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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 With Hadoop

Hadoop has become the de facto standard in the world of big data, especially over the past three to four years. Hadoop started as a subproject of Apache Nutch in 2006 and introduced two key features related to distributed filesystems and distributed computing, also known as MapReduce, that caught on very rapidly among the open source community. Today, there are thousands of new products that have been developed leveraging the core features of Hadoop, and it has evolved into a vast ecosystem consisting of more than 150 related major products. Arguably, Hadoop was one of the primary catalysts that started the big data and analytics industry.

In this chapter, we will discuss the background and core concepts of Hadoop, the components of the Hadoop platform, and delve deeper into the major products in the Hadoop ecosystem. We will learn about the core concepts...

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