Everyone knows that if MapReduce and Hadoop require elite programmers to write programs to analyze data, then the size of the market will be small. A burning question for all Hadoop vendors is ...

The MapReduce design pattern to distribute data processing was introduced by Google in 2004, and came first with a C++ implementation. A new Ruby implementation is now available under the name of ...

Hadoop MapReduce has been widely embraced for analyzing large, static data sets. New technology integrates a stand-alone MapReduce engine into an in-memory data grid, enabling real-time analytics on ...

Service multiple MapReduce users and lines of businesses, as well as potentially other distributed processing needs. Provide flexibility to choose the right storage/file system, based on the specific ...

Google's MapReduce patent raises some troubling questions for software like Hadoop, but it looks unlikely that Google will assert the patent in the near future; Google itself uses Hadoop for its ...

With growth in unstructured big data, RDBMS is inadequate for big data analytics. Know how to use SQL and MapReduce for big data analytics, instead.

Urs Holzle, the senior vice president of technical infrastructure and Google Fellow at Google and Google's eighth employee, announced Google is not using MapReduce anymore. But don't you worry ...

Given the accumulation of DNA sequence data sets at ever-faster rates, what are the key factors you should consider when using distributed and multicore computing systems for analysis?