Big Data Corporate training (Mar 29th to 2nd Apr 2014)

Big data is data that is too large to process using traditional methods. It originated with Web search companies who had the problem of querying very large distributed aggregations of loosely-structured data. Google developed MapReduce to support distributed computing on large data sets on computer clusters
Hadoop is an Apache project, written in Java and being built and used by a global community of contributors. Yahoo! has been the largest contributor to the project and uses Hadoop extensively across its businesses on 38,000 nodes.

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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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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