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With almost everything now online, organizations look at the Big Data collected to gain insights for improving their services. In the analytics process, derivation of such insights requires experimenting-with and integrating different analytics techniques, while handling the Big Data high arrival velocity and large volumes. Existing solutions cover(More)
Co-locating the computation as close as possible to the data is an important consideration in the current data intensive systems. This is known as data locality problem. In this paper, we analyze the impact of data locality on YARN, which is the new version of Hadoop. We investigate YARN delay scheduler behavior with respect to data locality for a variety(More)
Consumable analytics attempt to address the shortage of skilled data analysts in many organizations by offering analytic functionality in a form more familiar to in-house expertise. Providing consumable analytics for Big Data faces three main challenges. The first challenge is making the analytics algorithms run in a distributed fashion in order to analyze(More)
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