Azure Data Lake Storage Gen2 is a highly scalable and cost-effective data lake solution that enables organizations to store and analyze massive amounts of data. Power BI, on the other hand, is a powerful business intelligence tool that provides a wide range of data visualization and analysis capabilities. In this article, we will discuss how to use Power Query M Language code to connect to the Azure Data Lake Storage Gen2 data source from within Power BI.
Before we begin, there are a few prerequisites that you must have in place:
– An Azure subscription
– Access to an Azure Data Lake Storage Gen2 account
– Power BI Desktop installed on your machine
Follow the steps below to connect to the Azure Data Lake Storage Gen2 data source from within Power BI:
1. Open Power BI Desktop and click on the “Get Data” button located in the Home tab.
2. In the “Get Data” window, select “Azure” from the list of available data sources and click on “Azure Data Lake Storage Gen2“.
4. You will be prompted to enter your Azure credentials. Enter your Azure username and password and click on “Sign in”.
5. Once you have signed in, you will see a list of all the folders and files in your Azure Data Lake Storage Gen2 account. Select the folder containing the data that you want to analyze and click on “Edit”.
6. In the Power Query Editor window, click on the “Add Column” tab and select “Custom Column”.
7. In the “Custom Column” window, enter the following M Language code:
8. Click on “OK” to create the custom column. This will fetch the data from your Azure Data Lake Storage Gen2 account and convert it to a Power Query table.
9. You can now use the Power Query Editor to transform and shape the data as required. Once you are done, click on “Close & Apply” to load the data into Power BI.
In conclusion, connecting to the Azure Data Lake Storage Gen2 data source from within Power BI is a straightforward process that can be accomplished using Power Query M Language code. By following the steps outlined in this article, you can quickly and easily access and analyze massive amounts of data stored in your Azure Data Lake Storage Gen2 account.