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Published: 28 Oct 2013

Synchronous analytics and asynchronous analytics are distinguished by the way they process data. But they both have big data storage appetites and specialized needs. The term big data analytics has crept into the IT vernacular to represent our fixation on what might be called the "big data assumption" -- the belief that the answers to all our questions are buried in piles of data. Somehow, if we can compare and cross-reference enough data points, we'll gain insights that will help us beat the competition, catch all the crooks and save the world from the brink of disaster. The problem is that all this analysis requires lots of data, and therein lies the challenge for IT: How do you capture, store, access and analyze enough data to garner those insights and justify the resources that have been committed to the task? Big data analytics applications typically use information such as Web traffic, financial transactions and sensor data, instead of traditional forms of content. The value of the data is tied to comparing, associating or referencing it with other ... Access >>>

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