Hadoop and MapReduce have long been mainstays of the big data movement, but some companies now need new and faster ways to extract business value from massive — and constantly growing — datasets.

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.

The demand for job skills related to data processing — NoSQL, Apache Hadoop, Python, and a smattering of other such skills — has hit all-time highs, according to statistics collected by tech job site ...

Wikibon Principal Research Contributor Jeff Kelly provides an inclusive basic tutorial of the big data environment, including technologies, skill sets, and use cases, in “Big Data: Hadoop, Business ...

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 ...

Apache’s Hadoop technologies are becoming critical in helping enterprises manage vast amounts of data, with users ranging from NASA to Twitter to Netflix increasing their reliance on the open source ...

Big Data doesn't always involve Hadoop and MapReduce. This is a point I have made before, and I probably won't shut up about it anytime soon. Hadoop is good for a lot, but it has a batch-oriented ...

As 2014 gets into full swing, Hadoop is increasingly being used for applications that are integral to daily business operations. No longer is Hadoop viewed by some organizations as just a platform for ...

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 ...