Connect to and Query Azure Data Lake Storage Data in QlikView over ODBC

The CData ODBC drivers expand your ability to work with data from hundreds of data sources. QlikView is a business discovery platform that provides self-service BI for all business users in an organization. This article outlines simple steps to connect to Azure Data Lake Storage data using the CData ODBC driver and create data visualizations in QlikView.

The CData ODBC drivers offer unmatched performance for interacting with live Azure Data Lake Storage data in QlikView due to optimized data processing built into the driver. When you issue complex SQL queries from QlikView to Azure Data Lake Storage, the driver 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 QlikView data types.

Connect to Azure Data Lake Storage as an ODBC Data Source

If you have not already, first specify connection properties in an ODBC DSN (data source name). This is the last step of the driver installation. You can use the Microsoft ODBC Data Source Administrator to create and configure ODBC DSNs.

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.

When you configure the DSN, you may also want to set the Max Rows connection property. This will limit the number of rows returned, which is especially helpful for improving performance when designing reports and visualizations.

Populate a Chart with Azure Data Lake Storage Data

The steps below supply the results of an SQL query to a visualization in QlikView. In this article, you will create a bar chart with the query below:

SELECT FullPath, Permission FROM Resources WHERE Type = 'FILE'

1. Click File -> Edit Script (or click the Edit Script button in the Toolbar).
2. On the Data tab, select ODBC in the Database menu and click Connect. 👁 Connecting to an ODBC data source.
   
3. Select the DSN (CData ADLS Sys) in the resulting dialog. 👁 Selecting a DSN (Salesforce is shown).
   A command like the following is generated:

   ODBC CONNECT TO [CData ADLS Sys];
   

4. Enter the SQL query directly into the script with the SQL command (or click Select to build the query in the SELECT statement wizard).

   SQL SELECT FullPath, Permission FROM Resources WHERE Type = 'FILE';
   

   Where possible, the SQL operations in the query, like filters and aggregations, will be pushed down to Azure Data Lake Storage, while any unsupported operations (which can include SQL functions and JOIN operations) will be managed client-side by the CData SQL engine embedded in the driver.

   👁 A script that connects and executes an SQL query. (Salesforce is shown.)
   
5. Close the script editor and reload the document to execute the script.
6. Click Tools -> Quick Chart Wizard. In the wizard, select the chart type. This example uses a bar chart. When building the chart, you have access to the fields from Azure Data Lake Storage, typed appropriately for QlikView, thanks to built-in dynamic metadata querying.
7. When defining Dimensions, select FullPath in the First Dimension menu.
8. When defining Expressions, click the summary function you want and select Permission in the menu.

9. Finish the wizard to generate the chart. The CData ODBC Driver for Azure Data Lake Storage connects to live Azure Data Lake Storage data, so the chart can be refreshed to see real-time changes. Live connections are possible and effective, thanks to the high-performance data processing native to CData ODBC Drivers.

   👁 A chart populated with the results of a query. (Salesforce is shown.)