Compatible Data Sources to Use With DirectQuery


DirectQuery is a BI data-storing technique that allows users to access and evaluate data within a database. This is different from other BI querying techniques, which work by importing data into an in-memory engine or data warehouse. Using DirectQuery means you can connect your analytics to an external data source. These data sources allow you to issue queries against it and receive instant results.

However, not all data sources are compatible with DirectQuery. For example, Microsoft SQL Server is a DirectQuery data source, while Microsoft Excel is not. Data sources must have a relational database model or modeling engine to support DirectQuery mode.

Supported Data Sources

Fortunately, there are a range of compatible data sources you can use. These include relational databases like Microsoft SQL Server, Oracle Database, and Azure SQL Database. Each of these sources has a structured data format, and they all have built-in query-processing features.

Moreover, you can use cloud data warehouses to connect to DirectQuery. Platforms like Snowflake and Google BigQuery can handle massive datasets. They also offer support for DirectQuery connections.

Multidimensional databases are another way to go. You can store and retrieve analytics using SAP HANA and IBM DB2 OLAP. This is a perfect process for DirectQuery.

You can also access existing OLAP cubes via DirectQuery. These cubes are typically built on Microsoft Analysis Services or SSAS Tabular. The process of accessing cubes through DirectQuery allows for faster data analysis.

There are other data sources you can use as well. These include Power BI, which extends compatibility to include more cloud services. You could also choose to use Dynamics CRM or Salesforce.

Advantages of DirectQuery

DirectQuery is a Power BI desktop option and offers several advantages when storing data. For instance, you can query the data directly from the source on demand. Doing this allows you to get real-time insights to generate reports based on the latest information.

Since you do not need to import data, DirectQuery reduces your storage footprint. You do not need a lot of storage space on the Power BI server. This frees up resources and saves you money on related costs.

Furthermore, your reports automatically update whenever you make data changes to the underlying data source. So, you do not have to create manual updates, decreasing the risk of error.

You can query specific data subsets directly from the data source if you work with large datasets. It goes faster than trying to process an entire imported dataset. Also, DirectQuery is exceptionally scalable. It grows with your growing data and will not impact BI server performance.

You can do your pre-aggregation on the server when using Power BI and visualize large data sets. You also do not have the 1 GB data set limitation.

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Consider Limitations

However, even though there is no 1 GB limit, you must consider other limitations. For instance, if you consistently run complex queries or large datasets, you may put too much strain on the data source. This may lead to slow report rendering. Also, if you do not have access to a stable network connection, you may struggle to access reports. PSA Software users can leverage DirectQuery for real-time data access and up-to-date reporting. An unstable network connection will slow down performance and make it nearly impossible to generate the data you need. Because DirectQuery reports require an internet connection before accessing the data source, you can do any analysis offline.

Keep in mind that user credentials may reach the data source, so it is crucial to have security measures in place to prevent a breach or unauthorized access.

How to Choose the Right Data Source When Using DirectQuery

If you want to use DirectQuery, choose the appropriate data source. When deciding on a data source, you must consider the following factors.

If you work with large datasets, you should use cloud data warehouses or multidimensional databases. These options will allow you to handle the data more efficiently. If your data requires frequent updates, you must choose a data source that allows you to run these updates without hiccups. Again, there may be performance issues here if the workload is big and the network connection is unstable. DirectQuery’s ability to handle massive datasets can contribute to custom software success by enabling real-time analytics on even the largest data volumes.

Look for a data source that allows offline analysis. You may need to import the data for better success if you cannot find an appropriate data source for offline work. Consider the impact of DirectQuery on the time it takes to render reports. The speed of report generation will depend on data volume and query complexity.

Using Compatible Data Sources for DirectQuery Success

DirectQuery is a valuable BI tool. However, it is crucial to understand all the related data sources and their benefits and limits so you can continue to make the right decisions to optimize your workflow. It will also allow you to get the most out of your data. Also, read through all related documentation before using data sources to make sure they are truly compatible, and use all the online resources you can find before settling on a specific data source. DirectQuery offers real-time visibility as it retrieves data directly from the source whenever a user interacts with the report.

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