AWS Data Engineering: Build and Orchestrate Modern Data Pipelines

In today's data-driven world, organizations rely on robust data pipelines to turn raw information into actionable insights. Building these systems requires a solid grasp of cloud infrastructure, data modeling, and workflow automation. This course guides you through the process of designing and deploying production-ready data platforms on AWS. You will transition from understanding core database paradigms to building modern lakehouse architectures and automating workflows, gaining the practical skills needed to work as a cloud data engineer. What you'll learn: - Model data effectively across relational, document, and graph databases including PostgreSQL, MongoDB, and Neo4j - Build cloud data warehouses using Redshift, designing optimized dimensional schemas and robust ETL pipelines - Implement modern lakehouse architectures with S3, Glue, Iceberg, and Athena using Apache Spark to process data layers - Orchestrate and schedule complex data workflows using Apache Airflow and MWAA - Apply modern data quality validation techniques and basic Infrastructure as Code concepts to ensure pipeline reliability The course begins with foundational database concepts and data modeling strategies before progressing to cloud warehousing, lakehouse architectures, and automated orchestration. Through clear written explanations, architectural breakdowns, and structured code snippets, you will learn how to design end-to-end data systems. This course is designed for aspiring data engineers, software developers, and data analysts who want to transition to cloud-based data engineering. No prior AWS or data engineering experience is required, though a basic understanding of SQL and Python is helpful. Start building your cloud data engineering foundation today.