Model, Search, and Visualize Live Azure Data Lake Storage Data in ThoughtSpot

ThoughtSpot is a cloud-based analytics platform that uses artificial intelligence (AI) and natural language processing (NLP) to help users analyze data and make decisions. When paired with CData Connect AI, you get instant, cloud-to-cloud access to Azure Data Lake Storage data for visualizations, dashboards, and more. This article shows how to connect to Azure Data Lake Storage and build visualizations from Azure Data Lake Storage data in ThoughtSpot.

CData Connect AI provides a pure SQL Server, cloud-to-cloud interface for Azure Data Lake Storage, allowing you to easily build models and visualizations from live Azure Data Lake Storage data in ThoughtSpot. As you build visualizations, ThoughtSpot generates SQL queries to gather data. Using optimized data processing out of the box, CData Connect AI pushes all supported SQL operations (filters, JOINs, etc) directly to Azure Data Lake Storage, leveraging server-side processing to quickly return Azure Data Lake Storage data.

Configure Azure Data Lake Storage Connectivity for ThoughtSpot

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

Model, Search, and Visualize Live Azure Data Lake Storage Data in ThoughtSpot

To establish a connection from ThoughtSpot to the CData Connect AI Virtual SQL Server API, follow these steps.

1. Log into ThoughtSpot
2. On the top navigation bar, click Data.
3. Click Create new > Connection. 👁 Creating a new connection.
   
4. Name the connection and click "SQL Server" as the data warehouse. 👁 Selecting SQL Server as the connection type.
   
5. Click Continue on the top right.
6. Enter the connection settings:
   • Host: enter the Virtual SQL Server endpoint: tds.cdata.com
   • Port: : enter 14333
   • Username: enter your CData Connect AI username. This is displayed in the top-right corner of the CData Connect AI interface. For example, [email protected].
   • Password: enter the PAT you generated on the Settings page.
   • Database: enter the Connection Name of the CData Connect AI data source you want to connect to (for example, ADLS1).
   👁 Configuring the connection to the Virtual SQL Server API
   
7. Click Continue.
8. After connecting successfully, you will be able to choose which tables to include. 👁 Selecting tables (Salesforce is shown).
   
9. Click Create Connection.

After you successfully configure your connection, you can build models, search, and visualize your Azure Data Lake Storage data.

Real-Time Access to Azure Data Lake Storage Data from Cloud Applications

At this point, you have a direct, cloud-to-cloud connection to live Azure Data Lake Storage data from ThoughtSpot. You can model, search, and visualize your data from ThoughtSpot . For more information on gaining live access to data from more than 100 SaaS, Big Data, and NoSQL sources from cloud applications like ThoughtSpot, refer to our Connect AI page.