Introduction to Data Engineering on AWS

About this course

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you'll gain a comprehensive understanding of data engineering using AWS Glue and Redshift, two critical tools for modern data workflows. You will be equipped with the skills to manage and transform data at scale, from cataloging and processing with AWS Glue to leveraging Redshift for powerful data warehousing and analytics. By diving into hands-on tutorials, you'll learn the core concepts and practical applications necessary to streamline data pipelines and optimize query performance. As you progress through the course, you will explore a variety of AWS Glue features such as Data Catalogs, ETL development, job bookmarking, and data quality evaluation, empowering you to automate data workflows and manage large datasets effectively. With Amazon Redshift, you will learn how to configure clusters, optimize queries, and even work with Redshift Spectrum and Serverless, improving the scalability and efficiency of your data operations. This course is ideal for data professionals looking to enhance their cloud-based data engineering skills, especially those who want to integrate AWS Glue and Redshift into their existing systems. It is suitable for learners with a basic understanding of data analytics, but prior knowledge of AWS or data engineering concepts would be beneficial. The course is designed for both beginners and intermediate learners, offering a solid foundation and practical skills that can be applied in real-world data engineering roles. By the end of the course, you will be able to build and optimize ETL pipelines using AWS Glue, manage data workflows, configure Redshift clusters, optimize query performance, and deploy serverless Redshift for scalable data warehousing solutions.

Course objectives

• equip learners with practical skills in data engineering on AWS
• enhance understanding of data management and transformation at scale
• prepare data professionals for integrating AWS tools into their workflows