AWS Database and Compute Essentials for Data Engineers

Modern data engineering relies heavily on scalable cloud infrastructure to store, process, and analyze massive datasets. Understanding how to select and configure the right AWS database and compute services is essential for building efficient data pipelines. This text-based course guides you from foundational cloud concepts to designing integrated data architectures. You will learn how to match specific data workloads with the appropriate AWS storage and processing services, ensuring high performance and cost-efficiency. What you'll learn: - Understand core AWS database models, including relational (RDS), NoSQL (DynamoDB), and analytical data warehousing (Redshift). - Configure serverless and managed compute services like AWS Lambda and AWS Glue for automated data processing. - Apply data lakehouse patterns by integrating Amazon S3 storage with Athena for interactive querying. - Design scalable data pipelines that connect compute resources to databases while maintaining security best practices. - Practice writing structured queries and data transformation scripts optimized for cloud-native architectures. The course begins with essential cloud data concepts and terminology before guiding you through step-by-step written walkthroughs and configuration scenarios for key AWS services. It is designed for aspiring data engineers, database administrators, and software developers new to the AWS data ecosystem, with no prior cloud engineering experience required. Start reading today to build your foundational cloud data engineering skills.