Maximize performance when handling large data sets in Power BI Desktop with partial data loading. Here are some of the best ways to achieve this:
1. Filter data from Power Query
Applying filters in Power Query is the easiest way to load data partially. Let's take an example: This approach works really well with a date-type column filter. Filter the data in your source to include records that fall within predetermined dates, say the last 6 months. Your dataset becomes much smaller than what it originally was, and it does improve performance. In Power Query, move to the "Transform Data" part to apply these filters to limit the data.
2. Use Query Parameters for Dynamic Filtering
You can create dynamic parameters in Power Query that permit you to control which subset of data is loaded according to user input. For example, you can parameterize date ranges or regions. So, rather than having to edit the queries every time to control the loaded data, you can do so through parameters.
Go to "Manage Parameters" in Power Query.
Create parameters for the filtering criteria (e.g., date range or specific columns).
Use these parameters in your query to filter data dynamically when loading.
3. Incremental Data Loading
Incremental data loading can be enabled for bigger datasets that are refreshed periodically. Power BI allows you to load data in increments; that is, only new or changed data will be loaded, thus reducing the load time and improving performance. Incremental refresh can be enabled on Power BI Desktop in early access under the "Modeling" tab.
Define a date or timestamp column for incremental loading. Set up a refresh policy so that only the most recent data gets loaded.
Make use of DirectQuery Mode.
When your data source allows, you can shift to DirectQuery instead of loading data to Power BI. In this mode, your data is not stored in Power BI but is directly queried from your source. This feature is a relief amidst problems associated with large datasets. However, it comes with limitations, such as the type of calculation and transformations you can execute.
5. Data Load Diagnostics
Power BI comes with built-in diagnostics to help you discover specifically where the performance bottlenecks exist while the data is loading. You can run the Performance Analyzer tool in Power BI Desktop to troubleshoot slow visuals and queries. The tool will help you understand the queries that are taking ages to load so you can optimize them.
To utilize the Performance Analyzer:
Go to the "View" tab on Power BI Desktop.
Click "Performance Analyzer" and start recording.
Check results for slow queries or visuals that may impact performance.
6. People should be Removing Unwanted Columns
Only load empty columns that are crucial to your report. Irrelevant columns can be removed, thereby reducing the data size and improving the loading time. You can remove columns in Power Query before loading the data to Power BI.
7. Aggregating Data
However, it can also be the case that very detailed data will not be needed in some analyses. Aggregation can be done in Power Query, and then this data is loaded into Power BI. That will reduce the volume of data, which means improved performance after aggregating data: taking transactional data to a more monthly or yearly level, for example.
When combined in several of these ways, the data loading will be optimized to a more performance-maximized condition of your Power BI report. Make it a habit to always check and adjust your data load settings based on the size and complexity of the dataset.
