Data Lakes and Distributed Compute on AWS for Beginners

Modern organizations generate massive volumes of data that must be stored cost-effectively and analyzed quickly. Building a scalable repository requires a solid understanding of how storage and distributed processing work together in the cloud. This text-based course guides you through the foundational concepts of data lakes, helping you transition from basic file storage to high-performance analytical environments. You will start by mastering core terminology, architectural pillars, and security fundamentals before moving into hands-on data engineering configurations. By the end of this course, you will understand how to design secure ingestion pipelines, catalog metadata, and structure data to minimize query costs and maximize performance. What you'll learn: - Understand the core architectural pillars of a production-ready cloud data lake - Configure secure data ingestion workflows using modern transfer protocols - Automate metadata management and schema discovery using crawlers - Apply partitioning strategies and columnar formats like Parquet to optimize storage - Process large-scale datasets efficiently using distributed compute frameworks - Implement security best practices, access controls, and data encryption This course begins with fundamental definitions and storage principles, gradually progressing to schema automation, data transformation, and distributed query optimization. You will learn through clear, written explanations, architectural breakdowns, and practical configuration examples. This course is designed for beginner data engineers, cloud practitioners, and database administrators who want to build a strong foundation in cloud-based data lake architecture. No prior experience with distributed computing is required.