This page provides you with instructions on how to extract data from Microsoft SQL Server and analyze it in Power BI. (If the mechanics of extracting data from Microsoft SQL Server seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Microsoft SQL Server?
Microsoft SQL Server is a relational database management system that supports applications on a single machine, on a local area network, or across the web. SQL Server supports Microsoft's .NET framework out of the box, and integrates nicely into the Microsoft ecosystem.
What is Power BI?
Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.
With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.
Getting data out of SQL Server
The most common way most folks who work with databases get their data is by using queries for extraction. With SELECT statements you can filter, sort, and limit the data you want to retrieve. If you need to export data in bulk, you can use Microsoft SQL Server Management Studio, which enables you to export entire tables and databases in formats like text, CSV, or SQL queries that can restore the database if run.
Loading data into Power BI
You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.
Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.
Analyzing data in Power BI
In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.
The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.
Once you've created a report, Power BI lets you share it with report "consumers" in your organization.
Keeping SQL Server data up to date
All set! You've written a script to move data from SQL Server into your data warehouse. But data freshness is one of the most important aspects of any analysis – what happens when you have new data that you need to add?
You could load the entire SQL Server database again. Doing this is almost guaranteed to be slow and painful, and cause all kinds of latency.
A better approach is to build your script to recognize new and updated records in the source database. Using an auto-incrementing field as a key is a great way to accomplish this. The key functions something like a bookmark, so your script can resume where it left off. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in SQL Server.
From Microsoft SQL Server to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Microsoft SQL Server data in Power BI is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Microsoft SQL Server to Redshift, Microsoft SQL Server to BigQuery, Microsoft SQL Server to Azure Synapse Analytics, Microsoft SQL Server to PostgreSQL, Microsoft SQL Server to Panoply, and Microsoft SQL Server to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Microsoft SQL Server with Power BI. With just a few clicks, Stitch starts extracting your Microsoft SQL Server data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Power BI.