Your Power BI model contains a SalesTransactions fact table with roughly 500 million imported rows at order-line granularity. Users only analyze revenue and units by month and product category and never need line-level details. Report visuals that reference this table render slowly and dataset refresh consumes excessive memory. You must improve both refresh and query performance while keeping all required business metrics. Which approach should you choose?
Load a summarized version of SalesTransactions that groups by calendar month and product category and returns pre-aggregated revenue, margin, and quantity, removing the detailed rows from the model.
Change the storage mode of SalesTransactions to DirectQuery so queries run directly against the source system.
Turn on the Auto date/time option so Power BI creates cached date hierarchies for each date column.
Enable incremental refresh on SalesTransactions and keep only the last two years of partitions.
Summarizing the fact data to the granularity that end users actually query-month and product category-slashes the row count and therefore the amount of data VertiPaq must load, scan, and compress. Because the detailed rows are no longer imported, refresh completes faster and visuals can answer queries from a much smaller in-memory table. Incremental refresh reduces how much data is refreshed but does not shrink the model or speed calculations. Switching to DirectQuery pushes work to the source but still scans the full detailed table at query time. Auto date/time creates additional hidden tables and therefore cannot improve performance in this scenario.
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