The first article in this series showed how to use Hadoop in a single-node cluster. This article continues with a more 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 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.

Hadoop is a distributed computing platform that provides a framework for storing and processing petabytes of data. Because it is Java-based, Hadoop runs on Linux, Windows, Solaris, BSD, and Mac OS X. Hadoop is widely used in organizations that demand a scalable, economical (read commodity hardware), efficent, and reliable platform for processing vast amounts of data.

Hadoop is a library for writing distributed data processing programs using the MapReduce framework. It's got all the makings of a blogosphere hit: cluster computing, large datasets, parallelism, algorithms published by Google, and open source.

Google has granted a license for a recently granted MapReduce process patent to the Apache Software Foundation for the Apache Hadoop open source framework for distributed computing

Last month’s Hadoop Summit in Santa Clara, California, saw a number of interesting developments around this increasingly hot open source project…

Cloudera is releasing a Linux distro for Hadoop, the cloud-computing technology behind Facebook, Google and others. Cloudera's Hadoop is aimed at regular enterprise data centers.

Apache's Cassandra, the "NoSQL" distributed data store, has been updated to version 0.6, adding support for Apache Hadoop, integrated caching and improved performance

Blue Cloud itself consists of Xen and PowerVM virtualized Linux operating-system images and Hadoop parallel workload scheduling, all supported by IBM Tivoli server-management software.