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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

Summary

This chapter provided a technical overview of Hadoop. We discussed the core components and core concepts that are fundamental to Hadoop, such as MapReduce and HDFS. We also looked at the technical challenges and considerations of using Hadoop. While it may appear simple in concept, the inner workings and a formal administration of a Hadoop architecture can be fairly complex. In this chapter we highlighted a few of them.

We concluded with a hands-on exercise on Hadoop using the Cloudera Distribution. For this tutorial, we used the CDH Virtual Machine downloaded earlier from Cloudera's website.

In the next chapter, we will look at NoSQL, an alternative or a complementary solution to Hadoop depending upon your individual and/or organization al needs. While Hadoop offers a far richer set of capabilities, if your intended use case(s) can be done with simply NoSQL solutions...

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