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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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 Dasgupta Dasgupta
Author Profile Icon Dasgupta
Dasgupta
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Table of Contents (16) Chapters Close

Title Page
Packt Upsell
Contributors
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 1. Other Books You May Enjoy

Summary


This chapter introduced some of the key tools used for data science. In particular, it demonstrated how to download and install the virtual machine for the Cloudera Distribution of Hadoop (CDH), Spark, R, RStudio, and Python. Although the user can download the source code of Hadoop and install it on, say, a Unix system, it is usually fraught with issues and requires a fair amount of debugging. Using a VM instead allows the user to begin using and learning Hadoop with minimal effort as it is a complete preconfigured environment.

Additionally, R and Python are the two most commonly used languages for machine learning and in general, analytics. They are available for all popular operating systems. Although they can be installed in the VM, the user is encouraged to try and install them on their local machines (laptop/workstation) if feasible as it will have relatively higher performance.

In the next chapter, we will dive deeper into the details of Hadoop and its core components and concepts...

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