Performance optimization, also known as performance tuning, involves making changes to the current state of the semantic model so that it runs more efficiently. Essentially, when your semantic model is optimized, it performs better.
You might find that your report runs well in test and development environments, but when deployed to production for broader consumption, performance issues arise. From a report user's perspective, poor performance is characterized by report pages that take longer to load and visuals taking more time to update. This poor performance results in a negative user experience.
As a data analyst, you will spend approximately 90 percent of your time working with your data, and nine times out of ten, poor performance is a direct result of a bad semantic model, bad Data Analysis Expressions (DAX), or the mix of the two. The process of designing a semantic model for performance can be tedious, and it is often underestimated. However, if you address performance issues during development, you will have a robust Power BI semantic model that will return better reporting performance and a more positive user experience. Ultimately, you will also be able to maintain optimized performance. As your organization grows, the size of its data grows, and its semantic model becomes more complex. By optimizing your semantic model early, you can mitigate the negative impact that this growth might have on the performance of your semantic model.
A smaller semantic model requires fewer resources (memory) and allows for faster data refreshes, calculations, and visual rendering in reports. Thus, performance optimization involves reducing the size of the semantic model and efficiently utilizing the data within it by:
Using appropriate data types.
Removing unnecessary columns and rows.
Avoiding redundant values.
Replacing numeric columns with measures.
Lowering cardinalities.
Examining model metadata.
Summarizing data whenever possible.
For instance, suppose you work as a Microsoft Power BI developer for Tailwind Traders. You have been assigned to review a semantic model created several years ago by a developer who has since left the company.
The report generated by this semantic model has received negative feedback from users. While they are pleased with the report's content, they are dissatisfied with its performance. The report pages are slow to load, and tables do not refresh quickly enough when specific selections are made. Additionally, the IT team has noted that the file size of this semantic model is excessively large, straining the organization's resources.
SWETA SARANGI
12-06-2024