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Distributed data processing with Hadoop, Part 2: Going further
Distributed data processing with Hadoop, Part 2: Going further
Learn advanced setup that uses multiple nodes for parallel processing. It demonstrates the various node types required for multinode clusters and explores MapReduce functionality in a parallel environment. This article also digs into the management aspects of Hadoopóboth command line and Web based.
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Distributed data processing with Hadoop
Distributed data processing with Hadoop
This article explores the Hadoop framework, including its fundamental elements, such as the Hadoop file system (HDFS), and node types that are commonly used. Learn how to install and configure a single-node Hadoop cluster, and delve into the MapReduce application. Finally, discover ways to monitor and manage Hadoop using its core Web interfaces.
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Installing and Using Hadoop
Installing and Using Hadoop
Learn to install configure and use the open source Hadoop framework from the Apache Foundation. Basic installation, configuration, executing jobs, and using the HDFS filesystem and the Web GUI.
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Cloud computing with Linux and Apache Hadoop
Cloud computing with Linux and Apache Hadoop
This article shows you how to use Apache Hadoop to build a MapReduce framework to make a Hadoop Cluster and how to create a sample MapReduce application which runs on Hadoop. You will also learn how to set up a time/disk-consuming task on the cloud.
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Distributed computing with Linux and Hadoop
Distributed computing with Linux and Hadoop
Every day people rely on search engines to find specific content in the many terabytes of data that exist on the Internet, but have you ever wondered how this search is actually performed? One approach is Apache's Hadoop, which is a software framework that enables distributed manipulation of vast amounts of data. This article introduces the Hadoop framework and shows you why it's one of the most important Linux-based distributed computing frameworks.
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