata, while dimension tables hold the data
• B. Fact tables store numerical data, while dimension tables add context
• C. Fact tables are isolated from dimension tables
• D. Fact tables contain only summaries, while dimension tables are detailed

Answer: (B)

Fact tables and dimension tables serve complementary roles in a relational database, particularly within the context of data warehousing and dimensional modeling. Fact tables are primarily designed to store quantitative data, often referred to as "measures," which can be aggregated or analyzed based on various dimensions. This numerical data could include sales amounts, quantities sold, or other metrics relevant to the business. Dimension tables, on the other hand, provide context to the data stored in fact tables. They store attributes that describe the facts, such as time periods, geographical locations, product details, or customer characteristics. This additional information allows users to analyze the numerical data in a meaningful way. For example, when examining sales data from a fact table, the associated dimension tables could provide insights into sales trends over time, performance by product category, or distribution by region, thereby enhancing the understanding of the underlying facts. The combination of fact tables and dimension tables facilitates complex queries and reporting, enabling organizations to derive business intelligence from their data effectively. This integration creates a multidimensional view of data, allowing users to slice and dice the information in various ways to uncover insights.

Fact tables and dimension tables serve complementary roles in a relational database, particularly within the context of data warehousing and dimensional modeling. Fact tables are primarily designed to store quantitative data, often referred to as "measures," which can be aggregated or analyzed based on various dimensions. This numerical data could include sales amounts, quantities sold, or other metrics relevant to the business.

Dimension tables, on the other hand, provide context to the data stored in fact tables. They store attributes that describe the facts, such as time periods, geographical locations, product details, or customer characteristics. This additional information allows users to analyze the numerical data in a meaningful way. For example, when examining sales data from a fact table, the associated dimension tables could provide insights into sales trends over time, performance by product category, or distribution by region, thereby enhancing the understanding of the underlying facts.

The combination of fact tables and dimension tables facilitates complex queries and reporting, enabling organizations to derive business intelligence from their data effectively. This integration creates a multidimensional view of data, allowing users to slice and dice the information in various ways to uncover insights.