Synopses & Reviews
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.
Synopsis
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.
About the Author
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.
Table of Contents
1. Introduction to Big-data Analytics
2. Berkeley Big-data Analytics (BDA) Stack: Motivation, Design and Architecture
3. Implementing Machine Learning Algorithms with BDA
4. Real-time Analytics with Storm
5. Performance, Throughput and Accuracy Analysis
6. GraphLab: Processing Large Graphs
7. Conclusion
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Master cutting-edge alternative technologies for Big Data analysis applications Hadoop can't handle well -- including real-time analysis and iterative machine learning