Microsoft Azure - Data Factory

Overview

This comprehensive course empowers learners to construct, implement, monitor, and optimize data pipelines using Microsoft Azure Data Factory (ADF). Structured into four progressive modules, the course starts with foundational setup and connectivity, advancing to robust pipeline design, scheduling, debugging, and performance optimization using Azure Data Lake integration. Learners will configure source and destination datasets, execute copy activities, and deploy end-to-end workflows with precision. Through practical exercises, graded quizzes, and scenario-based tasks, learners will also diagnose failures, analyze pipeline behavior, and evaluate dataset and trigger configurations. By the end of the course, participants will be proficient in building dynamic, scalable, and production-ready data integration solutions using ADF. Designed for data professionals, engineers, and cloud practitioners, this course bridges theory with cloud-based implementation—helping learners transition from foundational concepts to enterprise-grade automation.

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.