What you'll learn

Processing today’s massive, fast-moving datasets is challenging because single machines can’t keep up with the scale, speed, or complexity of modern data workloads. In this course, Big Data Foundations: Distributed Processing Principles, you’ll gain the ability to understand how distributed systems scale computation, manage data across clusters, and deliver reliable performance. First, you’ll explore why distributed processing is necessary and how core concepts like parallelism, coordination, and fault tolerance work. Next, you’ll discover the foundational execution models used in big data systems, including batch, streaming, and DAG-based processing. Finally, you’ll learn how distributed engines achieve scalability and resilience through partitioning, data locality, checkpointing, retries, and key performance optimizations. When you’re finished with this course, you’ll have the skills and knowledge of distributed processing principles needed to design, evaluate, and reason about modern big data architectures.