Analyze Azure Data Lake Storage Data in Infragistics Reveal

Reveal, an offering from Infragistics, serves as a data visualization tool that seamlessly integrates with CData Connect AI. Together, they empower users to deliver dynamic dashboards using real-time data from a diverse range of data sources, including Azure Data Lake Storage data. In this article, we will guide you through the process of connecting to Azure Data Lake Storage via Connect AI and subsequently linking Connect AI with Infragistics Reveal to construct a straightforward dashboard.

Connect to Azure Data Lake Storage from Infragistics Reveal

To work with live Azure Data Lake Storage data in Infragistics Reveal, we need to connect to Azure Data Lake Storage from Connect AI, provide user access to the connection, and create a Workspace for the Azure Data Lake Storage data.

Connect to Azure Data Lake Storage from Connect AI

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

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.

Configure Azure Data Lake Storage Endpoints for Infragistics Reveal

After connecting to Azure Data Lake Storage, create a workspace for your desired table(s).

1. Navigate to the Workspaces page and click Add to create a new Workspace (or select an existing workspace). 👁 The Workspaces page.
   👁 Adding a new Workspace.
   
2. Click Add to add new assets to the Workspace.
3. Select the Azure Data Lake Storage connection (e.g. ADLS1) and click Next. 👁 Selecting an Asset (Salesforce is shown).
   
4. Select the table(s) you wish to work with and click Confirm. 👁 Selecting Tables (Salesforce is shown).
   
5. Make note of the OData Service URL for your workspace, e.g. https://cloud.cdata.com/api/odata/{workspace_name}

With the connection, PAT, and Workspace configured, you are ready to connect to Azure Data Lake Storage data from Infragistics Reveal.

Create a Dashboard in Reveal

With Connect AI configured, we can visualize Azure Data Lake Storage data in Reveal.

1. Log into Reveal and click Dashboards -> New 👁 Adding a new dashboard
   
2. Click Data Sources -> OData Feed 👁 Adding a new OData data source
   
3. Specify the Connect AI OData API endpoint URL (found on the OData page): https://cloud.cdata.com/api/odata/{workspace_name} 👁 Configuring the OData URL
   
4. Select Generic Credentials and
   • Set Username to a Connect AI username (e.g. [email protected])
   • Set Password to the PAT for the user
   👁 Configuring the credentials
   
5. Select the entity you wish to visualize 👁 Selecting an entity to visualize (Salesforce is shown.)
   
6. Select fields and choose a chart type 👁 Visualizing data in Reveal (Salesforce is shown.)
   

More Information & Free Trial

At this point, you have created a simple 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 sources), visit the Connect AI page. Sign up for a free trial and start working live Azure Data Lake Storage data in tools that consume OData APIs.