A fact table is a central component of a dimensional model in data warehousing. It is a relational table that contains quantitative measurements or facts related to a business process or event. The primary purpose of a fact table is to provide a comprehensive view of business activities and enable data analysis and reporting.
In a fact table, each row represents a specific instance or occurrence of a business event, such as a sales transaction, a customer purchase, or a website click. The columns in the fact table, known as measures or metrics, represent key performance indicators (KPIs) or numerical values associated with the event. These measures can include sales revenue, quantity sold, cost, profitability, or any other relevant numerical information.
Fact tables typically have foreign keys that link to various dimensions, such as time, location, products, or customers. These dimensions provide context and help in organizing and categorizing the data. The combination of the fact table and related dimension tables forms a star schema or snowflake schema, which is widely used in dimensional modeling for effective data analysis.
Fact tables are typically large in size and optimized for analytical queries. They are often denormalized to improve query performance and contain aggregated data to represent summaries over time or other dimensions. Fact tables play a vital role in supporting complex business intelligence tasks, such as trend analysis, data mining, forecasting, and decision-making processes.
