Fact and dimension tables are key components of a dimensional data model used in data warehousing. They help organize and manage large volumes of data, allowing for more efficient querying and analysis of data.
A fact table contains the quantitative measurements, or “facts,” of a data warehouse. These facts are often numeric values that can be aggregated, such as sales amounts or quantities, and are usually grouped by dimensions. Fact tables have a primary key that is a composite of all the foreign keys that link to the dimension tables.
Dimension tables provide the context for the facts in the fact table. They contain descriptive attributes, such as customer name or product category, which are used to slice and dice the facts in the fact table. Dimension tables have a primary key that uniquely identifies each row of data, and they are linked to the fact table through foreign keys.
The relationship between fact and dimension tables is often represented as a star schema, where the fact table is at the center of the schema, surrounded by the dimension tables. The fact table is typically much larger than the dimension tables and is often used for reporting and analysis.
In summary, fact tables contain the quantitative measurements of a data warehouse, while dimension tables provide the context for those measurements. Together, they form a dimensional model that enables efficient querying and analysis of large volumes of data.