This document introduces Big Data and its challenges, highlighting Hadoop as a scalable solution for distributed storage and parallel processing. It explains HDFS (Hadoop Distributed File System) for fault-tolerant storage, MapReduce for distributed computing, and YARN for resource management. Hadoop follows a Master-Slave architecture, where the Master Node (JobTracker, NameNode) assigns tasks, and Slave Nodes (TaskTrackers, DataNodes) process data. The document details the MapReduce workflow, from mapping, sorting, shuffling, and reducing. Real-world applications, including its adoption by Facebook, Amazon, and IBM, are discussed. It also touches on Hadoop deployment on AWS EMR for cloud-based big data processing.