Big data analytics beyond hadoop : real time applications with storm spark and more hadoop alternatives Master alternative Big Data technologies that can do what Hadoop cant: real time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today they think of Hadoop. But there are many cutting edge applications that Hadoop isnt well suited for especially real time 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 up to date example code for: Spark the next generation in memory computing technology from UC Berkeley; Storm the parallel real time Big Data analytics technology from Twitter; GraphLab the next generation 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 real time. He concludes by previewing emerging trends including real time video analytics SDNs and even Big Data governance security and privacy issues. He identifies intriguing startups and new research possibilities including BDAS extensions and cutting edge model driven 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. Publishers website