BigDataFr recommends: The power behind Apache Spark

« Building robust analytics applications requires careful planning, many iterations and fine tuning. Imagine reusing and dynamically updating a basic set of models more quickly than ever to address a broad set of requirements with a simple tag. For example, when developing a watch list of terms to monitor on social media, how quickly can you change the watch list or expand the data set? Or what if you developed a facial recognition application for a cloud platform to spot your dog in family photos? Could you quickly change the subject of the photos to your spouse or in-law?

Data scientists need advanced solutions to make these analytics artifacts a reality. This need is driven in large part by the explosion of unstructured text that is quite likely to hit 40 zettabytes by 2020. Apache Spark is one of the most important innovations in this regard, and it’s rapidly maturing into a power tool for development of machine learning–driven analytics applications in the world of big data.

Sparking a revolution

One recent maturation milestone from more than a year ago was the elevation of Spark to top-level status by the Apache Software Foundation. Initially developed at the University of California, Berkeley’s AMPLab starting in 2009, Spark has not yet achieved widespread commercial adoption. Nevertheless, a growing number of organizations have put Spark into production in their advanced analytics environments. » […]
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By James Kobielus, Big Data Evangelist, IBM
Source: http://www.ibmbigdatahub.com