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URL: https://www.cdata.com/kb/tech/azuredatalake-cloud-workato.rst

⇱ Build Automated Workflows with Live Azure Data Lake Storage Data in Workato

Build Automated Workflows with Live Azure Data Lake Storage Data in Workato

👁 Jerod Johnson
Jerod Johnson
Director, Technology Evangelism
Use CData Connect AI to connect to live Azure Data Lake Storage data and build automated workflows in Workato.

Workato is a cloud-based automation platform that helps businesses integrate applications and automate workflows. 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 workflows with live Azure Data Lake Storage data in Workato.

CData Connect AI provides a pure SQL Server, cloud-to-cloud interface for Azure Data Lake Storage, allowing you to easily build visualizations from live Azure Data Lake Storage data in Workato. As you build automations, Workato 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 Workato

Connectivity to Azure Data Lake Storage from Workato is made possible through CData Connect AI. To work with Azure Data Lake Storage data from Workato, we start by creating and configuring a Azure Data Lake Storage connection.

  1. Log into Connect AI, click Sources, and then click Add Connection
    2. 👁 Adding a Connection
  2. Select "Azure Data Lake Storage" from the Add Connection panel
    3. 👁 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 Workato.

Automate Azure Data Lake Storage Data Workflows in Workato

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

  1. Log into Workato.
  2. In the navigation bar, click Projects.
  3. In your new (or existing) project, click Create > Connection. 👁 Creating a new Connection.
  4. In the search bar, enter "SQL Server" and open the Connect to SQL Server screen.
  5. In Connect to SQL Server, enter the connection name and the following connecting settings:
    • Location: enter the name of the project.
    • Connection type: select Cloud.
    • Host: enter 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 previously generated 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 CData Connect AI.
  6. Click Connect to see the Connection established message.
  7. Next, set up your recipe. In your project, click Create > Recipe. 👁 Creating a new Recipe.
  8. In Set up your recipe, enter the name and location of your recipe.
  9. Select Run on a schedule.
  10. Select the action to occur on the schedule.
  11. Select a table from your Connect AI connection and any applicable filters. 👁 Selecting tables for the Recipe (Jira is shown).
  12. Click Save to save the recipe.
  13. Click Test Jobs to test you recipe.

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 Workato for workflows and automations. For more information on gaining live access to data from more than 100 SaaS, Big Data, and NoSQL sources from cloud applications like Workato, refer to our Connect AI page.