Author Power BI Reports on Real-Time Azure Data Lake Storage Data

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Azure Data Lake Storage Power BI Connector

The fastest and easiest way to connect Power BI to Azure Data Lake Storage data. Includes comprehensive high-performance data access, real-time integration, extensive metadata discovery, and robust SQL-92 support.



Use the CData Power BI Connector for Azure Data Lake Storage to visualize Azure Data Lake Storage data in Power BI.

CData Power BI Connectors provide self-service integration with Microsoft Power BI. The CData Power BI Connector for Azure Data Lake Storage links your Power BI reports to real-time Azure Data Lake Storage data. You can monitor Azure Data Lake Storage data through dashboards and ensure that your analysis reflects Azure Data Lake Storage data in real time by scheduling refreshes or refreshing on demand. This article details how to use the Power BI Connector to create real-time visualizations of Azure Data Lake Storage data in Microsoft Power BI Desktop.

If you are interested in publishing reports on Azure Data Lake Storage data to PowerBI.com, refer to our other Knowledge Base article.

Collaborative Query Processing

The CData Power BI Connectors offer unmatched performance for interacting with live Azure Data Lake Storage data in Power BI due to optimized data processing built into the connector. When you issue complex SQL queries from Power BI to Azure Data Lake Storage, the connector pushes supported SQL operations, like filters and aggregations, directly to Azure Data Lake Storage and utilizes the embedded SQL Engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can visualize and analyze Azure Data Lake Storage data using native Power BI data types.

Connect to Azure Data Lake Storage as a Power BI Data Source

Installing the Power BI Connector creates a DSN (data source name) called CData Power BI Azure Data Lake Storage. This the name of the DSN that Power BI uses to request a connection to the data source. Configure the DSN by filling in the required connection properties.

You can use the Microsoft ODBC Data Source Administrator to create and configure the DSN: From the Start menu, enter "ODBC Data Sources" and select the CData PowerBI REST DSN. Ensure that you run the version of the ODBC Administrator that corresponds to the bitness of your Power BI Desktop installation (32-bit or 64-bit). You can also use run the ConfigureODBC.exe tool located in the installation folder for the connector.

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.

Query Azure Data Lake Storage Tables

Follow the steps below to build a query to pull Azure Data Lake Storage data into the report:

  1. Open Power BI Desktop and click Get Data -> Other -> CData ADLS.
  2. Select CData PowerBI Azure Data Lake Storage in the Data Source Name menu and select a data connectivity mode:
    Select Import if you want to import a copy of the data into your project. You can refresh this data on demand.
    Select DirectQuery if you want to work with the remote data.
  3. Select tables in the Navigator dialog.
  4. In the Query Editor, you can customize your dataset by filtering, sorting, and summarizing Azure Data Lake Storage columns. Click Edit to open the query editor. Right-click a row to filter the rows. Right-click a column header to perform actions like the following:

    • Change column data types
    • Remove a column
    • Group by columns

    Power BI detects each column's data type from the Azure Data Lake Storage metadata retrieved by the connector.

    Power BI records your modifications to the query in the Applied Steps section, adjusting the underlying data retrieval query that is executed to the remote Azure Data Lake Storage data. When you click Close and Apply, Power BI executes the data retrieval query.

    Otherwise, click Load to pull the data into Power BI.

Create Data Visualizations

After pulling the data into Power BI, you can create data visualizations in the Report view by dragging fields from the Fields pane onto the canvas. Follow the steps below to create a pie chart:

  1. Select the pie chart icon in the Visualizations pane.
  2. Select a dimension in the Fields pane: for example, FullPath.
  3. Select a measure in the Fields pane: for example, Permission.

You can change sort options by clicking the ellipsis (...) button for the chart. Options to select the sort column and change the sort order are displayed.

You can use both highlighting and filtering to focus on data. Filtering removes unfocused data from visualizations; highlighting dims unfocused data. You can highlight fields by clicking them:

You can apply filters at the page level, at the report level, or to a single visualization by dragging fields onto the Filters pane. To filter on the field's value, select one of the values that are displayed in the Filters pane.

Click Refresh to synchronize your report with any changes to the data.

At this point, you will have a Power BI report built on top of live Azure Data Lake Storage data. Learn more about the CData Power BI Connectors for Azure Data Lake Storage and download a free trial from the Azure Data Lake Storage Power BI Connector page. Let our Support Team know if you have any questions.