Data360 Code Extension Generate

data360-code-extension-generate Skill

Overview

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:

1. SF CLI with plugin installed

   sf plugins --core | grep data-code-extension

   If not installed:

   sf plugins install @salesforce/plugin-data-code-extension

2. Python 3.11

   python --version  # Should show 3.11.x

3. Data Cloud Custom Code SDK

   pip list | grep salesforce-data-customcode

   If not installed:

   pip install salesforce-data-customcode

4. Docker running (for deploy only)

   docker ps

5. 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:

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')

Function Example (Real-time):

from datacustomcode import FunctionClient

def transform(event, context):
    client = FunctionClient(context)
    input_data = event['data']
    output = {
        'name': input_data['name'].upper(),
        'status': 'processed'
    }
    return output

Common Operations:

• client.read_dlo('DLO_Name__dll') - Read from DLO
• client.read_dmo('DMO_Name') - Read from DMO
• client.write_to_dlo('DLO_Name__dll', df, 'overwrite') - Write to DLO
• client.write_to_dmo('DMO_Name', df, 'upsert') - Write to DMO

Phase 3: Scan for Permissions

Scan the entrypoint file to detect required permissions and generate config.json.

Command:

sf data-code-extension script scan --entrypoint ./payload/entrypoint.py

What it detects:

• Read permissions for DLOs/DMOs
• Write permissions for DLOs/DMOs
• Python package dependencies
• Updates config.json and requirements.txt

Phase 4: Validate DLO Schema (Pre-Test Check)

CRITICAL: Before running tests locally, validate that all DLOs used in your code exist and have the expected fields.

Step 4a: Extract DLOs from config.json

After scanning, review the generated config.json to identify all DLOs:

cat payload/config.json

Step 4b: Validate Each DLO Schema

Use the data360-schema-get skill to verify DLOs exist and check field names.

For each DLO referenced in your code:

1. Verify DLO exists:

   python3 scripts/get_dlo_schema.py <org_alias> <dlo_name>

2. Verify field names match — compare fields used in your entrypoint.py against the DLO schema.

3. Check all DLOs:

   • Validate all DLOs in read permissions
   • Validate all DLOs in write permissions
   • Check field names match exactly (case-sensitive)
   • Verify data types are compatible with operations

Step 4c: Validation Checklist

Before proceeding to run, ensure:

• All DLOs in config.json exist in target org
• All field names used in code exist in DLO schemas
• Field data types match your transformation logic
• Primary key fields are correctly identified
• Write target DLOs are created and accessible

Phase 5: Test Locally

After validating DLO schemas, run the code extension locally against your Data Cloud org.

Command:

sf data-code-extension script run --entrypoint <entrypoint_file> --target-org <org_alias> [options]

Options:

• --target-org, -o - SF CLI org alias (required)
• --config-file, -c - Custom config file path

If you get errors:

• Re-validate DLO schemas
• Check field names are exact matches
• Verify data types are compatible
• Review error messages for field/DLO issues