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Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives

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Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives

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  • Your Price: $55.99
  • List Price: $69.99
  • Includes EPUB, MOBI, and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

About

Features

Master Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning

  • Take the next steps beyond Hadoop, with real-life use cases and hands-on programming examples
  • Master key elements of the Berkeley Data Analytics Stack (BDAS), including Spark and Storm
  • Develop advanced machine learning applications with GraphLab, the next-generation graph processing paradigm
  • Includes architecture, design, and code sketches for scaling machine learning algorithms to Big Data, and realizing them in real-time

Description

  • Copyright 2014
  • Dimensions: 6" x 9"
  • Pages: 240
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-383794-7
  • ISBN-13: 978-0-13-383794-0

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.

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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

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