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Practical Big Data Analytics

You're reading from   Practical Big Data Analytics Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781783554393
Length 412 pages
Edition 1st Edition
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Author (1):
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Nataraj Dasgupta Nataraj Dasgupta
Author Profile Icon Nataraj Dasgupta
Nataraj Dasgupta
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Table of Contents (13) Chapters Close

Preface 1. Too Big or Not Too Big FREE CHAPTER 2. Big Data Mining for the Masses 3. The Analytics Toolkit 4. Big Data With Hadoop 5. Big Data Mining with NoSQL 6. Spark for Big Data Analytics 7. An Introduction to Machine Learning Concepts 8. Machine Learning Deep Dive 9. Enterprise Data Science 10. Closing Thoughts on Big Data 11. External Data Science Resources 12. Other Books You May Enjoy

Spark for Big Data Analytics

As the use of Hadoop and related technologies in the respective ecosystem gained prominence, a few obvious and salient deficiencies of the Hadoop operational model became apparent. In particular, the ingrained reliance on the MapReduce paradigm, and other facets related to MapReduce, made a truly functional use of the Hadoop ecosystem possible only for major firms that were invested deeply in the respective technologies.

At the UC Berkeley Electrical Engineering and Computer Sciences (EECS) Annual Research Symposium of 2011, a vision for a new research group at the university was announced during a presentation by Prof. Ian Stoica (https://amplab.cs.berkeley.edu/about/). It laid out the foundation of what was to become a pivotal unit that would profoundly change the landscape of Big Data. The AMPLab, launched in February 2011, aimed to deliver a scalable...

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