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.
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.
Hadoop: Hadoop is an open source framework for processing, storing and analyzing massive amounts of distributed unstructured data. Originally created by Doug Cutting at Yahoo!, Hadoop was inspired by MapReduce, a user-defined function developed by Google in early 2000s for indexing the Web. It was designed to handle petabytes and exabytes of data distributed over multiple nodes in parallel.
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Big Data Training for FastTrack Session on Mar 29- Apr 2, 2014