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August 2013, Volume 1, Number 4

It takes more than technology to achieve big data success

Zions Bancorporation gathers huge amounts of data each day -- customer details and information about online deposits and withdrawals, for example -- then feeds it all into a 1.2-peta-byte-and-growing Hadoop-based repository. The records are then analyzed to uncover anomalous patterns that may indicate fraud, theft or other criminal activity. But it takes a lot more than headline-grabbing technology like Hadoop -- the Apache Software Foundation's popular distributed processing framework -- and related software to turn vast amounts of structured and unstructured data into insight, and that insight into action. The problem begins with big data itself. In many cases, it is in fact big -- vastly, hugely, mind-bogglingly big, as sci-fi writer Douglas Adams might put it. And it often consists of more than conventional transaction data -- system and network logs, sensor data from industrial equipment, social network posts and other text data. Then comes the challenge of spotting glimmers of useful info amid that enormous space and ...

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