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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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Concepts
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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

Technical elements of the big data platform

Our discussion, so far, has been focused on the high-level characteristics of design and deployment of big data solutions in an enterprise environment. We will now shift attention to the technical aspects of such undertakings. From time to time, we’ll incorporate high-level messages where appropriate in addition to the technical underpinnings of the topics in discussion.

At the technical level, there are primarily two main considerations:

  • Selection of the hardware stack
  • Selection of the software and BI (business intelligence) platform

Over the recent 2-3 years, it has become increasingly common for corporations to move their processes to cloud-based environments as a complementary solution for in-house infrastructures. As such, cloud-based deployments have become exceedingly common and hence, an additional section on on-premises...

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