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Map-reduce and Hadoop provide new scaling opportunities for analyzing data. As a result organizations are beginning to analyze and derive business value from large amounts of data that, in many cases, were previously simply being discarded. In some cases such as on-line advertising, the ability to analyze these previously impenetrable volumes of data have disrupted entire industries such as is the case with on-line advertising.
Such green field opportunities are rare, however, and few companies can afford to build an entirely new analytics pipeline. Integrating big data analytics systems like Apache Hadoop into existing analytics systems can be very difficult, however, because there are huge differences in the fundamental approaches being taken to the basic problems of how data should be accessed and analyzed.
These differences are exactly what makes these new technologies hugely effective, but they are also what makes integration between conventional and new approaches so difficult.
This talk will provide detailed descriptions of how to use new technologies to
These descriptions will be taken from real-life customer situations. Each will describe the problems faced and the solutions that solved these problems.
This session is sponsored by MapR Technologies
Ted Dunning is chief application architect at MapR Technologies and a committer and PMC member for the Apache Mahout project. He contributed to the Mahout clustering, classification, and matrix decomposition algorithms. He was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, and built fraud detection systems for ID Analytics.
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