Category: DAX

Marco Russo and Alberto Ferrari looks at calculation groups:

Calculation groups are often used to display options in a report to change the calculation of existing measures by selecting items on a slicer. However, only a single calculation item can be executed for a measure reference, which could make the semantic model harder to use when the user selects two or more items in a calculation group.

Two new calculation group properties, multipleOrEmptySelectionExpression and noSelectionExpression, provide a way to control the calculation in these conditions that, so far, ignored the presence of the calculation group, thus executing the measures without applying any transformation. This article shows how to use these features and provides guidance on using the feature in preview: despite not having a user interface to manage these new properties, the TMDL view in Power BI Desktop and external tools like Tabular Editor already allow you to create and publish a semantic model that uses these new properties.

Read on to see how these properties work.

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Koen Verbeeck has a few more mistakes we shouldn’t make:

What advice would you give to someone new to Power BI? What are common Power BI mistakes to avoid? In this article, we will discuss several Power BI mistakes to avoid to help you navigate this powerful tool effectively.

Click through for thoughts on DAX, governance, security, and more.

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Marco Russo and Alberto Ferrari get us to 80%:

The Pareto analysis is an analytical technique used to identify the most impactful elements within a dataset, based on the principle that a small proportion of causes often leads to a large proportion of effects. For example, the ABC Classification in DAX Patterns is also based on the Pareto principle. However, typical implementations often face limitations. The analysis based on the Pareto principle commonly uses categorical axes, such as customer names or identifiers, making it impossible to leverage a continuous axis on a Power BI line chart. A categorical axis creates a scrollbar on the line chart when there are too many data points, limiting the ability to compare the distribution of data points in different categories within the same line chart.

This article shows how to overcome this limitation by introducing a numeric axis reflecting each item’s position based on the selected measure.

Read on to see how it all works.

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Chris Webb does the math:

The series of blog posts I wrote last year on semantic model memory usage, in particular this post on the query memory limit and the “This query uses more memory than the configured limit” error in Power BI, gets a lot of traffic. Since writing that post on the query memory limit I’ve written a few follow-ups on common mistakes that lead to increased query memory usage, such as this one on measures that never return a blank. Today’s post is sort of in that series but it isn’t about a design mistake – it’s just to point out that distinct count measures can be surprisingly memory-hungry.

Read on for Chris’s findings and the explanation, as well as a couple of potential workarounds if you find yourself in this situation.

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Marco Russo and Alberto Ferrari show that tracking weekdays is like an onion:

Computing time intelligence calculations in DAX is rather simple. However, as soon as the requirements are not trivial, the complexity of formulas skyrockets, and it is necessary to have a very good understanding of several details about DAX to obtain a good formula. In this article, we show a simple requirement: the need to maintain a filter on weekdays while computing time intelligence. As you are about to read, it will require several complex steps despite being a simple requirement; but let us start by clarifying what we want to obtain and what a filter-preserving column is.

Click through for the full article.

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