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

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CData Connect



Use CData Connect Cloud to create an OData feed for Azure Data Lake Storage and create custom reports in the Power BI Service.

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 Cloud, you get access to Azure Data Lake Storage data for visualizations, dashboards, and more. This article shows how to use the CData Connect Cloud to generate an OData feed for Azure Data Lake Storage, import Azure Data Lake Storage data into Power BI and then create reports on Azure Data Lake Storage data in the Power BI service.

NOTE: You can also use the on-premise data gateway and the SQL interface in Connect Cloud to connect to Azure Data Lake Storage data in real-time (instead of importing the data). Read how in the related Knowledge Base article.

Create a Virtual SQL Database for Azure Data Lake Storage Data

CData Connect Cloud uses a straightforward, point-and-click interface to connect to data sources and generate APIs.

  1. Login to Connect Cloud and click Databases.
  2. Select "Azure Data Lake Storage" from Available Data Sources.
  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 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 "Azure Active Directory".
    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.
  4. Click Test Database.
  5. Click Privileges -> Add and add the new user (or an existing user) with the appropriate permissions.

Connecting to Connect Cloud from Power BI

To import and visualize your Azure Data Lake Storage data in the Power BI service, add a Connect Cloud API user, add Azure Data Lake Storage OData endpoints in Connect Cloud, and create & publish a dataset from Power BI Desktop to the service.

Add a Connect Cloud User

Create a User to connect to Azure Data Lake Storage from Power BI through Connect Cloud.

  1. Click Users -> Add
  2. Configure a User.
  3. Click Save Changes and make note of the Authtoken for the new user.
  4. Click Database and select the Azure Data Lake Storage virtual database.
  5. On the Privileges tab, add the newly created user (with at least SELECT permissions) and click Save Changes.

Add Azure Data Lake Storage OData Endpoints in Connect Cloud

After connecting to Azure Data Lake Storage, create OData Endpoints for the desired table(s).

  1. Click OData -> Tables -> Add Tables.
  2. Select the Azure Data Lake Storage database.
  3. Select the table(s) you wish to work with and click Next.
  4. (Optional) Edit the table definition to select specific fields and more.
  5. Save the settings.

Publish a Dataset from Power BI Desktop

With OData endpoints added to Connect Cloud, you can create a dataset in Power BI descktop using OData connectivity and publish the dataset to the Power BI service.

  1. Open Power BI, click Get Data -> More, then select OData feed, and click Connect.
  2. Set URL to the Base URL of your Connect Cloud instance (e.g.: https://www.cdatacloud.net/myinstance/api.rsc) and click OK.
  3. Set User name and Password to user name and Authtoken from above.
  4. Select the full Base URL (https://www.cdatacloud.net/myinstance/api.rsc) as the level to apply the settings to/
  5. Select the table(s) to visualize in the Navigator dialog.
  6. Click Load to pull the data into Power BI.
  7. Define any relationships between the selected entities on the Relationships tab.
  8. 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.
  5. Choose fields and visualizations to add to your report.

SQL 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 SQL data access to 200+ SaaS, Big Data, and NoSQL sources directly from your cloud applications, see the CData Connect Cloud.