What are some common scenarios or use cases for applying factless fact tables and degenerate dimensions?

1 Factless fact tables

A factless fact table is a table that contains only foreign keys to dimensions, but no numeric facts. It is used to capture events or situations that have no measurable outcome, but are important for analysis. For example, you can use a factless fact table to record student attendance, product promotions, or customer visits. The factless fact table can be joined with the relevant dimensions to answer questions like: How many students attended a specific course? Which products were promoted in a certain period? How often did a customer visit a store?

3 Benefits of factless fact tables and degenerate dimensions

Factless fact tables and degenerate dimensions can help you simplify and optimize your dimensional model by reducing the number of tables and columns, avoiding redundancy and inconsistency, and improving query performance and flexibility. For example, you can use a factless fact table to avoid creating a separate dimension for each event or situation that has no facts, and use a degenerate dimension to avoid creating a separate fact table for each transaction or event that has only one fact.

4 Challenges of factless fact tables and degenerate dimensions

Factless fact tables and degenerate dimensions can also pose some challenges and trade-offs for your dimensional model. For example, you need to ensure that the factless fact table has a clear and meaningful grain, that is, the level of detail at which the events or situations are recorded. You also need to ensure that the degenerate dimension has a unique and consistent value for each transaction or event, and that it does not contain any business meaning or logic that belongs to another dimension.

5 Best practices for factless fact tables and degenerate dimensions

To use factless fact tables and degenerate dimensions effectively, you should follow some best practices and guidelines. For example, you should name the factless fact table and the degenerate dimension clearly and consistently, using terms that reflect the business context and purpose. You should also document the grain and the source of the factless fact table and the degenerate dimension, and explain how they relate to other facts and dimensions in your model. You should also test and validate your factless fact table and degenerate dimension with sample data and queries, and monitor their size and performance over time.

6 Examples of factless fact tables and degenerate dimensions

To illustrate how factless fact tables and degenerate dimensions can be applied in different scenarios, let's look at some examples. Suppose you have a data warehouse for a retail company that sells products online and offline. You can use a factless fact table to store the product reviews that customers leave on the website, with foreign keys to the product, customer, and date dimensions. You can use a degenerate dimension to store the order number for each sales transaction, with a foreign key to the sales fact table. You can then use these tables to analyze the customer feedback and the sales performance by product, customer, and date.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?