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Hadoop is an open-source framework designed to store and process vast amounts of data efficiently across clusters of computers. Its architecture is primarily based on a master/slave model, which allows it to scale horizontally and handle Big Data workloads effectively. At the core of Hadoop is the Hadoop Distributed File System (HDFS), which is optimized for high-throughput access to large datasets and provides fault tolerance through data replication. HDFS is designed to store files across multiple machines in a cluster, ensuring that data is available even if some nodes fail, making it particularly suitable for data-intensive applications in Data Science and Big Data analytics. The Hadoop architecture consists of two main layers: HDFS for storage and the MapReduce or YARN layer for processing. The master/slave architecture of HDFS is fundamental to how Hadoop manages and accesses data. In this architecture, the master node, called the NameNode, is responsible for managing the metadata of the file system. This metadata includes information about the directory structure, file permissions, and the mapping of data blocks to DataNodes. The NameNode does not store the actual data itself; instead, it keeps track of where each piece of data is stored across the cluster. On the other hand, the slave nodes, called DataNodes, are responsible for storing the actual data. Each file in HDFS is split into fixed-size blocks, typically 128 MB or 256 MB, and each block is replicated across multiple DataNodes to ensure fault tolerance. By default, __________ ___ _________ __________ ___ ___ ______ ___ ________ ___ ________ __________.
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