Hadoop Architecture
Hadoop’s architecture is designed to support massive parallel processing through its two main components:
- Hadoop Distributed File System (HDFS)
- MapReduce
How Does Hadoop Handle Parallel Processing of Large Datasets Across a Distributed Cluster?
Apache Hadoop is a powerful framework that enables the distributed processing of large datasets across clusters of computers. At its core, Hadoop’s ability to handle parallel processing efficiently is what makes it indispensable for big data applications. This article explores how Hadoop achieves parallel processing of large datasets across a distributed cluster, focusing on its architecture, key components, and mechanisms.
Hadoop processes large datasets across distributed clusters using HDFS to distribute data and MapReduce for parallel processing. It optimizes tasks with data locality, manages resources via YARN, and ensures scalability and fault tolerance through automatic task redistribution among nodes, maximizing efficiency and reliability in data handling.