fact table
A fact table is the central table in a star schema of a data warehouse. A fact table stores quantitative information for analysis and is often denormalized.
A fact table works with dimension tables. A fact table holds the data to be analyzed, and a dimension table stores data about the ways in which the data in the fact table can be analyzed. Thus, the fact table consists of two types of columns. The foreign keys column allows joins with dimension tables, and the measures columns contain the data that is being analyzed.
Suppose that a company sells products to customers. Every sale is a fact that happens, and the fact table is used to record these facts. For example:
| Time ID | Product ID | Customer ID | Unit Sold |
| 4 | 17 | 2 | 1 |
| 8 | 21 | 3 | 2 |
| 8 | 4 | 1 | 1 |
Now we can add a dimension table about customers:
| Customer ID |
Name |
Gender |
Income |
Education |
Region |
| 1 | Brian Edge | M | 2 | 3 | 4 |
| 2 | Fred Smith | M | 3 | 5 | 1 |
| 3 | Sally Jones | F | 1 | 7 | 3 |
In this example, the customer ID column in the fact table is the foreign key that joins with the dimension table. By following the links, you can see that row 2 of the fact table records the fact that customer 3, Sally Jones, bought two items on day 8. The company would also have a product table and a time table to determine what Sally bought and exactly when.
When building fact tables, there are physical and data limits. The ultimate size of the object as well as access paths should be considered. Adding indexes can help with both. However, from a logical design perspective, there should be no restrictions. Tables should be built based on current and future requirements, ensuring that there is as much flexibility as possible built into the design to allow for future enhancements without having to rebuild the data.
