To identify frequently used columns loaded into memory from a Direct Lake semantic model, which methods can be used?

The Vertipaq Analyzer tool is specifically designed to work with the in-memory storage engine of Power BI and other Microsoft analysis services, enabling users to get detailed insights into how their semantic models are structured and utilized. This tool provides essential information about the schema, cardinality, and usage statistics for columns within a model, allowing users to identify which columns are frequently used and how they contribute to memory consumption.

When using the Vertipaq Analyzer, you benefit from its capability to visualize data compression and memory usage details, helping you optimize your model for performance. This makes it particularly valuable for identifying frequently used columns since it provides a clear overview of which elements are actively consuming resources in memory.

The other options, while useful in different contexts, do not directly provide insights into column usage frequency in the same way. For instance, the Analyze in Excel feature allows users to explore data visually in Excel but does not analyze in-memory behavior or utilization patterns directly. Querying the DISCOVER_MEMORYGRANT DMV focuses more on the memory grants for queries rather than column usage statistics. Lastly, querying the $System.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS DMV might give information regarding the column segments but lacks the broader analytical capabilities for identifying frequency of use like the Vertipaq Analyzer