Overview

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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.

Syllabus

Getting Started with Azure Data Factory

This module introduces learners to the foundational concepts of Azure Data Factory, including the interface, environment setup, and essential components such as copy operations, blob storage, and dataset creation. It prepares learners to begin working with ADF by configuring source and destination connections.

Developing Pipelines in Azure Data Factory

This module guides learners through the process of building, debugging, and scheduling pipelines within Azure Data Factory. It explores copy activities, the authoring environment, and deploying pipelines using different scheduling strategies.

Monitoring, Debugging, and Data Lake Integration

This module focuses on monitoring pipeline execution, identifying failed runs, debugging issues, and interpreting pipeline behavior. It provides learners with skills to ensure the reliability and accuracy of data processing workflows.

Advanced Data Integration and Optimization

This module covers advanced integration techniques using Azure Data Lake, dynamic datasets, and pipeline parameterization. It emphasizes optimization, dataset configuration, and input validation for complex workflows.