Set Up Salesforce CLI for Code Extension

Salesforce CLI, the Code Extension plugin, and the Data Custom Code Python SDK work together as your local toolchain for Code Extension. Use this setup to develop and test custom Python scripts locally against a sandbox and then deploy them to Data 360 for execution.

Edition Table
Available in: Developer, Enterprise, Performance, and Unlimited Editions. See Data 360 edition availability.
User Permissions Needed
To set up and use Salesforce CLI for code extension development:Permission set:
  • Data Cloud Architect
  • Local machine:
    • Operating system: macOS, Linux, or Windows
    • Python 3.11
      • macOS and Linux: We recommend using pyenv to install Python 3.11.
      • Windows: Install Python 3.11 from Python.org, the Microsoft Store, or use pyenv-win. Depending on your install, run Python as py -3.11 or python when creating a virtual environment.
    • Azul Zulu OpenJDK 17.x
    • Docker Desktop. On Windows, enable the WSL 2 backend for Dev Containers and other container workflows. See Docker Desktop on Windows.
  • Data 360: Sandbox or org access with appropriate permission sets. See Create a Data 360 Sandbox. Enable Code Extension in Feature Manager. See Enable Data 360 Features in Feature Manager.

  1. Install Salesforce CLI version 2.130.9 or later. See Install Salesforce CLI.

  2. Confirm that your CLI version is 2.130.9 or later.

    If you have an earlier version, update CLI. See Update Salesforce CLI, and then run sf version to confirm.

  3. On Windows, install Node.js LTS and verify the installation.

  4. Install the Code Extension plugin.

  5. Make sure that the installed Code Extension plugin version is 0.1.5 or later. If you have an earlier version, update the plugin to the latest available version.

  6. Install the Data Custom Code Python SDK.

    macOS and Linux

python3.11 -m pip install –upgrade salesforce-data-customcode

  1. (Optional) Upgrade the Data Custom Code Python SDK to the latest available version.

    macOS and Linux

    Windows

  2. Confirm the SDK installation.

    • macOS and Linux: python3.11 -m pip show salesforce-data-customcode
    • Windows: py -3.11 -m pip show salesforce-data-customcode

  3. Log in to your org. From a terminal, run one of the commands with the flags for your environment. The CLI opens your default browser for Salesforce login. After you log in, the CLI saves the session on your computer.

    • Production or Developer Edition

    • Sandbox

    • Custom domain

  4. After you log in, confirm which orgs the CLI is connected to and which alias you must use for code extension commands.

For example, sf org display --target-org myorg shows the connection status of your target org myorg.

Running the sf org login command is a one-time setup per environment or profile. Rerun this command only if your credentials or environment details change, or to set up a new profile.

  1. Create a code extension script package.

    Replace <my_package> with the directory name for your project (for example, my_package).

The code extension script package sets up the required directory structure, configuration files, and dependencies for writing, testing, and deploying code extension.

This table summarizes the directories and files in the package.

Directory or FileDescription
DockerfileUsed for containerized builds and deployments. Don’t modify this file.
requirements.txtList the Python packages your script needs for deployment.
requirements-dev.txtPython packages for local development and testing only. Don’t modify this file.
payload/Contains the script and configuration that the CLI deploys to Data 360.
payload/config.jsonData 360 deployment configuration for your code extension package.
payload/entrypoint.pyMain Python file where you implement batch data transform logic.
examples/Example scripts and sample data you can copy or learn from.
examples/employee_hierarchy/Sample that builds a directed acyclic graph (DAG) from employee hierarchy data. Upload employee_data.csv, create the DLOs the sample expects, then copy or run entrypoint.py as described in the example.
jupyterlab.shShell script to start JupyterLab for interactive development.
.devcontainer/Visual Studio Code Dev Container configuration for a consistent local environment.
README.mdPackage overview, setup notes, and how to run the sample in a container or locally.