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Learn Hadoop Administration from best Institutes in Udaipur. Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of commodity hardware nodes, and to handle thousands of terabytes of data. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating in case of a node failure. This approach lowers the risk of catastrophic system failure and unexpected data loss, even if a significant number of nodes become inoperative. Consequently, Hadoop quickly emerged as a foundation for big data processing tasks, such as scientific analytics, business and sales planning, and processing enormous volumes of sensor data, including from internet of things sensors.
Hadoop was created by computer scientists Doug Cutting and Mike Cafarella in 2006 to support distribution for the Nutch search engine. It was inspired by Googles MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts, which are also called fragments or blocks, can be run on any node in the cluster. After years of development within the open source community, Hadoop 1.0 became publically available in November 2012 as part of the Apache project sponsored by the Apache Software Foundation. Organizations can deploy Hadoop components and supporting software packages in their local data center. However, most big data projects depend on short-term use of substantial computing resources. This type of usage is best-suited to highly scalable public cloud services, such as Amazon Web Services (AWS), Google Cloud Platform and Microsoft Azure. Public cloud providers often support Hadoop components through basic services, such as AWS Elastic Compute Cloud and Simple Storage Service instances. However, there are also services tailored specifically for Hadoop-type tasks, such as AWS Elastic MapReduce, Google Cloud Dataproc and Microsoft Azure HDInsight. The Hadoop frameworks comprised of two main core components i.e. HDFS and MapReduce framework. Hadoop framework divides the data into smaller chunks and stores each part of the data in the separate node within the cluster. By doing this, the time frame to storing the data onto the disk significantly reduces. In order to provide high availability, Hadoop replicates each part of data on to other machines that are present within the cluster. The number of copies it replicates depends on the replication factor. The advantage of distributing this data across the cluster is that while processing the data it reduces the lot of time as this data can be processed simultaneously. The Figure shows the Hadoop working model for 4TB of data in 4 nodes of the Hadoop cluster.
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