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.
Table of contents
- About This eBook
- Title Page
- Copyright Page
- Dedication Page
- About the Author
- 1. Introduction: Why Look Beyond Hadoop Map-Reduce?
- 2. What Is the Berkeley Data Analytics Stack (BDAS)?
- 3. Realizing Machine Learning Algorithms with Spark
- 4. Realizing Machine Learning Algorithms in Real Time
- 5. Graph Processing Paradigms
- 6. Conclusions: Big Data Analytics Beyond Hadoop Map-Reduce
- A. Code Sketches
- Title: Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives
- Release date: May 2014
- Publisher(s): Pearson
- ISBN: 9780133838268
You might also like
Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools
Learn how to use the Apache Hadoop projects, including MapReduce, HDFS, Apache Hive, Apache HBase, Apache …
Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark
Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments …
Hadoop MapReduce v2 Cookbook - Second Edition
Explore the Hadoop MapReduce v2 ecosystem to gain insights from very large datasets In Detail Starting …
Sams Teach Yourself Hadoop in 24 Hours
Apache Hadoop is the technology at the heart of the Big Data revolution, and Hadoop skills …