When it comes to business intelligence, Power BI has become one of the most popular tools in the market. With its ability to analyze, visualize, and share data insights, Power BI is a game-changer for businesses of all sizes. One of the key features of Power BI is its ability to connect to various data sources, including Azure SQL Database. In this article, we will explore the Power Query M Language code for connecting to the Azure SQL Database data source from inside Power BI.
What is Power Query M Language?
Power Query is a data transformation and cleansing tool that is integrated into Power BI. It enables users to connect to different data sources and transform the data into the desired format for analysis and visualization. Power Query M Language is the formula language used in Power Query to create and manipulate data transformations. It is a functional language that allows users to perform complex data transformations using a simple syntax.
4. Select “SQL Server Authentication” and enter your credentials
5. Click on “Connect” to establish the connection
Once the connection is established, Power BI will load the tables from the database into the Navigator window. You can select the tables you want to import and click on “Load” to bring the data into Power BI.
Using Power Query M Language for Data Transformations
After connecting to the Azure SQL Database data source, you may need to perform data transformations to get the data into the desired format for analysis and visualization. Power Query M Language makes it easy to perform these transformations using a simple syntax.
Here is an example of Power Query M Language code for transforming data from a table in Azure SQL Database:
In this example, we are connecting to a table in Azure SQL Database and transforming two columns to the desired data types. The ““, ““, ““, “
“, and “” placeholders should be replaced with the appropriate values for your data source.
Conclusion
Power Query M Language is a powerful tool for connecting to and transforming data from different data sources, including Azure SQL Database. With its simple syntax and functional approach, users can easily perform complex data transformations to prepare data for analysis and visualization in Power BI. By following the steps outlined in this article, you can connect to your Azure SQL Database data source from inside Power BI and use Power Query M Language to perform the necessary data transformations.
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(In this blog post/tutorial, we will use a database called “Event Sales”, which you can download here. If you wish to follow along, you will need to download and UnZip the .BAK file, then use it to restore the EventSales database on an instance of SQL Server.) When connecting to a relational database from Power…
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