big data online Online Training Course for Global learners

Hadoop is designed to efficiently process large volumes of information by connecting many commodity computers together to work in parallel. The theoretical 1000-CPU machine described earlier would cost a very large amount of money, far more than 1,000 single-CPU or 250 quad-core machines.
Hadoop will tie these smaller and more reasonably priced machines together into a single cost-effective compute cluster.

The advent of the Web, mobile devices and other technologies has caused a fundamental change to the nature of data. Big Data has important, distinct qualities that differentiate it from �traditional� corporate data. No longer centralized, highly structured and easily manageable, now more than ever data is highly distributed, loosely structured (if structured at all), and increasingly large in volume.

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In a recent survey, 46% of Big Data practitioners report that they have only realized partial value from their Big Data deployments. An unfortunate 2% declared their Big Data deployments total failures, with no value achieved.
The three compelling reasons for this struggle to achieve maximum business value form Big Data are:
� A lack of skilled Big Data practitioners.
� �Raw� and relatively immature technology.
� A lack of compelling business use case.

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