Hadoop MapReduce is a cornerstone of the Hadoop ecosystem, providing scalable data storage, processing, and resource management. It operates in two key phases: the Map Phase, which processes input data into intermediate key-value pairs, and the Reduce Phase, which aggregates these pairs into the final result. This dual-phased model empowers organizations to split and process petabytes of data across distributed systems, delivering results far faster than traditional methods. Hadoop MapReduce streamlines the processing of large datasets by dividing the task into smaller, manageable chunks and distributing them across multiple nodes, enabling seamless scalability, cost-efficiency, and flexibility across industries. By leveraging parallel computing and batch workflows, it addresses challenges such as fraud prevention in finance and inventory optimization in retail. The framework has transformative applications in various sectors, including e-commerce, financial services, social media, and IoT, where it empowers organizations to harness big data for smarter, faster, and more informed decisions. However, Hadoop MapReduce faces several challenges, including performance limitations, competition from modern frameworks such as Apache Spark, and Java-centric development. By addressing these challenges strategically, organizations can maximize the potential of Hadoop MapReduce while mitigating its limitations.