10 Facts About Apache Hadoop


Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation.

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All the modules in Apache Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.

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Core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System, and a processing part which is a MapReduce programming model.

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The very first design document for the Apache Hadoop Distributed File System was written by Dhruba Borthakur in 2007.

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Small Apache Hadoop cluster includes a single master and multiple worker nodes.

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MapReduce Java

Apache Hadoop distributed file system is a distributed, scalable, and portable file system written in Java for the Apache Hadoop framework.

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Apache Hadoop cluster has nominally a single namenode plus a cluster of datanodes, although redundancy options are available for the namenode due to its criticality.

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Also, Apache Hadoop 3 permits usage of GPU hardware within the cluster, which is a very substantial benefit to execute deep learning algorithms on a Apache Hadoop cluster.

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Theoretically, Apache Hadoop could be used for any workload that is batch-oriented rather than real-time, is very data-intensive, and benefits from parallel processing.

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Apache Hadoop can be deployed in a traditional onsite datacenter as well as in the cloud.

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