Big Data Analytics Beyond Hadoop

Real-Time Applications with Storm, Spark, and More Hadoop Alternatives

Nonfiction, Science & Nature, Technology, Operations Research, Business & Finance, Management & Leadership, Production & Operations Management, Computers, Database Management
Cover of the book Big Data Analytics Beyond Hadoop by Vijay Srinivas Agneeswaran, Pearson Education
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Author: Vijay Srinivas Agneeswaran ISBN: 9780133838251
Publisher: Pearson Education Publication: May 15, 2014
Imprint: Pearson FT Press Language: English
Author: Vijay Srinivas Agneeswaran
ISBN: 9780133838251
Publisher: Pearson Education
Publication: May 15, 2014
Imprint: Pearson FT Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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: 

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.

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