Develop and deploy Data Cloud Code Extensions using SF CLI plugin. Use this skill when creating custom Python transformations for Data Cloud, deploying code extensions, or testing data transformations. Supports init, run, scan, and deploy operations.
Instrucciones de origen · Vista previa de solo lectura
name
data360-code-extension-generate
description
Develop and deploy Data Cloud Code Extensions using SF CLI plugin. Use this skill when creating custom Python transformations for Data Cloud, deploying code extensions, or testing data transformations. Supports init, run, scan, and deploy operations.
This skill provides a complete workflow for developing, testing, and deploying custom Python code extensions to Salesforce Data Cloud. Code extensions allow you to write Python transformations that read from and write to Data Lake Objects (DLOs) and Data Model Objects (DMOs).
When to Use
User wants to create a new code extension project
User needs to test a code extension locally
User wants to scan code for required permissions
User needs to deploy a code extension to Data Cloud
User is working with Data Cloud transformations
User wants to read/write DLO or DMO data programmatically
Prerequisites Check
Before executing any code extension commands, verify prerequisites:
SF CLI with plugin installed
sf plugins --core | grep data-code-extension
If not installed:
sf plugins install @salesforce/plugin-data-code-extension
Python 3.11
python --version # Should show 3.11.x
Data Cloud Custom Code SDK
pip list | grep salesforce-data-customcode
If not installed:
pip install salesforce-data-customcode
Docker running (for deploy only)
docker ps
Authenticated org
sf org display --target-org <org_alias> --json
Skill Workflow
Phase 1: Initialize Project
Create a new code extension project with scaffolding.
Commands:
For script-based code extensions (batch transformations):
sf data-code-extension script init --package-dir <directory>
For function-based code extensions (real-time):
sf data-code-extension function init --package-dir <directory>
Required Option:
--package-dir, -p - Directory path where the package will be created
What it creates:
my-transform/ # Project root
├── payload/ # CRITICAL: This is what --package-dir must point to for deploy
│ ├── entrypoint.py # Main transformation code
│ └── config.json # Code extension configuration
├── requirements.txt # Python dependencies
└── README.md
Directory Context During Workflow
IMPORTANT: Understanding the directory structure is critical for successful deployment.
Commands and their directory requirements:
Command
Run From
Path/File Argument
init
Parent directory
<project-name> or .
scan
Project root
./payload/entrypoint.py
run
Project root
./payload/entrypoint.py
deploy
Project root
--package-dir ./payload (REQUIRED)
CRITICAL: The --package-dir argument in deploy command MUST point to the payload directory, not the project root.
Phase 2: Develop Transformation
Edit payload/entrypoint.py with transformation logic.
Script Example (Batch):
from datacustomcode import Client
client = Client()
# Read from DLO
df = client.read_dlo('Employee__dll')
# Transform data (uppercase position field)
df['position_upper'] = df['position'].str.upper()
# Write to output DLO
client.write_to_dlo('Employee_Upper__dll', df, 'overwrite')
