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Big Data Analytics Beyond Hadoop: RealTime Applications With Storm, Spark, and More Hadoop Alternatives,Used
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Master alternative Big Data technologies that can do what Hadoop can't: realtime analytics and iterative machine learning.When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cuttingedge applications that Hadoop isn't well suited for, especially realtime analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and uptodate example code for: Spark, the next generation inmemory computing technology from UC Berkeley Storm, the parallel realtime Big Data analytics technology from Twitter GraphLab, the nextgeneration graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo)Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in realtime. He concludes by previewing emerging trends, including realtime video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cuttingedge modeldriven analytics.Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.
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