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Coursera

Microsoft Azure - Data Factory

EDUCBA via Coursera

Overview

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Build practical expertise in designing, managing, and optimizing data pipelines with Microsoft Azure Data Factory (ADF). Across four progressive modules, you’ll move from foundational setup to advanced data integration by configuring ADF, Azure Blob Storage, SQL schemas, source and sink datasets, and copy activities. Designed for data professionals, data engineers, and cloud practitioners, this hands-on course guides you through building, debugging, deploying, and scheduling end-to-end pipelines. You’ll automate workflow execution with triggers, monitor pipeline runs and performance metrics, interpret execution logs, and diagnose failures using ADF monitoring and diagnostic features. You’ll also integrate Azure Data Lake, create dynamic datasets, parameterize pipelines, and validate inputs to support scalable, reliable workflows. Interactive exercises, scenario-based activities, and graded assessments reinforce each stage of the learning process. By the end of the course, you’ll be able to construct production-ready data pipelines, automate execution, assess data integrity, troubleshoot pipeline issues, and optimize cloud data integration processes. Whether you’re creating your first ADF pipeline or strengthening existing Azure skills, this course offers a structured path from setup through advanced pipeline optimization.

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.

Taught by

EDUCBA

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4.5 rating at Coursera based on 24 ratings

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