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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

System recommendations


If you're installing Hadoop on a local machine, it is recommended that your system should have at least 4-8 GB of RAM (memory) and sufficient free disk space of at least 50 GB. Ideally, 8 GB or more memory will suffice for most applications. Below this, the performance will be lower but not prevent the user from carrying out the exercises. Please note that these numbers are estimates that are applicable for the exercises outlined in this book. A production environment will naturally have much higher requirements, which will be discussed at a later stage.

Installing analytics software, especially platforms such as Hadoop, can be quite challenging in terms of technical complexity and it is highly common for users to encounter errors that would have to be painstakingly resolved. Users spend more time attempting to resolve errors and fixing installation issues than they ideally should. This sort of additional overhead can easily be alleviated by using virtual machines ...

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