Master the process of designing, building, monitoring, and optimizing data pipelines with Microsoft Azure Data Factory (ADF) in this hands-on course. Through four progressive modules, you'll develop practical skills in configuring Azure Data Factory, connecting source and destination datasets, creating copy activities, and deploying end-to-end data integration workflows.

You'll learn how to build and schedule pipelines, configure datasets and triggers, monitor pipeline execution, analyze performance, and troubleshoot failures using Azure Data Factory's monitoring and diagnostic capabilities. The course also explores Azure Data Lake integration, dynamic datasets, pipeline parameterization, and input validation to help you create scalable and reliable data integration solutions. Designed for data professionals, data engineers, and cloud practitioners, this course combines foundational concepts with practical implementation. Interactive exercises, scenario-based activities, and graded assessments reinforce each stage of the learning process, enabling you to apply Azure Data Factory features with confidence. By the end of the course, you'll be able to construct production-ready data pipelines, automate workflow execution, diagnose pipeline issues, and optimize data integration processes using Azure Data Factory. Whether you're building your first ADF pipeline or strengthening your cloud data integration skills, this course provides a structured, hands-on path to mastering Azure Data Factory.