Apache Hadoop’s Second Generation: Performance and Resource Management through YARN

Abstract

The rapid expansion of digital technologies has resulted in an enormous growth of data generated by individuals, organizations, and technological systems. This large and complex collection of data is commonly referred to as Big Data. Big Data typically involves datasets that are too large, fast-growing, or complex to be processed efficiently using traditional data management systems and conventional analytical tools. As a result, organizations increasingly rely on advanced technologies capable of storing, managing, and analysing such massive volumes of information.

One of the most widely used technologies designed to address Big Data challenges is Hadoop. Hadoop is an open-source framework that enables the distributed storage and processing of large datasets across clusters of computers. Its architecture allows data to be stored and processed in parallel, thereby improving scalability, reliability, and efficiency. A key component of Hadoop is the MapReduce programming model, which divides large data processing tasks into smaller subtasks that can be executed simultaneously across multiple computing nodes. This distributed approach significantly enhances the speed and efficiency of data processing.

Furthermore, the Hadoop ecosystem has evolved with the introduction of YARN (Yet Another Resource Negotiator), which serves as the next generation resource management platform for Hadoop. YARN separates resource management from data processing, enabling multiple data processing frameworks to run on the same cluster. This paper provides an overview of Big Data, Hadoop architecture, the MapReduce model, and the role of YARN in modern data processing systems.