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.
Enterprise practitioners believe the potential value of Big Data is significant, but many are struggling to derive maximum value from their investments in related technology. While a majority a Fortune 500 companies have Big Data deployments in production, and a significant percentage of mid-sized enterprises have proof-of-concept and pilot projects underway, We estimate that close to half have not realized the level of value anticipated at their onset.
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Big Data Training for FastTrack Session on Apr 5th To 9th 2014