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

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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.

Azure Data Factory Bootcamp: From Beginner to Expert

**(Select all that apply.)**
Which feature is available when debugging an Azure Data Factory pipeline?

(Select all that apply.)
Which feature is available when debugging an Azure Data Factory pipeline?

To only load new data from the source system

What is the purpose of incremental loads in Azure Data Factory?

What is the purpose of incremental loads in Azure Data Factory?

**(Select all that apply.)**
What option(s) represent the types of incremental loads in Azure Data Factory?

(Select all that apply.)
What option(s) represent the types of incremental loads in Azure Data Factory?

A dependency within the same data factory

What is an internal dependency when managing dependencies in Azure Data Factory?

What is an internal dependency when managing dependencies in Azure Data Factory?

To backup data pipelines in case of failure

To synchronize data between different systems

To manage access control to data pipelines

To track changes to data pipeline definitions over time

What is the purpose of version control in Azure Data Factory?

What is the purpose of version control in Azure Data Factory?

**(Select all that apply.)**
Which tool(s) can be used to manage Azure Data Factory pipelines with version control?

(Select all that apply.)
Which tool(s) can be used to manage Azure Data Factory pipelines with version control?

How can auditing and logging be enabled in Azure Data Factory?

How can auditing and logging be enabled in Azure Data Factory?

To monitor pipeline performance and diagnose issues

What is the purpose of auditing and logging in Azure Data Factory?

What is the purpose of auditing and logging in Azure Data Factory?

Captures only the changed data in a source system

Supports both batch and real-time data processing

Automatically creates a backup of the source system

Can be used to synchronize data between different systems

**(Select all that apply.)**
Which option(s) represents the features of change data capture in Azure Data Factory?

(Select all that apply.)
Which option(s) represents the features of change data capture in Azure Data Factory?

Better performance for large data pipelines

Improved version control and collaboration for data pipelines

Reduced costs for data pipeline deployment

What is the benefit of using GitHub with Azure Data Factory?

What is the benefit of using GitHub with Azure Data Factory?

Test your understanding of data pipeline management in Azure Data Factory

Getting Started

Introduction to Azure Data Factory

Setting Up an Azure Data Factory Environment

Data Connectivity and Management

Azure Data Factory: Introduction and Connectivity Exam

Creating Data Pipelines in Azure Data Factory

Managing and Monitoring Azure Data Factory Pipelines

Azure Data Factory: Designing and Maintaining Data Pipelines Exam

Big Data Integration and Processing

Machine Learning and Advanced Analytics

Azure Data Factory: Big Data Processing and Machine Learning Exam

Data Governance and Security

Azure Data Factory: Best Practices

Conclusion

Appendix

Quiz on ADF Data Pipeline Management