Take a Deep Dive into Filtering in DAX | Towards Data Science

Towards Data Science

by Salvatore Cagliari

February 26, 2026

AI-Generated Deep Dive Summary

Deep diving into the inner workings of filtering in DAX reveals how these operations impact query execution and performance. The article explores the differences between using simple filters, such as restricting results to a specific product color, and more complex functions like FILTER(), which can lead to unnecessary overhead. By analyzing server execution plans, it becomes clear that leveraging the Storage Engine (SE) for as much processing as possible is key to optimizing query speed and efficiency. The article highlights that while adding simple filters, such as restricting data by product color, results in a straightforward WHERE clause in SQL, which is efficient and fast. However, using functions like FILTER() can sometimes lead to suboptimal execution plans, unnecessarily involving the Formula Engine (FE), which operates more slowly due to its single-threaded nature. This demonstrates the importance of writing DAX code that avoids unnecessary complexity. When multiple filters are applied, the article shows how the SE still efficiently handles the query, often without significantly impacting performance. However, overloading a query with overly complex logic or nested functions can cause tasks to shift toward the FE, which is less efficient. The key takeaway is that using functions like CALCULATE and CALCULATETABLE in DAX can help maintain optimal performance. For readers interested in AI and data science, understanding these nuances matters because optimizing DAX queries directly impacts the efficiency of data models used in machine learning workflows. Better-performing data models enable faster insights and more efficient integration with AI systems, making this knowledge essential for anyone working at the intersection of analytics and artificial intelligence.

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Originally published on Towards Data Science on 2/26/2026