A fact table is the central table in a dimensional data model that stores measurable, quantitative data about business events. It represents the “what happened” in analytics systems.

Examples of fact tables include:

• Sales transactions

• Website sessions

• Support tickets

• Financial ledger entries

Each row in a fact table represents a single event at a defined grain, such as one order, one page view, or one support interaction.

Fact tables typically include:

• Foreign keys to dimension tables

• Numeric measures (revenue, quantity, duration)

• Timestamps

From a BI perspective, fact tables are where metrics come from. Aggregations like sum, count, and average are performed on fact tables.

Choosing the correct grain is critical. A fact table at the wrong grain leads to incorrect metrics and poor performance.

Fact tables are optimized for analytical queries and work best with star schemas. They enable fast aggregation and slicing across dimensions.