Duration 3 Days – 24 hrs.

 

Overview

 

This training course provides participants with a comprehensive understanding of Azure Data Factory (ADF)—Microsoft’s cloud-based data integration and orchestration service. The program is designed to equip participants with the skills to build, manage, and optimize data pipelines, enabling efficient data movement and transformation across various sources.

 

Through a combination of concept discussions, guided labs, and hands-on exercises, participants will learn how to design scalable ETL/ELT workflows, integrate with modern data platforms, and implement data-driven solutions using Azure services.

 

Objectives

 

• Understand the architecture and core concepts of Azure Data Factory
• Create and manage pipelines for data integration workflows
• Design ETL/ELT processes using ADF components
• Integrate multiple data sources (on-premise and cloud)
• Implement data transformation using Data Flows and external compute
• Monitor, troubleshoot, and optimize pipelines
• Apply best practices for secure and scalable data orchestration

 

Target Audience

 

• Data Engineers and ETL Developers
• Business Intelligence (BI) Developers
• Database Administrators
• Cloud Engineers and Architects
• IT Professionals transitioning to data engineering roles

 

 Prerequisites

 

• Basic understanding of databases (SQL concepts)
• Familiarity with data warehousing concepts
• Basic knowledge of cloud computing (preferably Microsoft Azure)
• Experience with any ETL tool is an advantage but not required

 

Course Outline

 

Day 1: Introduction and Core Concepts

 

Module 1: Introduction to Azure Data Factory

 

• Overview of Azure Data Platform
• What is Azure Data Factory
• ADF vs traditional ETL tools
• Use cases and real-world applications

 

Module 2: ADF Architecture and Components

 

• Pipelines, Activities, Datasets, Linked Services
• Integration Runtime (Azure, Self-hosted)
• Authoring and monitoring interface

 

Module 3: Getting Started with ADF

 

• Creating an ADF instance
• Navigating ADF Studio
• Creating Linked Services
• Creating Datasets

 

Hands-On Exercise:

 

• Set up ADF environment
• Connect to sample data sources (Azure Blob, SQL Database)

 

 Day 2: Data Integration and Transformation

 

Module 4: Building Pipelines

 

• Pipeline design and structure
• Control flow activities
• Parameterization and dynamic content

 

Module 5: Data Movement Activities

 

• Copy Activity deep dive
• Supported data sources and sinks
• Incremental data loading

 

Module 6: Data Transformation in ADF

 

• Mapping Data Flows
• Wrangling Data Flows
• Integration with Azure Databricks / SQL

 

Hands-On Exercise:

 

• Build end-to-end pipeline (source → transformation → destination)
• Implement data transformations using Data Flows

 

 

Day 3: Advanced Topics and Optimization

 

Module 7: Scheduling and Triggering Pipelines

 

• Schedule triggers
• Event-based triggers
• Tumbling window triggers

 

Module 8: Monitoring and Troubleshooting

 

• Monitoring pipelines and activities
• Debugging pipelines
• Logging and alerts

Module 9: Security and Governance

 

• Role-based access control (RBAC)
• Managed identities
• Secure data access

 

Module 10: Performance Optimization and Best Practices

 

• Pipeline performance tuning
• Cost optimization strategies
• Design patterns and reusable components

 

Module 11: Integration with Azure Ecosystem

 

• Integration with Azure Synapse Analytics
• Integration with Power BI
• Data lake architectures

 

Final Hands-On / Capstone:

 

• Design and implement a complete data pipeline solution
• Apply monitoring, triggers, and optimization