Topic: Tabular
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In DAX there are different ways to test whether a table is empty. This test can be used in complex DAX expressions and this short article briefly discuss what are the suggested approaches from a performance perspective. Read more
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A common best practice is to use CALCULATETABLE instead of FILTER for performance reasons. This article explores the reasons why and explains when FILTER might be better than CALCULATETABLE. Read more
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With DAX you can calculate the sales of the first, second and third year of a new customer without any ETL. In this article you see how to implement this calculation with good performance. Read more
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Grouping transactions by customers’ age requires a computation made row by row at transaction level. DAX offers an elegant solution using calculated columns, which is described in this article. Read more
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PowerPivot and Analysis Services 2012 Tabular do not support many-to-many (M2M) relationships directly in the data model. However, you can obtain the desired result from a many-to-many relationship by writing a DAX expression. For example, consider the classic M2M relationship… Read more
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DAX is the new language used by PowerPivot and Analysis Services in Tabular mode and it resembles the syntax of Excel formula and it can be considered a functional language. You do not have iterative statements, but you can run… Read more
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VertiPaq is the internal column-based database engine used by PowerPivot and BISM Tabular models. High cardinality columns might be the more expensive parts of a table. If you cannot remove a high cardinality column from a table, by using the… Read more
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This article shows how to use the USERELATIONSHIP function in DAX to change the active relationship in a CALCULATE function. Read more
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If you want to execute a DAX query from your code to a Tabular model, you might think that an OLE DB connection should be the right way to get your data. It works, but if you your code is… Read more
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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. Read more