Fast and Furious:  Big Data Analytics Meets Hadoop

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Charles Babbage’s 19th century mechanical computer could store 1,000 numbers of 40 decimal digits each.  That’s about 16.7 kilobytes, equivalent to four pages of today’s digitized novel.

In the mid 1980s, a state-of-the-art PC had a 20-megabyte hard disk and one megabyte of random access memory (RAM).  That would have been enough capacity to store seven digital songs (if iTunes had existed back then), but not enough RAM to play one of them.

Today’s Kindle Fire tablet has 8 gigabytes of internal storage, enough to support 80 applications and store 10 movies or 800 songs or 6,000 books – all in a slim handheld device.

Scale that capacity more than 1,000 times over, and you start to approach the entry-level point for an enterprise Hadoop cluster.  Hadoop is an open-source software framework for storing and processing huge data sets on a large cluster of commodity hardware.  Hadoop delivers distributed processing power at remarkably low-cost, making it an effective complement to a traditional enterprise data infrastructure.

 

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