The answer is Power BI User-defined Aggregations. The trick is configuring your Measures to take advantage of aggregation tables in your model, without the need to write lots of DAX IF() statements into each measure. The new aggregate table can get used by your calculations to produce correct results by scanning a table with significantly fewer rows, and therefore faster query speed compared with a measure scanning the original larger FACT table. It makes sense to create an additional table in your model summarizing the sales data in the FACT table by month, with pre-aggregated data. You have several visuals in a report that show aggregated values summarised by month. What does “automatically make use of” mean in this context?Ĭonsider a FACT table containing millions of rows of sales data in detail in your model. This feature is available in both Pro and Premium. One of the most powerful features of Power BI data modelling today is creating aggregation tables in your dataset and having simple calculations automatically make use of the tables without writing complex DAX. Links to each article will appear once I have written the article. MIN, MAX, AVG and aggregations other than SUM.This article will be the first in a series covering the following topics explaining some of the different ways to use Power BI User-defined Aggregations: The Power BI User-defined Aggregation feature is not new nor to be confused with the recently announced Automatic Aggregations, which while using the same fundamental – is covered in a later article. ![]() If you have not used Power BI User-defined Aggregations before, need a refresher or reminder, or are just keen to understand how to configure, this article is for you. ![]() It includes a walkthrough example ( with video) showing how to configure and test a simple model to use this feature. ![]() This article introduces the Power BI User-defined Aggregations feature.
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