Your Power BI model contains a fact table with 1.2 billion transaction rows stored in DirectQuery mode against Azure Synapse. Most report visuals display sales amounts by month and product category, and users complain that visuals take several seconds to render. You must improve query performance without significantly increasing the model's memory footprint or removing the ability to drill through to the detailed rows. What should you do?
Create an Import-mode table that summarizes the fact data by month and product category, mark it as an aggregation table, and keep the original fact table in DirectQuery mode.
Enable the Auto date/time option so Power BI automatically caches monthly totals for the current visuals.
Add a row-level security role that restricts the fact table to the last 24 months of data for all users.
Change the storage mode of the existing fact table from DirectQuery to Import so that all data is cached in memory.
Creating an Import-mode aggregation table summarized at month and product-category granularity lets Power BI answer most visuals from a highly compressed in-memory table, eliminating expensive DirectQuery calls. Because the detail fact table remains in DirectQuery, drillthrough to individual transactions is still possible. Importing the entire fact table would greatly increase model size. Auto date/time affects only date hierarchies and does not create aggregated caches. A row-level security filter may reduce rows returned to specific users but does not create a reusable aggregated cache for all visuals and could remove required history.
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