Integrate with Live Snowflake Data in MuleSoft (via CData Connect AI)

The MuleSoft Anypoint Platform enables the building, deployment, and management of APIs and integrations, facilitating seamless connectivity across applications and systems. When combined with CData Connect AI, it provides access to Snowflake data for visualizations, dashboards, and more. This article explains how to use CData Connect AI to create a live connection to Snowflake and how to connect and access live Snowflake data from the MuleSoft Anypoint Platform.

Prerequisites

Before configuring and using MuleSoft with CData Connect AI, you must first connect a data source to your CData Connect AI account. For more information, see the Connections section.

Additionally, you need to generate a Personal Access Token (PAT) on the Settings page. Be sure to copy it down, as it serves as your password during authentication.

Getting Started

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Configure Snowflake Connectivity for MuleSoft

Connectivity to Snowflake from MuleSoft is made possible through CData Connect AI. To work with Snowflake data from MuleSoft, we start by creating and configuring a Snowflake connection.

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a Connection

2. Select "Snowflake" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Snowflake.

   To connect to Snowflake:

   1. Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
   2. Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
   3. Set Warehouse to the Snowflake warehouse.
   4. (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
   5. (Optional) Set Database and Schema to restrict the tables and views exposed.
   6. (Optional) If MFA is enabled on your Snowflake account (via Duo Security), set MFACode to the passcode generated by your Duo authenticator app.

   See the Getting Started guide in the CData driver documentation for more information.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Snowflake 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 Snowflake data from Mulesoft.

Connecting to CData Connect AI

Follow these steps to establish a connection from Mulesoft to CData Connect AI through the JDBC driver:

1. Download and install the CData Connect AI JDBC driver.
   • Open the Integrations page of CData Connect AI.
   • Search for and select JDBC.
   • Download and run the setup file.
   • When the installation is complete, the JAR file can be found in the installation directory (inside the lib folder).
2. Log into Mulesoft Anypoint Studio or launch the desktop application.
3. Create a new Mulesoft project. 👁 Create a new MuleSoft project
   👁 Add the project name
   The new project appears in a project folder. 👁 The new project is created
   
4. In the Mule Palette located on the right, drag an HTTP Listener to the Message Flow area. 👁 Drag the HTTP Listener to the Message Flow area
   
5. Click on the HTTP Listener to configure it. 👁 Click on the HTTP Listener to configure it
   
6. Click the + sign on the right of Connector configuration. The HTTP Listener config dialog appears.
7. Configure the HTTP Listener, providing a Port on which to query your data, and click OK. 👁 Add the port number to configure the HTTP Listener
   
8. Provide a path on which to perform the actions. The HTTP Listener is now configured. 👁 Provide a path to perform the actions
   
9. In the Mule Palette on the right, type database in the search bar. 👁 Search for database in Mule Palette search bar
   
10. Drag the database operation you want to perform to the Message Flow area. For this example, we choose Select. 👁 Drag the database operation in the Message Flow area
    
11. Select Generic Connection from the Connection dropdown in the Database Config dialog. 👁 Select Generic Connection from the Connection dropdown
    
12. Click the Configure button to configure the JDBC driver. Select Use local file from the drop-down list. 👁 Select Use local file from the dropdown
    
13. Locate the CData Connect AI JAR file from the JDBC driver installation and click OK. 👁 Add the CData Connect AI JAR file path
    
14. Provide the following information:
    • URL: the URL for the connection, for example:

       jdbc:connect:Authscheme=Basic;user=username;password=PAT

      Note: the password is the PAT created in the Prerequisites section.
    • Driver class name: Enter the Driver class name as:

       cdata.jdbc.connect.ConnectDriver

      👁 Add the URL and the Driver class name
      
15. Click Test Connection. 👁 Click on Test Connection
    
16. If the connection is successful, provide the SQL Query Text in the editor. You can see the table metadata on the right side in the Output tab. 👁 Write the SQL Query
    
17. In the Mule Palette, drag Transform Message to the Message Flow area. 👁 Drag Transform Message to the Message Flow area
    
18. Click Transform Message to configure it. Change the Output as follows: 👁 Configure Transform Message
    
19. Save your project and run it. In the console, Mulesoft starts initializing the dependencies. 👁 Save and Run the project
    
20. Once you see the message, "Message source 'listener' on flow your_project_name successfully started", you can start querying your data at the endpoint you provided. 👁 Check for the 'Message source 'listener' on flow your_project_name successfully started' message to get started
    
21. Query to check out the data using the Postman application (as shown below). 👁 Send an API request from Postman to check the Snowflake data
    

SQL Access to Snowflake Data from Cloud Applications

Now you have a direct connection to live Snowflake data from MuleSoft Anypoint Platform. You can create more connections to ensure seamless data flow, automate business processes, and manage APIs - all without replicating Snowflake data.

To get real-time data access to hundreds of SaaS, Big Data, and NoSQL sources (including Snowflake) directly from your cloud applications, explore the CData Connect AI.