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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
Languages
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

Big Data Mining for the Masses

Implementing a big data mining platform in an enterprise environment that serves specific business requirements is non-trivial. While it is relatively simple to build a big data platform, the novel nature of the tools present a challenge in terms of adoption by business-facing users used to traditional methods of data mining. This, ultimately, is a measure of how successful the platform becomes within an organization.

This chapter introduces some of the salient characteristics of big data analytics relevant for both practitioners and end users of analytics tools. This will include the following topics:

  • What is big data mining?
  • Big data mining in the enterprise:
    • Building a use case
    • Stakeholders of the solution
    • Implementation life cycle
  • Key technologies in big data mining:
    • Selecting the hardware stack:
      • Single/multinode architecture
      • Cloud-based...
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