Inside the Briefcase

The 5 Most Common Application Bottlenecks

The 5 Most Common Application Bottlenecks

Application bottlenecks can lead an otherwise functional computer or...

How Machine Learning Helps Improve the Security Industry

How Machine Learning Helps Improve the Security Industry

We’ve been moving more and more towards computerized processes...

Transformation on a Global Scale

Transformation on a Global Scale

Necessity may be the mother of invention, but it’s...

IT Briefcase Exclusive Interview: As Container Adoption Swells, So Do Security Concerns

IT Briefcase Exclusive Interview: As Container Adoption Swells, So Do Security Concerns

Fei Huang, NeuVector
In this Fresh Ink interview segment,...

6 Marketing Strategies for Your Small Business

6 Marketing Strategies for Your Small Business

One of the main problems facing small businesses is...

Large Scale Analytics in the Enterprise

August 9, 2012 No Comments

SOURCE: Think Big Analytics

The growth of Internet businesses led to a whole new scale of data processing
challenges. Companies like Google, Facebook, Yahoo, Twitter, and Quantcast now
routinely collect and process hundreds to thousands of terabytes of data on a daily basis.
This represents a significant change in the volume of data which can be processed, a
major reduction in processing time required, and of the cost required to store data. The
most important of the techniques used at these companies is storing data in a cluster of
servers and using a distributed data processing technique Google invented, called
MapReduce. Facebook, Yahoo, Twitter, and Quantcast all process data with an open
source technology implementation of MapReduce called Hadoop.

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