Azure Data Factory (ADF) equips data engineers and IT professionals with the knowledge and practical skills to build, orchestrate, automate, and monitor cloud-based data integration pipelines using Microsoft Azure.
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 Factor
- 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
