Foundations of Big Data: Processing with Apache Hadoop and Spark

As organizations generate massive volumes of information, traditional databases struggle to keep up. This text-based course introduces you to the essential concepts of distributed storage and parallel processing, giving you a clear path to understanding how modern big data systems operate. You will transition from learning basic terminology to understanding how massive datasets are managed and analyzed across computer clusters. Learn to write data processing pipelines, run queries, and manage resources efficiently. What you'll learn: Understand the foundational architecture of distributed systems including HDFS and MapReduce; Write Spark applications using PySpark to filter, aggregate, and transform large-scale data; Query big data efficiently using Spark SQL for structured analysis; Apply modern data lakehouse concepts to maintain data reliability and performance; Configure and manage cluster resources using YARN. This course begins with essential definitions and architectural overviews before guiding you through written code examples and conceptual data pipelines. It is designed specifically for beginners, data analysts, and aspiring data engineers with no prior experience in distributed systems. Start reading today to build a solid foundation in modern big data engineering.