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Hadoop architecture is a fundamental framework in data science and big data that allows for distributed storage and processing of large datasets across clusters of commodity hardware. The architecture is designed with scalability, reliability, and fault tolerance in mind, which makes it capable of handling petabytes of data efficiently. At the core of Hadoop’s architecture lies the Hadoop Distributed File System (HDFS), which organizes data across multiple machines, and the master/slave architecture, which governs how tasks are coordinated between central controllers and worker nodes. Understanding how the NameNode and DataNodes operate within this architecture is essential to appreciating the distributed nature of data management in Hadoop. HDFS is the storage system of Hadoop and is inspired by the Google File System. It is designed to store very large files and provide high throughput access to application data. The data is stored in blocks, with a default block size of 128 MB (although it can be configured to be larger). Each file is divided into blocks, and these blocks are distributed across multiple machines in the cluster. This design ensures parallelism in data access and provides resilience in case of node failures because blocks are replicated across multiple machines. Typically, each block has three replicas stored on different nodes in the cluster. This replication mechanism guarantees data reliability and availability even when hardware failures occur. The master/slave architecture in Hadoop comes into play with the roles of NameNode and DataNodes in HDFS. The NameNode is the master server that manages the file system namespace and controls access to files by clients. It stores metadata, such as the directory tree of all files in the file system, and tracks where across the cluster the blocks of each file are located. However, it does not store the actual ___ ______ ______ ___ ________ ___ _______ _____ ____ _______ ______ _________.
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