Describe semantic model optimization techniques
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 poorly designed semantic model, inefficient Data Analysis Expressions (DAX) calculations, or a mix of the two. The process of designing a semantic model for performance can be tedious, and it's often underestimated.
However, if you address performance issues during development, you'll have a robust semantic model that will deliver better reporting performance and an overall more positive user experience. Ultimately, you'll also be able to maintain optimized performance. As your organization grows, the size of its data grows, and its semantic models 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 sized semantic model uses less resources (memory) and achieves faster data refresh, calculations, and rendering of visuals in reports. Therefore, the performance optimization process involves minimizing the size of the semantic model and making the most efficient use of the data in the model. Your design decisions should:
- Ensure that the correct data types are used.
- Remove unnecessary columns and rows.
- Avoid repeated values.
- Surface numeric columns as measures.
- Reduce column cardinality.
- Analyze model metadata.
- Summarize data where possible.
For example, consider that you work as a Power BI developer for Tailwind Traders. You have been tasked to review a semantic model that was created a few years ago by another developer, a person who has since left the organization.
The semantic model produces a report that has received negative feedback from users. The users are happy with the results that they see in the report, but they aren't satisfied with the report performance. Loading the pages in the report takes too long, and tables aren't updating quickly enough when new filters are applied. In addition to this feedback, the IT team has highlighted that the file size of this particular semantic model is too large, and that it's putting a strain on the capacity resources.
You need to review the semantic model in order to identify the root cause(s) of the performance issues and make changes to optimize performance.