A factless fact table is a type of fact table in a data warehouse that does not contain any measures (numerical facts). Instead, it primarily records the occurrence of events or the presence of relationships between dimensions.
Here's a breakdown :
Key Characteristics :
- Absence of Measures:
- Unlike traditional fact tables that store numerical values like sales amounts or quantities, factless fact tables do not contain such measures.
- Focus on Relationships:
- They focus on capturing the relationships or associations between dimension members.
- Presence of Dimension Keys:
- They consist primarily of foreign keys that reference dimension tables. These keys define the combinations of dimension members that are present.
- Tracking Events and Conditions:
- They are used to track events or conditions where the presence or absence of something is important, rather than the quantity.
Purpose and Use Cases :
- Tracking Events:
- Factless fact tables can be used to track events such as student attendance, participation in training sessions, or the occurrence of specific activities.
- Analyzing Coverage:
- They can be used to analyze coverage or the presence of relationships, such as which products were included in a promotion, or which customers were assigned to a specific sales representative.
- Many-to-Many Relationships:
- They are useful for representing many-to-many relationships between dimensions.
Example :
- Student Attendance:
- A factless fact table could be used to track student attendance. It would contain foreign keys referencing the student dimension, the course dimension, and the date dimension. Each record in the table would represent a student attending a specific course on a specific date. There would be no measures, just the presence of the attendance event.
- Promotional Events:
- A factless fact table can be used to track which products were included in which promotions. It would hold foriegn keys to product dimension, and promotion dimension.
