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Azure Data Factory Bootcamp: From Beginner to Expert

This course helps you master Azure Data Factory, build scalable pipelines, integrate data, orchestrate workflows, and excel in data engineering roles.

4.9
52 Lessons
7h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
  • An understanding of Azure Data Factory’s fundamentals, benefits, and role in data integration and processing
  • Hand-on experience implementing data pipelines in ADF, including data transformation, copying, masking, and scheduling
  • Proficiency in big data processing techniques using Azure SQL, HDInsight, and Hive and leveraging machine learning capabilities in ADF
  • The ability to connect ADF to various data sources, both on-premises and in the cloud, and perform data profiling and modeling

Learning Roadmap

52 Lessons9 Quizzes3 Assessments

2.

Introduction to Azure Data Factory

Introduction to Azure Data Factory

Explore Azure Data Factory’s integration capabilities, architecture, and real-world applications.

3.

Setting Up an Azure Data Factory Environment

Setting Up an Azure Data Factory Environment

5 Lessons

5 Lessons

Master Azure account setup, storage management, and Data Factory integration for efficient data workflows.

4.

Data Connectivity and Management

Data Connectivity and Management

5 Lessons

5 Lessons

Explore Azure Data Factory’s integration, data quality, and metadata management for effective data workflows.

5.

Creating Data Pipelines in Azure Data Factory

Creating Data Pipelines in Azure Data Factory

7 Lessons

7 Lessons

Understand the essentials of Azure Data Factory for efficient data pipeline creation and management.

6.

Managing and Monitoring Azure Data Factory Pipelines

Managing and Monitoring Azure Data Factory Pipelines

7 Lessons

7 Lessons

Master efficient data management, monitoring, debugging, and version control in Azure Data Factory.

7.

Big Data Integration and Processing

Big Data Integration and Processing

6 Lessons

6 Lessons

Explore Azure’s data management solutions, focusing on relational databases, HDInsight, and big data integration.

8.

Machine Learning and Advanced Analytics

Machine Learning and Advanced Analytics

5 Lessons

5 Lessons

Master machine learning project management and automation using Azure’s integrated tools.

9.

Data Governance and Security

Data Governance and Security

5 Lessons

5 Lessons

Explore data governance, security, and access management in Azure Data Factory.

10.

Azure Data Factory: Best Practices

Azure Data Factory: Best Practices

4 Lessons

4 Lessons

Learn how to design patterns, optimize costs, and ensure scalability in Azure Data Factory.
Certificate of Completion
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Author NameAzure Data Factory Bootcamp:From Beginner to Expert
Developed by MAANG Engineers
Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.
ABOUT THIS COURSE
Azure has become a leading cloud platform. Expertise in ADF opens doors to lucrative roles in data engineering and analytics. This course provides you with the essential skills to master Azure Data Factory. You’ll start with ADF’s core concepts, architecture, and data connectivity to both on-premises and cloud sources. Through hands-on lessons and real-world examples, you’ll learn to create, manage, and monitor pipelines, handle incremental data loads, and work with version control using Git. Advanced topics include big data integration with Azure SQL, HDInsight, and Hive and designing analytics and machine learning workflows. After completing this course, you’ll confidently design, develop, and maintain data pipelines in Azure Data Factory. You’ll be equipped to tackle integration challenges, optimize cost and performance, ensure scalability and high availability, and implement data governance and security—boosting your career prospects in data engineering and analytics.

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