Big Data Processing with Hadoop and Spark

Master the tools and techniques that power large-scale data processing and analytics. This course introduces the principles and frameworks of Big Data Processing with Hadoop and Spark, enabling learners to manage, process, and analyze massive datasets efficiently. You’ll start by understanding the Hadoop ecosystem, including HDFS and MapReduce, and how distributed storage and computation work together to handle data at scale. Then, you’ll explore Apache Spark, a powerful framework for fast, in-memory data processing and real-time analytics. Through guided exercises and case studies, you’ll learn how to build scalable data pipelines, optimize performance, and apply transformations for business insights. By the end of this course, you’ll be equipped to handle complex data workloads using industry-standard big data tools. Ideal for aspiring data engineers, analysts, and developers, this course bridges data management and cloud computing—preparing you to design, implement, and manage big data solutions that drive intelligent decision-making in modern organizations

• Explain how Hadoop and Spark enable large-scale data processing
• Build and manage distributed data pipelines with Hadoop
• Implement in-memory and real-time analytics using Spark
• Apply big data tools for scalable applications

• Basic familiarity with the topic and terminology
• Readiness for applied exercises and case-based learning