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Recent posts by Star Power. How it works. Add a payment method. To support accurate historic analysis of sales by salesperson, the dimension table must store versions of salespeople and their associated region s.
The table should also include start and end date values to define the time validity. The table must also define a surrogate key because the business key in this instance, employee ID won't be unique.
It's important to understand that when the source data doesn't store versions, you must use an intermediate system like a data warehouse to detect and store changes.
The table load process must preserve existing data and detect changes. When a change is detected, the table load process must expire the current version.
It records these changes by updating the EndDate value and inserting a new version with the StartDate value commencing from the previous EndDate value.
Also, related facts must use a time-based lookup to retrieve the dimension key value relevant to the fact date. It can, however, load data from a pre-loaded SCD Type 2 dimension table.
The Power BI model should support querying historical data for a member, regardless of change, and for a version of the member, which represents a particular state of the member in time.
In the context of Adventure Works, this design enables you to query the salesperson regardless of assigned sales region, or for a particular version of the salesperson.
To achieve this requirement, the Power BI model dimension-type table must include a column for filtering the salesperson, and a different column for filtering a specific version of the salesperson.
It's also important to educate report authors and consumers about the basics of SCD Type 2, and how to achieve appropriate report designs by applying correct filters.
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.
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