Reza Rad dives into aggregations in Power BI. Part one introduces the topic:
Imagine a Fact table with 250 millions of rows. Such a fact table is big enough to be considered as a good candidate for DirectQuery connection. You don’t want to load such a big table into the memory, and most probably, the Power BI file size exceeds the 1GB limitation. Now, think about your reporting solution for a second. Do you always query this fact table at the finest or minimum granular level? I mean do you always look at every single transaction in this table when you do report on it?
The answer is No. In most of the times, you are querying the data by other fields or columns. As an example; you query the Sales value in the fact table, by Year. Some other times, you query the fact table’s values by Customer’s education category. Some other times, you query the values in the fact table, by each product. When you look at real-world scenarios, most of the time, you are querying the fact table by aggregations of dimension tables.
Aggregation tables are the fast performing solution for huge DirectQuery tables in Power BI. In the previous blog post, I explained what is an aggregation, and why it is an important part of a Power BI implementation. Aggregations are part of the Composite model in the Power BI. For the aggregation set up, your first step is to create an aggregated table. In this blog post, I’ll explain how that step can be done. If you want to learn more about Power BI, read the Power BI book, from Rookie to Rock Star.
There’s a lot of detail packed into the first posts in this series, so it looks like a good one to watch.