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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

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781783554393

Length 412 pages

Edition 1st Edition

Languages

Java

Tools

Apache Spark

Concepts

Big Data

Author (1):

Dasgupta

View More author details

Table of Contents (13) Chapters

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

Leave a review - let other readers know what you think

NoSQL products

Redis: In-Memory Key-Value NoSQL Database:https://redis.io

Cassandra: One of the most powerful production NoSQL systems:http://cassandra.apache.org

MongoDB: Document-based NoSQL database:https://www.mongodb.com

Vertica: Community Edition:https://www.vertica.com/try/

Teradata: Aster Community Edition:https://aster-community.teradata.com/community/download

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Authors (1)

Dasgupta

Dasgupta

Nataraj Dasgupta is the vice president of advanced analytics at RxDataScience Inc. Nataraj has been in the IT industry for more than 19 years, and has worked in the technical and analytics divisions of Philip Morris, IBM, UBS Investment Bank, and Purdue Pharma. At Purdue Pharma, Nataraj led the data science division, where he developed the company's award-winning big data and machine learning platform. Prior to Purdue, at UBS, he held the role of Associate Director, working with high-frequency and algorithmic trading technologies in the foreign exchange trading division of the bank.

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