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. The CData Connect Server creates a virtual database for Azure Data Lake Storage and can be used to generate an OData API (natively consumable in Qlik Sense Cloud) for Azure Data Lake Storage. By pairing Qlik Sense Cloud with the CData Connect Server, you get live 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 Server from Qlik Sense Cloud as you would any other REST service 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 Server
2. Connecting to Connect Server from Qlik Sense Cloud to create a model and build a simple dashboard

Configure Connect Server to Connect to Azure Data Lake Storage

To connect to Azure Data Lake Storage data from Qlik Sense Cloud, you need to configure Azure Data Lake Storage access from your Connect Server instance. This means creating a user, connecting to Azure Data Lake Storage, adding OData endpoints, and (optionally) configuring CORS.

Add a Connect Server User

Create a Connect Server User to connect to Azure Data Lake Storage from Qlik Sense Cloud.

1. Click Users -> Add
2. Configure a User
3. Click Save Changes and make note of the Authtoken for the new user

Connect to Azure Data Lake Storage from Connect Server

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

1. Open Connect Server and click Connections
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 Save Changes
5. Click Privileges -> Add, and add the new user (or an existing user) with the appropriate permissions (SELECT is all that is required for Reveal)

Add Azure Data Lake Storage OData Endpoints in Connect Server

After connecting to Azure Data Lake Storage, create OData Endpoint 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 resource to select specific fields and more
5. Save the settings

(Optional) Configure Cross-Origin Resource Sharing (CORS)

When accessing and connecting to multiple domains from an application such as Ajax, there is a possibility of violating the limitations of cross-site scripting. In that case, configure the CORS settings in OData -> Settings.

• Enable cross-origin resource sharing (CORS): ON
• Allow all domains without '*': ON
• Access-Control-Allow-Methods: GET, PUT, POST, OPTIONS
• Access-Control-Allow-Headers: Authorization

Save the changes to the settings.

Create a Qlik Sense App from Azure Data Lake Storage Data

With the connection to Azure Data Lake Storage and OData endpoints created, we are ready 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
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
5. Select the REST connector and set the configuration properties. For the most part, you will use the default values, with the following exceptions:
   • URL: Set this to the API endpoint for your Azure Data Lake Storage table, using the @CSV URL parameter to ensure a CSV response (i.e. CONNECT_SERVER_URL/api.rsc/ADLS_Resources?@CSV)
   • Authentication Schema: Set this to "Basic"
   • User Name: Set this to the user name you configured above
   • Password: Set this to the Authtoken for the above user
6. Click "Create" to query Connect Server for the Azure Data Lake Storage data
7. Check "CSV has header" and under "Tables," select "CSV_source"
8. Select columns and click "Add data"

Generate Insights or Customize Your App

With the data loaded into Qlik Sense, you are ready to begin discovering insights. Click "Generate insights" to let Qlik analyze your data. Otherwise, you can build custom visualizations, reports, and dashboards based on 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 200 other data sources), visit the Connect Server page. Sign up for a free trial and start working with live Azure Data Lake Storage data in Qlik Sense Cloud.