How do you handle multiple factless fact tables in a single data warehouse?

1 Identify the purpose and scope of each factless fact table

The first step is to clearly define the purpose and scope of each factless fact table in your data warehouse. What business question or problem are you trying to answer or solve with each table? What are the relevant dimensions and attributes that describe each event or condition? How are they different from the other factless fact tables in your data warehouse? By answering these questions, you can avoid creating unnecessary or overlapping factless fact tables that may cause confusion or inconsistency in your analysis.

2 Use degenerate dimensions to link factless fact tables

A degenerate dimension is a dimension that has only one attribute, which is usually the key of the fact table. For example, a sales transaction ID or a customer visit ID. Degenerate dimensions are useful for linking factless fact tables that share the same event or condition, but have different levels of detail or granularity. For example, you may have a factless fact table that records customer visits to your website, and another factless fact table that records the actions they performed on each page. You can use the customer visit ID as a degenerate dimension to link these two tables and analyze the customer behavior across different pages.

3 Use bridge tables to handle many-to-many relationships

A bridge table is a table that connects two or more dimensions that have a many-to-many relationship. For example, a product promotion may apply to multiple products, and a product may be part of multiple promotions. A bridge table can store the relationship between the product and promotion dimensions, and link them to the factless fact table that records the promotion events. Bridge tables can help you handle complex scenarios where multiple factless fact tables need to join with the same dimensions, but have different cardinalities or associations.

4 Use conformed dimensions to ensure consistency and compatibility

A conformed dimension is a dimension that has the same meaning and values across different fact tables in your data warehouse. For example, a customer dimension or a date dimension. Conformed dimensions can help you ensure consistency and compatibility among multiple factless fact tables that share the same dimensions, but have different facts or measures. By using conformed dimensions, you can avoid duplication or discrepancy of dimension data, and enable cross-functional analysis and reporting across different factless fact tables.

5 Use role-playing dimensions to avoid aliasing or snowflaking

A role-playing dimension is a dimension that plays different roles or perspectives in different fact tables. For example, a date dimension can play the role of order date, delivery date, or invoice date in different fact tables. Role-playing dimensions can help you avoid aliasing or snowflaking, which are techniques to create separate copies or hierarchies of the same dimension for different roles. By using role-playing dimensions, you can simplify your dimensional model and reduce the number of tables and joins in your data warehouse.

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?