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 ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...

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 ...

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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, ...

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 ...

Data science is an interdisciplinary sphere of study that has gained traction over the years, given the sheer amount of data we produce on a daily basis — projected to be over 2.5 quintillion bytes of ...

Hadoop is hard. There’s just no way around that. Setting up and running a cluster is hard, and so is developing applications that make sense of, and create value from, big data. What Hadoop really ...

Over at The Data Stack, Intel’s Tim Allen writes that the key to optimizing Hadoop on x86 is to tune the underlying Java so that it takes advantage of capabilities in Intel hardware. When you do that, ...