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

The process of organizing and managing metadata associated with data assets

The process of organizing and managing data assets associated with metadata

The process of organizing and managing data storage infrastructure

Improved data governance, collaboration, data quality, and efficiency

Improved data storage infrastructure, collaboration, data quality, and efficiency

Improved data integration, collaboration, data quality, and efficiency

What are the benefits of data cataloging in ADF?

<p>What are the benefits of data cataloging in ADF?</p>


A feature that allows users to analyze data and generate statistics about its quality

A feature that allows users to store data assets in the cloud

A feature that allows users to create ETL and ELT pipelines

Any location where data is stored, such as a database or a file

Any location where metadata is stored, such as a database or a file

Any location where data is integrated, such as a database or a file

A cloud-based, enterprise-wide data storage service that provides a single place to discover, understand, and manage all of an organization’s data assets

A cloud-based, enterprise-wide data integration service that provides a single place to discover, understand, and manage all of an organization’s data assets

A cloud-based, enterprise-wide metadata catalog that provides a single place to discover, understand, and manage all of an organization’s data assets

Discover, register, and consume data sources

Analyze data and generate statistics about its quality

What can users do with the Azure Data Catalog?

<p>What can users do with the Azure Data Catalog?</p>


A cloud-based analytics service that brings together big data and data warehousing

A cloud-based data integration service for creating ETL and ELT pipelines

A cloud-based data cataloging service for creating ETL and ELT pipelines

Test your understanding of data connections, profiling, data quality, catalog maintenance, and metadata management. 

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: Data Connectivity and Management