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 ...

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 ...

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 ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Senyo Simpson discusses how Rust's core ...

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 ...

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, ...

MapR's latest Hadoop distribution includes support for Hadoop 2.2 with YARN, but is also backward compatible with the MapReduce 1.x scheduler, promising organizations a risk-free upgrade path to the ...

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 ...

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 ...

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, ...