This intensive bootcamp delivers hands-on mastery in AWS data engineering through 15 real-world labs that simulate complex business pipelines. You’ll start by setting up your environment using Docker and AWS CLI, then dive into batch processing with Airflow and Redshift, distributed processing with PySpark and DynamoDB, and automated ETL using Glue and Step Functions. Each project is designed to reinforce data orchestration, workflow automation, and scalable processing. As you progress, you’ll build data lakes with EMR and Athena, construct event-driven systems using ECS, Lambda, and Kinesis, and visualize streaming data with Streamlit dashboards. Real-time pipelines and CI/CD deployment with GitHub Actions add production-level skills to your toolkit. The course structure emphasizes practical applications, allowing you to architect, deploy, and manage end-to-end AWS data solutions. By completing five advanced assignments, you’ll solidify concepts and create a robust cloud-native portfolio. This course is built for professionals ready to apply cloud and Python skills to real engineering challenges.