Aggregation iterators in Power BI DAX (e.g., SUMX, MAXX) operate by evaluating a custom expression for each row within the context and then applying specific set-based aggregation functions to those intermediate results; this mechanism enables precise control over calculations that require granular logic before summation or other statistical operations. The theoretical distinction between these iterators lies in their naming convention directly mapping to mathematical properties (minimum, maximum, average), which serves as a formal identifier for the operator's behavior within the domain of OLAP and multidimensional data modeling. This concept extends scalar aggregation functions by introducing iterative row-by-row evaluation capabilities essential for handling complex business rules that simple context transition cannot resolve.
Aggregation iterators in Power BI DAX (e.g., SUMX, MAXX) operate by evaluating a custom expression for each row within the context and then applying specific set-based aggregation functions to those …