A date dimension is an essential table in a data model that allows us to analyze performance more effectively across different time periods. It should be included in every dimensional model that contains a date or requires date intelligence as part of the analysis.
A date dimension contains a continuous range of dates that cover the entire date period required for the analysis. It also includes columns that will allow a user to filter the data by almost any date logic. It can include the day of the week, workdays, weekends, quarters, months, years, or seasons.
Why is it Important in a Data Model?
A date dimension table will provide the ability to leverage the powerful date intelligence functions using languages like DAX or M (Power Query), but more importantly, the ability to do so correctly. The dates in the fact table or other model tables may not have a continuous range of dates, which can result in errors in date intelligence measure calculations.
Reporting solutions will usually require trend analysis over time or comparison to previous reporting periods. It is crucial to build the data model correctly to accurately report on the said measures.
Where can I Find a Date Dimension Table?
The date dimension might exist in your source data, but it is more likely that you will need to create one to satisfy the reporting requirements. A date dimension can be defined in multiple layers of report creation: the data source layer – in the source queries, the extract, transform, and load (ETL) layer – created with other report queries, and the analysis layer – in the report itself.
Creating a Date Dimension
There are three common methods, using different languages, for creating a date dimension for a data model:
M (Power Query)
There are advantages and disadvantages for each of the above methods, which will be reviewed in more detail in the sections below.
Power Query / M
A date dimension written using M in the Power Query Editor is created in the ETL layer of a reporting solution. Using the said method, the date dimension will be defined at the same point as many other dimensions in the data model.
There are many ways to create a date dimension in M, defining the date range with different techniques, including:
Explicit start and end dates
Power Query parameters
Dynamically based on information from other dimensions
The Power Query method keeps all the ETL logic in one place, ensuring the report is more easily interpreted and auditable. The data transformation will not split between the source, ETL, and analysis layers of the report.
Power Query provides the flexibility to create more complex calendar types, including fiscal periods or the 4-4-5 calendar.
The date dimension needs to be carefully developed to best practices for optimal performance. It is particularly important when it is dependent on start and end dates from other dimensions in the data model.
A date dimension written in DAX is created in the analysis layer directly within the report. The date dimension will recalculate a new date dimension upon report refresh after the queries of the report ETL layer are done processing. A date dimension can be created in a variety of ways in DAX, combining different functions to create and populate the dimension.
DAX comes with useful built-in functions that can be leveraged to build the date dimension quickly.
The date dimension written in DAX can be automatically created based on the range of dates in the data model.
A date dimension calculated in DAX does not benefit from the compression of the ETL layer. It can result in more calculations and computing run in the data model than necessary.
It can be confusing for other authors to understand and maintain the report when dimensions are created in different layers of the report.
A date dimension may already exist in an enterprise data warehouse or data mart. If the table is part of the source data, the dimension can be brought directly into the data model. If the dimension does not exist already, it is possible to build one in the source layer using a SQL script.
Using an existing date dimension in the source layer is a great option if it already exists and is reliably updated and maintained.
The SQL method takes advantage of the speed in processing in the data source server itself. It can potentially improve the performance of generating the date dimension.
The date dimension table may lack the flexibility of customization if it is brought into the data model directly from the source layer. Any necessary modifications may be dependent on an IT or DBA team, resulting in a loss of control of development time.
If a custom date dimension is required, SQL is characterized by limited date intelligence functions. It can be more difficult to create all the columns needed when compared to other languages.
It is important to include a robust date dimension in your model, as it will allow the report to take advantage of date intelligence functionality.
A date dimension can be built at different stages of the reporting process using different techniques.
A date dimension can be built at the ETL layer: this is useful for keeping the transformation logic in one place and leveraging the built-in date functions in the Power Query Editor.
A date dimension can be built in the analysis layer. It allows for the use of built-in DAX functionality, but it does not take advantage of the compression in the ETL layer and can be confusing.
A date dimension can be built at the data source layer. It can be efficient and performant but sometimes lacks flexibility and control of maintenance.
For most analysts, we recommend creating a date dimension using M in the Power Query Editor. Such a method will provide the flexibility to fully customize the date dimension to meet the requirements of the data model. The logic to build the date dimension will reside with the other dimensions, keeping the solution organized, streamlined, and auditable.
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