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 ...

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 ...

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 ...

This implementation is intended for illustration purposes only and the examples lack exception handling acceptable for production systems. Beyond showcasing an implementation of the MapReduce concept, ...

Finding frequent itemsets is one of the most important fields of data mining. Apriori algorithm is the most established algorithm for finding frequent itemsets from a transactional dataset; however, ...

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 ...

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 ...

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 ...

As the undisputed pioneer of big data, Google established most of the key technologies underlying Hadoop and many of the NoSQL databases. The Google File System (GFS) allowed clusters of commodity ...