Create Azure Data Lake Storage-Connected Nintex Workflows

Nintex Workflow Cloud is a cloud-based platform where you can design workflows to automate simple or complex processes using drag-and-drop interactions — without writing any code. When paired with CData Connect AI, you get instant, cloud-to-cloud access to Azure Data Lake Storage data for business applications. This article shows how to create a virtual database for Azure Data Lake Storage in Connect AI and build a simple workflow from Azure Data Lake Storage data in Nintex.

CData Connect AI provides a pure, cloud-to-cloud interface for Azure Data Lake Storage, allowing you to build workflows from live Azure Data Lake Storage data in Nintex Workflow Cloud — without replicating the data to a natively supported database. Nintex allows you to access data directly using SQL queries. 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 the requested Azure Data Lake Storage data.

Configure Azure Data Lake Storage Connectivity for Nintex

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

Connect to Azure Data Lake Storage from Nintex

The steps below outline creating a new connection to Azure Data Lake Storage data from Nintex (via Connect AI).

1. Log into Nintex Workflow Cloud
2. In the Connections tab, click "Add new"
3. Select "Microsoft SQL Server" as the connector and click "Connect" 👁 Adding a new SQL Server Connection
   
4. In the SQL Server connection wizard, set the following properties:
   • Connection Name: Connect AI
   • Username: a Connect AI username (e.g. [email protected])
   • Password: the Connect AI user's PAT
   • Database Host: tds.cdata.com:14333
   • Database Name: the Azure Data Lake Storage connection (a.g., ADLS1)
   👁 Configuring the Connection to Connect AI
   
5. Click "Connect"
6. Configure the connection permissions and click "Save permissions" 👁 Configuring permissions and saving the Connection
   

Create a Simple Azure Data Lake Storage Workflow

With the connection to CData Connect AI configured, we are ready to build a simple workflow to access Azure Data Lake Storage data. Start by clicking the "Create workflow" button.

Configure the Start Event Action

1. Click the start event task and select the "Form" event
2. Click "Design form"
3. Drag a "Text - Long" element onto the Form and click the element to configure it
   • Set "Title" to "Enter SQL query"
   • Set "Required" to true
4. Drag a "Text - Short" element onto the Form and click the element to configure it
   • Set "Title" to "Enter desired result column"
   • Set "Required" to true

👁 Designing the Start event Form

Configure an "Execute a Query" Action

1. Add an "Execute a query" action after the "Start event: Form" action and click to configure the action
2. Set "SQL Script" to the "Enter SQL Query" variable from the "Start event" action
3. Set "Column to retrieve" to the "Enter desired result column" variable from the "Start event" action
4. Set "Retrieved column" to a new variable (a.g., "values")

👁 Configuring the SQL Server query action

Configure a "Send an Email" Action

1. Add a "Send an email" action after the "Execute a query" action and click to configure the action
2. Set the "Recipient email address"
3. Set the "Subject"
4. Set the "Message body" to the variable created for the retrieved column

👁 Configuring the email action

Once you configure the actions, click "Save," name the Workflow, and click "Save" again. You now have a simple workflow that will query Azure Data Lake Storage using SQL and sand an email with the results.

To learn more about live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your cloud applications, check out the CData Connect AI page. Sign up for a free trial and reach out to our Support Team if you have any questions.