adf-master

Delivers an Azure Data Factory master knowledge base for CI/CD, troubleshooting, and migration.

51|10|Updated Oct 22, 2025
One-click install
npx skills add https://github.com/JosiahSiegel/claude-code-marketplace --skill adf-master
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: adf-master
Source: https://github.com/JosiahSiegel/claude-code-marketplace/tree/main/plugins/adf-master/skills/adf-master
Command: npx skills add https://github.com/JosiahSiegel/claude-code-marketplace --skill adf-master

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires npm, PowerShell.

What problem does it solve?

Azure Data Factory (ADF) projects often struggle with complex CI/CD setups, deployment challenges, and effective troubleshooting. This skill provides a comprehensive knowledge base, offering official documentation, modern CI/CD patterns, and diagnostic tools to streamline your ADF development and operations, ensuring reliable data integration.

Core Features & Use Cases

  • Modern CI/CD Guidance: Implement automated build and deployment pipelines using the @microsoft/azure-data-factory-utilities npm package with GitHub Actions or Azure DevOps.
  • Deployment Automation: Leverage the PrePostDeploymentScript.Ver2.ps1 for intelligent trigger management during ARM template deployments, minimizing disruption.
  • Troubleshooting & Best Practices: Access Kusto queries for diagnostic logs, common error patterns, and recommended repository structures to quickly resolve issues.
  • Use Case: Set up a new CI/CD pipeline for an ADF project, ensuring automated validation, ARM template generation, and intelligent trigger management across development, test, and production environments.

Quick Start

Use the adf-master skill to get the latest GitHub Actions workflow for deploying Azure Data Factory ARM templates with intelligent trigger management.

Frequently Asked Questions about adf-master

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up CI/CD for Azure Data Factory with GitHub Actions?

CI/CD for Azure Data Factory uses the @microsoft/azure-data-factory-utilities npm package to automate ARM template generation and deployment. GitHub Actions workflows trigger validation, build, and deployment stages across development, test, and production environments, ensuring consistent pipeline execution.

What's the best way to deploy ARM templates in Azure Data Factory without disrupting active triggers?

Use the PrePostDeploymentScript.Ver2.ps1 PowerShell script during ARM template deployment to intelligently manage triggers. The script pauses triggers before deployment and resumes them afterward, minimizing data pipeline disruption.

How do I troubleshoot Azure Data Factory deployment and runtime errors?

Troubleshooting Azure Data Factory uses Kusto queries to analyze diagnostic logs, identify common error patterns, and surface actionable resolution steps. Repository structure best practices and error documentation help resolve issues across development and production environments.

Can I use Azure DevOps instead of GitHub Actions for Azure Data Factory deployments?

Yes. Both Azure DevOps and GitHub Actions work with the @microsoft/azure-data-factory-utilities npm package for automated ADF deployment. Modern CI/CD patterns apply across platforms, supporting ARM template generation and trigger management regardless of pipeline tool.

What dependencies do I need to implement Azure Data Factory CI/CD automation?

Implement Azure Data Factory CI/CD with npm for the @microsoft/azure-data-factory-utilities package and PowerShell for deployment scripts. These dependencies enable ARM template generation, validation, and intelligent trigger management in automated workflows.

Why should I migrate to modern CI/CD patterns for Azure Data Factory projects?

Modern CI/CD patterns for Azure Data Factory eliminate manual deployments, ensure compliance with 2025 updates and deprecations, and provide repeatable automation across environments. Standardized repository structures and deployment workflows reduce errors and accelerate time-to-production.