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It's also a good design practice to include a hierarchy that allows visuals to drill down to the version level. A role-playing dimension is a dimension that can filter related facts differently.
For example, at Adventure Works, the date dimension table has three relationships to the reseller sales facts. The same dimension table can be used to filter the facts by order date, ship date, or delivery date.
In a data warehouse, the accepted design approach is to define a single date dimension table. At query time, the "role" of the date dimension is established by which fact column you use to join the tables.
For example, when you analyze sales by order date, the table join relates to the reseller sales order date column.
In a Power BI model, this design can be imitated by creating multiple relationships between two tables. In the Adventure Works example, the date and reseller sales tables would have three relationships.
While this design is possible, it's important to understand that there can only be one active relationship between two Power BI model tables.
All remaining relationships must be set to inactive. Having a single active relationship means there is a default filter propagation from date to reseller sales.
In this instance, the active relationship is set to the most common filter that is used by reports, which at Adventure Works is the order date relationship.
In our example, the model developer must create measures to enable analysis of reseller sales by ship date and delivery date.
This work can be tedious, especially when the reseller table defines many measures. It also creates Fields pane clutter, with an overabundance of measures.
There are other limitations, too:. To overcome these limitations, a common Power BI modeling technique is to create a dimension-type table for each role-playing instance.
You typically create the additional dimension tables as calculated tables , using DAX. Using calculated tables, the model can contain a Date table, a Ship Date table and a Delivery Date table, each with a single and active relationship to their respective reseller sales table columns.
This design approach doesn't require you to define multiple measures for different date roles, and it allows simultaneous filtering by different date roles.
A minor price to pay, however, with this design approach is that there will be duplication of the date dimension table resulting in an increased model storage size.
As dimension-type tables typically store fewer rows relative to fact-type tables, it is rarely a concern.
Observe the following good design practices when you create model dimension-type tables for each role:. For more information, see Active vs inactive relationship guidance.
A junk dimension is useful when there are many dimensions, especially consisting of few attributes perhaps one , and when these attributes have few values.
Good candidates include order status columns, or customer demographic columns gender, age group, etc. The design objective of a junk dimension is to consolidate many "small" dimensions into a single dimension to both reduce the model storage size and also reduce Fields pane clutter by surfacing fewer model tables.
A junk dimension table is typically the Cartesian product of all dimension attribute members, with a surrogate key column.
The surrogate key provides a unique reference to each row in the table. You can build the dimension in a data warehouse, or by using Power Query to create a query that performs full outer query joins , then adds a surrogate key index column.
You load this query to the model as a dimension-type table. You also need to merge this query with the fact query, so the index column is loaded to the model to support the creation of a "one-to-many" model relationship.
A degenerate dimension refers to an attribute of the fact table that is required for filtering. At Adventure Works, the reseller sales order number is a good example.
In this case, it doesn't make good model design sense to create an independent table consisting of just this one column, because it would increase the model storage size and result in Fields pane clutter.
In the Power BI model, it can be appropriate to add the sales order number column to the fact-type table to allow filtering or grouping by sales order number.
It is an exception to the formerly introduced rule that you should not mix table types generally, model tables should be either dimension-type or fact-type.
However, if the Adventure Works resellers sales table has order number and order line number columns, and they're required for filtering, a degenerate dimension table would be a good design.
For more information, see One-to-one relationship guidance Degenerate dimensions. A factless fact table doesn't include any measure columns.
It contains only dimension keys. A factless fact table could store observations defined by dimension keys.
For example, at a particular date and time, a particular customer logged into your web site. You could define a measure to count the rows of the factless fact table to perform analysis of when and how many customers have logged in.
A more compelling use of a factless fact table is to store relationships between dimensions, and it's the Power BI model design approach we recommend defining many-to-many dimension relationships.
In a many-to-many dimension relationship design , the factless fact table is referred to as a bridging table. For example, consider that salespeople can be assigned to one or more sales regions.
The bridging table would be designed as a factless fact table consisting of two columns: salesperson key and region key.
Duplicate values can be stored in both columns.