What characterizes a dimension table in a data model?

What characterizes a dimension table in a data model?

• A. Contains primarily numerical measurements
• B. Contains the main data for analysis
• C. Represents business objects with textual descriptions
• D. Is used exclusively for data storage

Answer: (C)

A dimension table is a vital component of a star schema or snowflake schema within a data warehouse, typically organizing data for analytical purposes. One of its primary characteristics is that it represents business objects — such as customers, products, or time — and contains descriptive attributes associated with those objects. This allows users to understand and analyze the data in a meaningful context. These descriptive attributes often include textual or categorical information, such as names, categories, or types. For example, a dimension table for customers might include columns for customer ID, name, address, and demographic information. This rich context enables more insightful querying and reporting. In contrast, other options do not accurately capture the essence of a dimension table. While numerical measurements are often found in fact tables, they are not the focus of dimension tables. A dimension table does not contain the main data for analysis in the same sense that a fact table does, which predominantly includes the quantitative metrics to be analyzed. Moreover, a dimension table is not defined by its role in data storage alone; it exists to serve a specific purpose in the analytical layer, facilitating the understanding of the data relationships and trends.

A dimension table is a vital component of a star schema or snowflake schema within a data warehouse, typically organizing data for analytical purposes. One of its primary characteristics is that it represents business objects — such as customers, products, or time — and contains descriptive attributes associated with those objects. This allows users to understand and analyze the data in a meaningful context.

These descriptive attributes often include textual or categorical information, such as names, categories, or types. For example, a dimension table for customers might include columns for customer ID, name, address, and demographic information. This rich context enables more insightful querying and reporting.

In contrast, other options do not accurately capture the essence of a dimension table. While numerical measurements are often found in fact tables, they are not the focus of dimension tables. A dimension table does not contain the main data for analysis in the same sense that a fact table does, which predominantly includes the quantitative metrics to be analyzed. Moreover, a dimension table is not defined by its role in data storage alone; it exists to serve a specific purpose in the analytical layer, facilitating the understanding of the data relationships and trends.