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Hadoop is an open-source, distributed computation framework designed for dispensation and storing large volumes of data across clusters of commodity hardware. It is a key technology in the field of big data analytics and is capable of handling massive datasets with scalability and fault tolerance. The project is preserved by the Apache Software Substance.
At its core, Hadoop consists of two primary components:
Hadoop Distributed Folder System (HDFS): HDFS is a spread file system that provides a scalable and reliable storage solution for large amounts of data. It breaks down files into slighter blocks and distributes them across numerous nodes in a Hadoop cluster, enabling parallel processing.
MapReduce: MapReduce is a programming model and meting-out engine for distributed data processing. It allows developers to write plans that can process vast amounts of data in parallel across a Hadoop cluster. The MapReduce model involves two main phases Map, which processes and filters data, and Reduce, which aggregates and summarizes the results.
Hadoop is highly valuable for organizations dealing with enormous datasets, such as those in web analytics, social media, and scientific research. Its distributed and fault-tolerant nature makes it suitable for processing and investigating data on a massive scale, providing insights that would be challenging or impossible with traditional databases or computing systems.
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