Data Engineering on AWS - The Complete Training

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. This course equips learners with the skills to efficiently process and analyze large volumes of data using AWS services. You will gain expertise in streaming data with Amazon Kinesis and Amazon MSK, running big data workloads on Amazon EMR, building data lakes on AWS, and querying data using Amazon Athena. The course is designed to help you develop a deep understanding of AWS tools and best practices for managing data in cloud environments. Through the course, you will explore the fundamentals of streaming data and various AWS services that support real-time analytics, such as Kinesis and MSK. You’ll also dive into building scalable data lakes using AWS Lake Formation and learn how to run big data processing workloads using Amazon EMR, along with optimizing them for cost and performance. Each module builds on the last, allowing you to master streaming, storage, and query operations seamlessly. As you progress, you will learn how to configure and optimize systems for maximum throughput. The course features hands-on exercises and best practices for using AWS tools, ensuring that you develop practical skills for real-world applications. The structure ensures that you understand the foundational concepts before advancing to complex data management and optimization techniques. This course is ideal for data engineers, cloud architects, or anyone looking to advance their skills in AWS data processing. While prior experience with cloud services is helpful, the course is designed for those with an intermediate understanding of data management and analytics. By the end of the course, you will be able to configure AWS services for real-time data processing, set up data lakes, optimize big data workloads on Amazon EMR, and query data efficiently using Amazon Athena.

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.