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Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives (Anglais) Relié – 7 mai 2014


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Présentation de l'éditeur

Master alternative Big Data technologies that can do what Hadoop can't: 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 isn't 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.

Biographie de l'auteur

DR. VIJAY SRINIVAS AGNEESWARAN
(Bangalore, India) is currently Director Technology/Principal Architect as head of Big Data R&D at Impetus. His R&D focuses on Big Data governance, batch and real-time analytics, and paradigms for implementing machine learning algorithms for Big Data. A professional member of ACM and the IEEE for more than 8 years, he was recently elevated to IEEE Senior Member. He has filed patents with US, European and Indian patent offices, holds two issued US patents, and has published in IEEE Transactions and other leading journals, and has been an invited speaker at multiple national and International conferences, including O’Reilly’s Strata Big Data Series.



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6 internautes sur 7 ont trouvé ce commentaire utile 
Large price for little return 14 juillet 2014
Par Damon B. - Publié sur Amazon.com
Format: Relié Achat vérifié
This book seems to be half-done. There are several well-written overviews, but the in-depth portion(s) of the book are not yet complete. It seems as though this was a graduate paper that was hastily turned into a technical overview. I would wait for the author to finish the book before paying such a hefty sum.
2 internautes sur 2 ont trouvé ce commentaire utile 
A good overview 17 septembre 2014
Par AOL Jack - Publié sur Amazon.com
Format: Relié Achat vérifié
This book is a good academic overview of some of the newer big data technologies. It is not going to enough to teach you how to use those technologies. But it will give you a good idea how they can be used. I would have liked more detail.
I very much enjoyed this book and have been referring to it both ... 31 octobre 2014
Par A. Jaokar - Publié sur Amazon.com
Format: Relié
I wanted to do a longer review of this book for my blog(opengardensblog) - but here is a short comment. I very much enjoyed this book and have been referring to it both in my professional capacity and also in my teaching (at Oxford and UPM). As the title says - it is 'beyond hadoop' .. and in that sense, expects a certain familiarity with the subject in the first place. It covers this task of 'beyond hadoop' very well for practitioners. I especially found the breadth very useful ex coverage of Spark, Storm, BDAS etc. My own interest lies in Real time and IoT (which is also in the beyond hadoop realm) and it was well covered (Ch 4 Realizing Machine Learning Algorithms in Real time). My students have also found the early chapters useful(Chapter 2 - Understanding the BDAS stack) and Ch 3 - Realizing Machine learning algorithms in Spark. So, overall - I would say .. If you know a bit of Hadoop and if you want to save yourselves a lot of time to understand the roadmap beyond - this is a great book from a practitioners perspective
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