Big Data Engineering Foundations with Hadoop and Spark

As the volume of global data continues to grow, organizations rely on robust data pipelines to process and analyze information at scale. Understanding how to manage this massive influx of data is one of the most valuable skills in modern technology.\n\nThis text-based course guides you from the absolute basics of big data architecture to building your first scalable processing pipelines. You will gain a clear, conceptual understanding of distributed computing and learn how to write code to process, query, and stream datasets using industry-standard tools.\n\nWhat you'll learn:\n- Understand foundational big data concepts, distributed storage, and the core architecture of Hadoop\n- Write Scala and Spark programs to clean, transform, and analyze large-scale datasets\n- Configure Kafka pipelines to ingest and stream real-time data feeds\n- Explore modern data lakehouse architectures and structured streaming concepts for up-to-date data engineering\n- Query distributed data efficiently using Spark SQL and manage cluster resources\n- Practice your skills with written exercises and code walk-throughs designed for local execution\n\nYou will begin with essential definitions and the history of distributed systems before moving step-by-step through Hadoop, Spark, Scala, and Kafka. Each module combines clear written explanations with practical code snippets that you can read, analyze, and adapt.\n\nThis course is designed for aspiring data engineers, analysts, and software developers who are new to big data. No prior experience with distributed systems is required, though a basic familiarity with programming concepts is helpful.\n\nStart reading today to build your foundational knowledge of big data engineering.