Hadoop, NoSQL and massively parallel analytic databases are not mutually exclusive. Far from it, we believe the three approaches are complimentary to each other and can and should co-exist in many enterprises. Hadoop excels at processing and analyzing large volumes of distributed, unstructured data in batch fashion for historical analysis. NoSQL databases are adept at storing and serving up multi-structured data in near-real time for web-based Big Data applications. And massively parallel analytic databases are best at providing near real-time analysis of large volumes of mainly structured data.
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.
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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Big Data Training for FastTrack Session on Mar 29- Apr 2, 2014