Import Azure Data Lake Storage Data into the Power BI Service for Visualizations

Power BI transforms your company's data into rich visuals for you to collect and organize so you can focus on what matters to you. When paired with CData Connect AI, you get access to Azure Data Lake Storage data for visualizations, dashboards, and more. This article shows how to connect to Azure Data Lake Storage in CData Connect AI, publish a dataset from Power BI Desktop to the Power BI Service and then create reports on Azure Data Lake Storage data in the Power BI service.

Configure Azure Data Lake Storage Connectivity for Power BI Service

Connectivity to Azure Data Lake Storage from Power BI Service is made possible through CData Connect AI. To work with Azure Data Lake Storage data from Power BI Service, we start by creating and configuring a Azure Data Lake Storage connection.

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a Connection

2. Select "Azure Data Lake Storage" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Azure Data Lake Storage.

   Authenticating to a Gen 1 DataLakeStore Account

   Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

   For this, an Active Directory web application is required. You can create one as follows:

   1. Sign in to your Azure Account through the
   2. Select "Entra ID" (formerly Azure AD).
   3. Select "App registrations".
   4. Select "New application registration".
   5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
   6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
   7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

   To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen1.
   • Account: Set this to the name of the account.
   • OAuthClientId: Set this to the application Id of the app you created.
   • OAuthClientSecret: Set this to the key generated for the app you created.
   • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

   Authenticating to a Gen 2 DataLakeStore Account

   To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen2.
   • Account: Set this to the name of the account.
   • FileSystem: Set this to the file system which will be used for this account.
   • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Azure Data Lake Storage Connection page and update the User-based permissions. 👁 Updating permissions
   

With the connection configured, you are ready to connect to Azure Data Lake Storage data from the Power BI service.

Publish a Dataset from Power BI Desktop

Once you connect to Azure Data Lake Storage in Connect AI, you can create a dataset in Power BI desktop and publish the dataset to the Power BI service.

1. Open Power BI Desktop and click Get Data -> Online Services -> CData Connect AI and click "Connect"
2. Click "Sign in" and authenticate with your CData Connect AI account 👁 Authenticating with Connect AI
   
3. After signing in, click "Connect" 👁 Connecting to Connect AI
   
4. Select tables in the Navigator dialog 👁 The available tables. (Salesforce tables are shown)
   
5. Click Load to establish the connection to your Azure Data Lake Storage data from Power BI
6. Define any relationships between the selected entities on the Relationships tab.
7. Click Publish (from the Home menu) and select a Workspace.

Build Reports and Dashboards on Azure Data Lake Storage Data in the Power BI Service

Now that you have published a dataset to the Power BI service, you can create new reports and dashboards based on the published data:

1. Log in to PowerBI.com.
2. Click Workspaces and select a workspace.
3. Click Create and select Report.
4. Select the published dataset for the report. 👁 Select a dataset
   
5. Choose fields and visualizations to add to your report. 👁 Visualizing Azure Data Lake Storage data in the Power BI service
   

Live Access to Azure Data Lake Storage Data from Cloud Applications

Now you have a direct connection to live Azure Data Lake Storage data from the Power BI service. You can create more data sources and new visualizations, build reports, and more — all without replicating Azure Data Lake Storage data.

To get live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your cloud applications, sign up for a free trial of CData Connect AI.