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 ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

After a brief review of how map-reduce works, we shall look at the trade-off that needs to be made when designing map-reduce algorithms for problems that are not embarrassingly parallel. In particular ...

A new data science learning resource is about to commence, brought to you by Stanford University via Coursera: Mining Massive Datasets. This class teaches algorithms for extracting models and other ...

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 ...

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 ...

Turning an entire paradigm on its head, an international group of researchers has figured out how to implement cloud computing’s most widely-used algorithm, one that’s usually deployed in giant, ...