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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

The fundamentals of Hadoop

In 2006, Doug Cutting, the creator of Hadoop, was working at Yahoo!. He was actively engaged in an open source project called Nutch that involved the development of a large-scale web crawler. A web crawler at a high level is essentially software that can browse and index web pages, generally in an automatic manner, on the internet. Intuitively, this involves efficient management and computation across large volumes of data. In late January of 2006, Doug formally announced the start of Hadoop. The first line of the request, still available on the internet at https://issues.apache.org/jira/browse/INFRA-700, was The Lucene PMC has voted to split part of Nutch into a new subproject named Hadoop. And thus, Hadoop was born.

At the onset, Hadoop had two core components : Hadoop Distributed File System (HDFS) and MapReduce. This was the first iteration of Hadoop...

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