What Is DirectQuery Mode in Power BI?
Last Updated on November 22, 2023
Power BI’s ability to connect disparate data sets, transform and clean the data into a data model, and create visually appealing dashboards and reports from an organization’s data has made it an incredibly popular business tool. However, Power BI supports specific methods for connecting data, sometimes making it difficult for users to decide if it is right for them.
DirectQuery is one of three Power BI dataset modes available, the other two being Import and Composite.
DirectQuery Mode Explained
The DirectQuery mode in Power BI is an alternative to Import mode that doesn’t require data to be imported. This mode is useful in connecting to data sources and displaying data in Power BI that has been queried. The queries are used to retrieve data from underlying data sources.
DirectQuery mode is useful when users are working with large data sets that are too big to import into Power BI, or when you need to display real-time data. You can also use this mode when your dataset is beyond what can be achieved within scheduled refresh limits.
Whenever you connect your data source to Power BI using the DirectQuery mode, it sends queries to the data source to retrieve the data that you want to display. When using this mode, Power BI can retrieve your data from an on-premises SQL Server database, a cloud-based database, or another data source that supports DirectQuery.
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Benefits of Using Direct Query Mode
There are several benefits of using DirectQuery mode above other modes in Power BI. These include:
1. Real-Time Data
One of the biggest benefits of the DirectQuery mode is that it provides real-time information. Because the data is not imported, changes to the data source are immediately reflected on your report or workspace in Power BI.
2. Security
If you have sensitive or confidential data, then DirectQuery is the best mode to use. This is because the data is not stored in Power BI when you use the DirectQuery mode. This helps ensure that sensitive data remains secure and confidential.
3. Less Data Storage Needed
Because the data is not imported into Power BI, DirectQuery mode reduces the amount of data stored on the interface. It also reduces the cost and complexity of storing data in Power BI. Not storing data on Power BI can also improve the performance of the software.
4. Better Performance for Large Data Sets
If you are working with a large data set, using the DirectQuery mode can improve performance because it doesn’t require data to be loaded into Power BI. This means that reports and visualizations can be created and displayed faster. You can also use Count Distinct in Power BI for faster computations.
5. Ensure Up-to-Date Data is Used
Because the data is always queried from the source, the latest data is always available in Power BI. If the data was stored in Power BI, you would need to update manually or reimport the up-to-date file.
6. Reduced Data Duplication
DirectQuery mode helps to reduce data duplication because the data is not imported into Power BI. This can help to improve the accuracy of your data and reduce the risk of errors.
Limitations of DirectQuery Mode
While there are certainly benefits of the DirectQuery mode over the other two, it is not without its limitations. One of the major drawbacks of this mode is that it may be slower than other modes when importing data because each query to the data source can take longer to complete. As a result, DirectQuery can result in slower report performance and longer wait times for users.
Another major drawback of this mode is that calculated tables and quick insight features aren’t supported.
In addition, DAX formulas are limited to functions that can be transposed to native queries understood by the data source. The same limitation applies to Power Query/Mashup expressions.
In Summary
DirectQuery mode in Power BI is a useful way to connect to and display data from data sources, and it can be a good choice for real-time data and large data sets. In deciding whether this is the best mode to use in Power BI, you should consider factors such as the size of your data, the frequency of changes to the data, and the performance and security requirements of your data.
