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In Power BI Desktop, you can choose to mimic a snowflake dimension design perhaps because your source data does or integrate denormalize the source tables into a single model table.
Generally, the benefits of a single model table outweigh the benefits of multiple model tables. The most optimal decision can depend on the volumes of data and the usability requirements for the model.
When you choose to integrate into a single model table, you can also define a hierarchy that encompasses the highest and lowest grain of the dimension.
Possibly, the storage of redundant denormalized data can result in increased model storage size, particularly for very large dimension tables. A slowly changing dimension SCD is one that appropriately manages change of dimension members over time.
It applies when business entity values change over time, and in an ad hoc manner. A good example of a slowly changing dimension is a customer dimension, specifically its contact detail columns like email address and phone number.
In contrast, some dimensions are considered to be rapidly changing when a dimension attribute changes often, like a stock's market price.
The common design approach in these instances is to store rapidly changing attribute values in a fact table measure.
A dimension-type table could be Type 1 or Type 2, or support both types simultaneously for different columns. A Type 1 SCD always reflects the latest values, and when changes in source data are detected, the dimension table data is overwritten.
This design approach is common for columns that store supplementary values, like the email address or phone number of a customer. When a customer email address or phone number changes, the dimension table updates the customer row with the new values.
It's as if the customer always had this contact information. It refreshes the table data to ensure the latest values are loaded. A Type 2 SCD supports versioning of dimension members.
If the source system doesn't store versions, then it's usually the data warehouse load process that detects changes, and appropriately manages the change in a dimension table.
In this case, the dimension table must use a surrogate key to provide a unique reference to a version of the dimension member.
It also includes columns that define the date range validity of the version for example, StartDate and EndDate and possibly a flag column for example, IsCurrent to easily filter by current dimension members.
For example, Adventure Works assigns salespeople to a sales region. When a salesperson relocates region, a new version of the salesperson must be created to ensure that historical facts remain associated with the former region.
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. I have always been a big fan of sprite movies, and this one is no exception.
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