Scalable Distributed Systems for Big Data Analytics Using Apache Spark and Hadoop

Abstract

The advent of big data has revolutionized how organizations process and analyze vast amounts of information. Apache Hadoop and Apache Spark are two leading frameworks designed to manage and analyze large datasets efficiently through scalable distributed systems. Hadoop, with its MapReduce model and Hadoop Distributed File System (HDFS), introduced a paradigm shift in handling data across clusters of commodity hardware, focusing on fault tolerance and scalability. In contrast, Apache Spark enhances data processing with its in-memory computing capabilities, offering faster performance and support for diverse data processing tasks, including batch processing, interactive queries, real-time streaming, and machine learning. This paper provides a comprehensive analysis of Hadoop and Spark, comparing their architectures, performance characteristics, and use cases. It examines the advantages and limitations of each framework, aiming to guide practitioners in selecting the most suitable tool for various big data applications. By exploring practical applications and performance comparisons, this study contributes to a deeper understanding of how scalable distributed systems can be effectively utilized for big data analytics.