Using the SELECTEDVALUE function in DAX
This article describes how the SELECTEDVALUE DAX function simplifies the syntax required in many scenarios where you need to read a single value selected in the filter context.
This article describes how the SELECTEDVALUE DAX function simplifies the syntax required in many scenarios where you need to read a single value selected in the filter context.
This article describes how table expansion and filter context propagation are important DAX concepts to understand and fix small glitches in DAX expressions.
This article describes how to create a virtual relationship in DAX using the TREATAS function, which is more efficient than approaches based on INTERSECT or FILTER.
A filter argument in CALCULATE is always an iterator. Finding the right granularity for it is important to control the result and the performance. This article describes the options available to create complex filters in DAX.
In Power BI Desktop (as of February 2016) you have to use DAX to apply calculations over dates (such as year-to-date, year-over-year, and others), but you do not have the Mark as Date Table feature. This article describes which scenarios are impacted and the possible workarounds.
The context transition in DAX is the transformation of row contexts into an equivalent filter context performed by CALCULATE and CALCULATETABLE. Managing this behavior is the next step in learning DAX once you understand row context and filter context. This article provides the basics of context transition.
Understanding the difference between row context and filter context is important to use DAX correctly. This article introduces you to these two concepts.
SUMMARIZE is a function that looks quite simple, but its functionality hides some secrets that might surprise even seasoned DAX coders. In this article, we analyze the behavior of SUMMARIZE, in order to completely describe its semantic. The final advice might surprise you: we will suggest to avoid the use
The autoexist feature used by MDX queries sent to an Analysis Services or Power Pivot data models can have different behaviors depending on the normalization level. This article describes these differences and provides best practices to avoid unwanted results.