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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

Enterprise infrastructure solutions


The proper choice of infrastructure also plays a key role in determining the efficiency of the organization's data science platform. Too little, and the algorithms will take too long to execute; too much and you may have a lot of resources remaining unutilized. As such, the latter is preferable to having too little, which thwarts progress and the ability of any machine learning researcher to efficiently perform his or her tasks.

Cloud computing

Over the past 5 - 7 years, organizations have gradually shifted their resources to cloud-based platforms such as Amazon Web Services, Microsoft Azure, and Google Compute Engine. Today, all of these contain extremely sophisticated and extensive architecture to support machine learning, data mining, and in general data science at an enterprise level to meet the needs of organizations of all sizes.

In addition, the concept of images, such as AMI images in Amazon's AWS, allows users to initiate a pre-built snapshot of...

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