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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

Summary

In this chapter, we got a high-level overview of Big Data and some of the components of implementing a Big Data solution in the Enterprise. Big Data requires selection of an optimal software and hardware stack, an effort that is non-trivial, not least because of the hundreds of solutions in the industry. Although the topic of a Big Data strategy may be deemed as a subject best left for management rather than a technical audience, it is essential to understand the nuances.

Note that without a proper, well-defined strategy and corresponding high level support, IT departments will remain limited in the extent to which they can provide successful solutions. Further, the solution, including the hardware-software stack should be such that it can be adequately managed and supported by existing IT resources. Most companies will find that it would be essential to recruit new hires...

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