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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 ___ _______ __________ __________ _________ ______ __________ _____ ________ ___ __________.
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