Create Apps from Azure Data Lake Storage Data in Qlik Sense Cloud

Qlik Sense Cloud allows you to create and share data visualizations and interact with information in new ways. CData Connect AI creates a live connection to Azure Data Lake Storage. By pairing Qlik Sense Cloud with CData Connect AI, you get true cloud-to-cloud connectivity to all of your SaaS and cloud-based Big Data and NoSQL sources — no need to migrate your data or write your integrations. Simply connect to Connect AI from Qlik Sense Cloud and get instant, live access to your Azure Data Lake Storage data.

In this article, we walk through two connections:

1. Connecting to Azure Data Lake Storage in Connect AI
2. Connecting to Connect AI from Qlik Sense Cloud to create a model and build a simple dashboard

Configure Azure Data Lake Storage Connectivity for Qlik Sense Cloud

Connectivity to Azure Data Lake Storage from Qlik Cloud is made possible through CData Connect AI. To work with Azure Data Lake Storage data from Qlik Cloud, 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
   

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create. 👁 Creating a new PAT
   
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to Azure Data Lake Storage data from Qlik Sense Cloud.

Create a Qlik Sense App from Azure Data Lake Storage Data

This section explains how to add Azure Data Lake Storage data to a Qlik Sense app for visualizations, analytics, reporting, and more.

Create a New App and Upload Data

1. Log into your Qlik Sense instance and click the button to create a new app 👁 Creating a new app
   
2. Name and configure the new app and click "Create"
3. In the workspace, click to open the new app
4. Click to add data from files and other sources 👁 Selecting a connector
   
5. Select the Microsoft SQL Server connector and set the configuration properties.
   • Set Server to tds.cdata.com
   • Set Port to 14333
   • Set Database to the connection you created (e.g. ADLS1)
   • Set User name to the Connect AI user (e.g. [email protected])
   • Set Password to the PAT for the above user
   👁 Configuring the connection
   
6. Select an owner for the connection
7. Select a Azure Data Lake Storage entity (Table) to view 👁 Adding data to the app (Salesforce is shown.)
   
8. Click Next, configure the model, and load the data into Qlik Sense.

Generate Insights or Customize Your App

With the data loaded into Qlik Sense, you are ready to begin discovering insights. You can build custom visualizations, reports, and dashboards engineered to gain actionable insights into your Azure Data Lake Storage data.

More Information & Free Trial

Now, you have created a simple but powerful dashboard from live Azure Data Lake Storage data. For more information on creating OData feeds from Azure Data Lake Storage (and more than 100 other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Azure Data Lake Storage data in Qlik Sense Cloud.