In addition to the techniques mentioned above, leveraging the Power Query Editor can also help optimize performance. Using techniques like data partitioning, data filtering, and removing unnecessary columns can reduce data size and improve query performance. It's also important to handle query dependencies properly by visualizing and understanding the query dependencies in the Query Dependencies View. Another tip is to avoid using calculated columns when possible, as they can impact performance. Instead, use measures or calculated tables to obtain the desired results. Lastly, consider using Power BI Premium or Power BI Paginated Reports for larger datasets and complex reports, as these offer enhanced performance capabilities.

One advanced technique for improving performance in Power BI reports is to optimize the data model. This can be done by creating relationships between tables, maintaining an efficient hierarchy, and using query folding to push as much processing as possible to the underlying data source. Additionally, you can use calculated tables and columns to pre-calculate measures and reduce the need for real-time calculations. Another approach is to optimize visuals by reducing the use of complex visuals, limiting the number of visuals in a report page, and using features like query reduction and aggregations to minimize the amount of data being loaded. Lastly, enabling and configuring features like data caching, incremental data refresh, and parallel loading can also significantly improve performance.

To optimize Power BI report performance, consider implementing data reduction techniques such as query folding, data summarization, and data compression. Query folding involves pushing query operations back to the data source and letting it perform the heavy lifting, thereby reducing the amount of data transferred to Power BI. Data summarization techniques like aggregations, which precalculate and store summarized data, can significantly speed up query performance. Data compression techniques, such as utilizing columnar storage and enabling compression algorithms, reduce the amount of disk space required and improve query response time. Additionally, closely monitor and tune the DAX formulas used in calculations. Evaluate measures and calculations that have a high impact on performance and consider optimizing them by rewriting or simplifying the expressions.