MapReduce was invented by Google in 2004, made into the Hadoop open source project by Yahoo! in 2007, and now is being used increasingly as a massively parallel data processing engine for Big Data.

When the Big Data moniker is applied to a discussion, it’s often assumed that Hadoop is, or should be, involved. But perhaps that’s just doctrinaire. Hadoop, at its core, consists of HDFS (the Hadoop ...

Google introduced the MapReduce algorithm to perform massively parallel processing of very large data sets using clusters of commodity hardware. MapReduce is a core Google technology and key to ...

Aster Da ta, which provides data management and data processing platform for big data analytic applications, today announced the delivery of over 30 ready-to-use advanced analytic packages and more ...

Hadoop has been widely embraced for its ability to economically store and analyze large data sets. Using parallel computing techniques like MapReduce, Hadoop can reduce long computation times to hours ...

This implementation is intended for illustration purposes only and the examples lack exception handling acceptable for production systems. Beyond showcasing an implementation of the MapReduce concept, ...

When Hadoop first started gaining attention and early adoption it was inseparable – both technologically and rhetorically – from MapReduce, its then-venerable big data-processing algorithm. But that’s ...

When your data and work grow, and you still want to produce results in a timely manner, you start to think big. Your one beefy server reaches its limits. You need a way to spread your work across many ...

Finding frequent itemsets is one of the most important fields of data mining. Apriori algorithm is the most established algorithm for finding frequent itemsets from a transactional dataset; however, ...

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