Further optimization might be included, specifically focusing on reducing the data pulled into Power BI, optimizing the queries, and making sure that the data model is efficient for connection to a large data warehouse via Power BI Desktop. To achieve that, here are some of the steps one should take:
Use Query Folding: Push data transformations back to the source database so that the server does the heavy lifting and does not fetch Power BI. Then, try to ensure that transformations in Power Query, such as filtering or aggregating, get pushed back to the warehouse database at any time. The best way to check whether a query is also folded is to use the option "View Native Query" within Power Query.
Efficient Data Model Design: Design data models for performance optimization. Design data models as star schema or snowflake schema rather than complex normalized design; these are more efficient with respect to analysis and minimize relationships that need to be defined. Further, do not use direct relationships for very large tables; use an aggregated or summarized table when appropriate.
Limit Amount of Data Loaded into Power BI: Overloading too much data into Power BI is amongst the most common performance issues. Filterers like this could be based on date and business-specific such that only a 'necessary' subset of data is loaded through a query. Parameters in Power Query can be used to acquire user-defined ranges of data to be loaded.
Incremental Refresh: A particular scenario for enabling incremental refresh in Power BI is large datasets. In this case, only the changes made to the data would be refreshed by Power BI, and hence, large datasets would not consume that time in loading. Offloading the full dataset becomes mandatory from time to time, especially concerning large warehouse solutions.
Cardinality and Granularity are reduced: High cardinality (the number of different values) slows the most Performance in Power BI. Aggregate or pre-summary aggregates on warehouse data or within Power Query where possible. Instead of getting all the raw transaction-level data directly into Power BI, aggregate it down to daily, weekly, or monthly totals, depending on what you need in the report.
Optimize DAX Calculation: In Power BI, DAX calculation hits a lot when there is a concern about performance. Try to sidestep very complex DAX measures that use a lot of filter criteria with CALCULATE or row-by-row intensive operations. Otherwise, pre-aggregate or filter much of that from the source or during data transformation in Power Query.
Use DirectQuery (when applicable): It is good to use DirectQuery mode for very large datasets; its beauty is that it allows querying directly into the data warehouse and returns results without bringing all this data to memory. You can avoid memory issues but at the cost of performance through round-trip queries to the database. Therefore, one should find a balance between DirectQuery and performance by writing efficient questions and taking into account any query optimizations on the database side.
Optimizing Indexes and Partitioning in Data Warehouse: Although Microsoft Power BI optimizes connection and data model, make sure the data warehouse is optimized for performance. This includes indexing key columns used in queries and partitioning very large tables to optimize query performance and decrease recharge time lags.
These two procedures combined are really effective ways to enhance Power BI's performance and usability when it connects to a big data warehouse, thus ensuring much faster query execution and report responsiveness.
