What are some best practices and pitfalls to avoid when using factless fact tables?

1 Choose the right grain

The grain of a factless fact table determines the level of detail and the number of rows in the table. You should choose the grain that matches your business requirements and analysis needs, and that avoids unnecessary complexity and redundancy. For example, if you want to analyze student attendance by day, you don't need to include the hour or the minute as part of the grain. On the other hand, if you want to analyze product promotions by store and by date, you need to include both dimensions as part of the grain.

2 Use descriptive column names

Since factless fact tables do not have any measures, they rely on the foreign keys to the dimension tables to provide context and meaning. Therefore, you should use descriptive and consistent column names for the foreign keys, and avoid generic names like key1, key2, or key3. For example, if you have a factless fact table for customer feedback, you should use column names like customer_id, product_id, feedback_type, feedback_date, and so on.

3 Add surrogate keys and flags

Surrogate keys are artificial keys that are generated by the data warehouse to uniquely identify each row in a factless fact table. Surrogate keys can help you improve the performance and maintainability of your factless fact tables, as they are independent of the source system keys and can be indexed and joined more efficiently. Flags are binary columns that indicate whether a certain event or condition occurred or not. Flags can help you simplify the queries and calculations on your factless fact tables, as they can be easily aggregated and filtered. For example, if you have a factless fact table for product promotions, you can add a surrogate key for each promotion event, and a flag for each product that participated in the promotion.

4 Avoid sparse or degenerate tables

Sparse tables are factless fact tables that have a large number of dimensions but a small number of rows. Sparse tables can waste space and resources, and make the queries and reports more complicated and slow. Degenerate tables are factless fact tables that have only one dimension and no foreign keys. Degenerate tables can lose the benefits of dimensional modeling, and create confusion and inconsistency in the data warehouse. You should avoid creating sparse or degenerate tables, and instead consider consolidating or splitting them into more meaningful and balanced tables.

5 Validate and document your tables

Factless fact tables can be tricky to validate and understand, as they do not have any obvious measures or indicators of quality and accuracy. You should validate your factless fact tables by comparing them with the source data and the business rules, and by checking for any missing, duplicate, or invalid rows. You should also document your factless fact tables by explaining their purpose, scope, grain, dimensions, keys, flags, and any other relevant information. This will help you and your users to use your factless fact tables correctly and effectively.

6 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?