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Incremental refresh and real-time data for semantic models in Power BI provide efficient ways to handle dynamic data and improve model refresh performance. By automating partition creation and management, incremental refresh reduces the amount of data that needs to be refreshed and allows for the inclusion of real-time data. Incremental refresh extends scheduled refresh operations by providing automated partition creation and management for semantic model tables that frequently load new and updated data. For most models, one or more tables contain transaction data that changes often and can grow exponentially, like a fact table in a relational or star database schema. An incremental refresh policy to partition the table, refreshing only the most recent import partitions, and optionally using another DirectQuery partition for real-time data can significantly reduce the amount of data that has to be refreshed. At the same time, this policy ensures that the latest changes at the data so...

Step 1: Load data to Power BI Desktop Open Power BI desktop   Click on use sample data to load sample data to Power BI desktop Click on load sample data to load data to model. Sample data looks like below Step 2 : Create measure table Home à click on enter data under data section Create table as Measure Click on load Have created below 4 measures Profit = SUM ( financials[Profit] ) Sales = SUM ( financials[Sales] ) Units Sold = SUM ( financials[Units Sold] ) Gross Sales = SUM ( financials[Gross Sales] ) Step 3: Create new table for display format Go to modeling tab Click on new table under calculations section and provide below query to new table DisplayFormat = DATATABLE (     "FormatOptions" , STRING ,     "Sort" , INTEGER ,     {         { "Millions" , 1 },         { "Thousands" , 2 },         { "Original Values" , 3 } ...