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Hadoop architecture is a distributed computing framework designed to process and store vast volumes of structured, semi-structured, and unstructured data. It is built on a master/slave architecture and is fundamentally centered around the Hadoop Distributed File System (HDFS) for storage and the MapReduce programming model for processing. The goal of Hadoop is to provide scalability, reliability, and fault tolerance in handling large datasets across clusters of commodity hardware. Understanding its architecture, particularly the role of HDFS and the functioning of the NameNode and DataNodes, is crucial for appreciating how it enables the efficient management of big data. HDFS is the backbone of Hadoop’s storage layer. It is inspired by Google File System (GFS) and is designed to store extremely large files, typically gigabytes to terabytes in size, by distributing them across many machines. The key idea of HDFS is to divide files into blocks and replicate those blocks across multiple machines to ensure fault tolerance and high availability. A standard block size in HDFS is 128 MB (though it can be configured), and each block is typically replicated three times by default across the cluster. This replication mechanism ensures that if one node fails, the data can still be retrieved from other nodes. The architecture of HDFS follows the master/slave model. At the center of the system lies the master node, known as the NameNode, which manages the ____ ___ ___ _________ ________ ______ _____ ______ ________ ___ ____.
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