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Dhanyawad 🙏

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File-options-setting-global- clear cache

In Power BI, you can optimize performance and speed by caching data through various methods. Here are some strategies you can use to cache or pre-calculate data in Power BI: DirectQuery vs. Import Mode: Power BI offers two main data storage modes: Import and DirectQuery. Import mode loads data into Power BI's internal data model, allowing for faster performance as data is cached in memory. DirectQuery mode queries data directly from the data source, which can be slower but ensures real-time data access. You can choose the appropriate mode based on your data size, refresh frequency, and performance requirements. Data Refresh Scheduling: Schedule regular data refreshes to ensure that your dataset stays up-to-date while also caching the data for faster access. Power BI allows you to set up automatic refresh schedules for datasets, ensuring that the data is refreshed at specified intervals. Incremental Data Refresh: Implement incremental data refresh to update only the new or changed data since the last refresh. This helps reduce data refresh time and optimizes performance, especially for large datasets. Data Aggregation: Pre-aggregate or summarize data where possible to reduce the volume of data loaded into memory. Aggregating data at a higher level of granularity can improve query performance and reduce memory usage. Optimize DAX Measures: Write efficient DAX measures that minimize the computational load on the data model. Avoid using complex or nested calculations that can slow down query performance. Use Calculated Columns Sparingly: Limit the use of calculated columns, especially if they involve heavy calculations or string manipulations. Calculated columns can increase the size of the data model and impact performance. Partitioning: Partition large tables into smaller segments based on date ranges or other criteria. Partitioning helps improve query performance by allowing Power BI to query only relevant segments of data. Optimize Data Model Relationships: Create optimized relationships between tables in your data model to improve query performance. Use appropriate cardinality and cross-filter direction settings based on your data relationships. Use Aggregations: Set up aggregations to store pre-calculated summary data at different levels of granularity. Aggregations help accelerate query performance, especially for large datasets. Data Compression: Ensure that your data is stored in a compressed format to reduce storage requirements and improve query performance. Power BI automatically compresses data where possible. By implementing these strategies, you can effectively cache data and optimize performance in Power BI, resulting in faster report rendering and improved user experience.

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First convert the non numeric values to null using conditional statement. Then sum the latest column which is recently made.

@@ananyabhattacharyya2362 not working

Didn't get your question

@@learnwidgiggs I have 5 feilds in my slice if my client select any two of them it have to give the difference of them

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@bangalore?

@@Engineering4675 hyderabad

From Power BI side, just revise all the videos from this channel...you are good to go.

You can share on my mail id. (mail id present in the about section of the channel)

No

Sorry I didn't see any message of yours. Maybe KZfaq would have deleted it on its own if founded it as spam.

😂