Power BI Desktop is a powerful tool for data analysis and visualization, but its performance can be affected by various factors, including the volume of data you're working with and the hardware specifications of your computer. Let's break down some of the factors that might be causing your issues:
Data Volume: Handling 30 million rows for YTD sales and 3.5 million rows for one month of cost is quite substantial, but Power BI should be able to handle it. However, performance can vary depending on the complexity of your data model and the operations you're performing.
Hardware: Your Lenovo T14 with an Intel Core i7 processor and 32GB of RAM is a decent machine for Power BI. However, if your data model and queries are very complex, you might still run into performance issues. Make sure you're not running other memory-intensive applications simultaneously.
Data Model: The efficiency of your data model design can significantly impact performance. Ensure that you have optimized relationships, calculated columns, and measures. Avoid using too many calculated columns as they can slow down performance.
Query Optimization: Optimize your data import queries. If you're merging tables or applying complex transformations, these operations can be resource-intensive. Use Power Query Editor to review and optimize your query steps.
Visualizations: Complex visuals with many data points can also slow down performance. Try to limit the number of visuals on your report page and avoid overloading them with data.
Data Types and Formatting: It's good that you're formatting your CSV files correctly upon import. Make sure that you're using appropriate data types for your columns to improve performance.
Indexing: Ensure that your data sources have proper indexing, especially if you are frequently filtering or sorting large datasets.
Performance Analyzer: Use Power BI's built-in Performance Analyzer to identify bottlenecks in your report. It can help you pinpoint which visuals or queries are causing the slowdown.
Data Refresh: If you're working with large datasets, consider configuring incremental data refresh to load only the new or changed data. This can significantly improve refresh times.
Check for Errors: Check the Query Diagnostics and Error messages to see if there are any specific issues reported that might be causing the long processing times.
If you've addressed these factors and are still experiencing performance issues, you might want to consider splitting your data into smaller chunks or aggregating it before loading into Power BI, depending on your specific analysis requirements.
Remember that performance optimization often involves a combination of factors, and it's a matter of fine-tuning your report and data model to achieve the best performance for your specific use case.
