This specialization 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 specialization.
This specialization prepares you to become an AWS data engineer by teaching you to build data pipelines, process large datasets, and manage workflows using key AWS services. You’ll start with core data engineering concepts, then dive into AWS Glue for ETL and Amazon Redshift for data warehousing. Learn streaming with Kinesis and MSK, big data processing on EMR, and scalable data lakes with Lake Formation. You’ll query data using Athena, visualize insights in QuickSight, and orchestrate pipelines with Step Functions and AppFlow. Migration tools like DMS, DataSync, and Snow Family are also covered. Designed for data professionals and cloud engineers with basic AWS and data knowledge, this hands-on program is ideal for intermediate learners.
By the end of the specialization, you will be able to design, build, and optimize scalable data pipelines, implement real-time analytics, manage large-scale data migrations, and deploy end-to-end data engineering workflows on AWS.
Applied Learning Project
Throughout this specialization, learners engage in hands-on projects such as configuring Redshift clusters, building Glue ETL pipelines, processing real-time data streams with Kinesis and Flink, and orchestrating workflows using AWS Step Functions. Additional projects include designing secure data lakes with Lake Formation and creating visual dashboards with QuickSight. These practical experiences simulate real-world challenges and reinforce concepts through applied learning across AWS analytics services.
















