With the rollout of Aggregated Tables in PowerBI, we gained some much-anticipated functionality to improve performance large datasets. The problem I’ve run into is that aggregation is very subjective and can still result in datasets that are still too large for publishing to PowerBI cloud. Additionally, it can take a frustrating amount of time for those aggregate tables to import into a developer’s local instance of PowerBI Desktop. In this article, I’d like to show you how you can leverage PowerBI's Aggregation, functionality, Direct Query and a simple calculation group to partition an aggregated table by Current (import) and Archive (Direct Query) periods. This is one way to work with aggregated tables within the constraints of your licensed capacity and budget. There are definitely caveots to this approach that I will cover along the way.
When it comes to dealing with DirectQuery, most of us think that columns are not allowed. But this is not true. We can directly create columns to let us explore more. Can we use date table create in Power BI for getting time intelligence?
Power BI supports Direct Query and, when the datasets are huge, the user prefers to go for the direct query. Not all is functionality available in Direct Query Mode. In the last article, we have discussed how time intelligence works well with Direct Query. In this article, we will explore more complex measures.
The time Intelligence in Power BI makes it easy to calculate Year till date (YTD), Quarter till date (QTD) and Month till date (MTD) and to compare them to the same period last year and last period. How will this work in a Direct query environment?
Let us try to see the same in a Direct Query SQL Server Environment.