Foundations of Hadoop for Big Data Processing

Managing massive datasets requires moving beyond single-machine databases to distributed systems that scale. This course introduces you to the core architecture of distributed storage and computing using Hadoop. You will transition from a traditional database mindset to understanding how to store, manage, and process petabytes of data across computer clusters, preparing you for modern big data engineering roles. What you'll learn: Understand foundational big data concepts, including the challenges of volume, velocity, and variety; Master the architecture of the Hadoop Distributed File System (HDFS) for reliable, fault-tolerant storage; Apply MapReduce programming concepts to process massive datasets in parallel; Configure resource management and job scheduling across clusters using YARN; Integrate traditional Hadoop storage workflows with modern cloud object storage and data lakehouse architectures; Practice querying distributed data using ecosystem tools and modern data pipelines. This text-based course begins with essential big data terminology and architectural definitions before guiding you through practical conceptual scenarios for configuring, querying, and managing distributed clusters. It is designed for absolute beginners to big data, with no prior distributed systems experience required. Start reading today to build your foundation in big data architecture.