Google and its MapReduce framework may rule the roost when it comes to massive-scale data processing, but there’s still plenty of that goodness to go around. This article gets you started with Hadoop, ...

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 ...

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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.

Reporting and analysis tools help businesses make better quality decisions faster. The source of information that enables these decisions is data. There are broadly two types of data: structured and ...

What are some of the cool things in the 2.0 release of Hadoop? To start, how about a revamped MapReduce? And what would you think of a high availability (HA) implementation of the Hadoop Distributed ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Leann Chen explains how knowledge graphs ...

Hadoop has been known as MapReduce running on HDFS, but with YARN, Hadoop 2.0 broadens pool of potential applications Hadoop has always been a catch-all for disparate open source initiatives that ...

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 ...

With the latest update to its Apache Hadoop distribution, Cloudera has provided the possibility of using data processing algorithms beyond the customary MapReduce, the company announced Tuesday.