Code Extension in Data 360 (Beta)

As of October 14, 2025, Data Cloud has been rebranded to Data 360. During this transition, you may see references to Data Cloud in our application and documentation. While the name is new, the functionality and content remains unchanged.

You can bring your custom Python code into Data 360 to extend Data 360's native capabilities by using code extension. If the native Data 360 features don't meet your business requirements, deploy your custom logic across supported Data 360 features such as batch data transforms. For example, if you need advanced data transformations for your business use case, you can deploy custom business logic to Data 360 to implement your requirements.

Code extension is a pilot or beta service that is subject to the Beta Services Terms at Agreements - Salesforce.com or a written Unified Beta Agreement if executed by Customer, and applicable terms in the Product Terms Directory. Use of this pilot or beta service is at the customer's sole discretion.

The code extension workflow spans local development, sandbox validation, and production deployment. Developers author and debug code locally, then deploy to Data 360 sandbox. Users with a Data Cloud Architect permission set manage code execution, monitoring, and migration in Data 360 environments.

Code Extension Development Workflow

  1. Write and Validate Custom Code: Developers write custom Python code in their local integrated development environment (IDE) and validate it locally by using Data Custom Code SDK local execution capabilities.

  2. Deploy to Sandbox: Developers deploy their validated code to Data 360 sandbox by using the Web UI, API, or SDK CLI.

  3. Invoke Custom Code in Sandbox: Data 360 users invoke custom code by creating a batch data transform that uses the deployed custom code.

  4. Run Custom Code in Sandbox: Data 360 users run or schedule the batch data transform to execute the custom code in the sandbox environment.

  5. Monitor and Validate in Sandbox: Data 360 users review execution logs, performance metrics, and output data to verify that the code works as expected.

  6. Package for Production: After successful validation, Data 360 users package the custom code, batch data transforms, and their dependencies into a data kit for migration to production.

  7. Install Data Kit in Production: Data 360 users install the packaged data kit containing the validated components into the production environment.

  8. Invoke Custom Code in Production (if needed): If the custom code was migrated, Data 360 users invoke the custom code by creating a batch data transform that uses it.

  9. Run Custom Code in Production: Data 360 users run or schedule the batch data transform to execute the custom code and process live production data.

  10. Monitor in Production: Data 360 users monitor execution logs, performance metrics, and output data to ensure reliable operation in production.