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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

Data science solutions in the enterprise


As discussed before, in general, we can broadly categorize data science into two primary sections:

  • Enterprise data warehouse and data mining
  • Enterprise data science: machine learning, artificial intelligence

In this section, we will look at each of these individually and discuss both the software and hardware solutions used in the industry for delivering these capabilities.

Enterprise data warehouse and data mining

Today, there are scores of databases available in the industry that are marketed as NoSQL systems capable of running complex analytical queries. Most of them have one or more features of typical NoSQL systems, such as columnar, in-memory, key-value, document-oriented, graph-based, and so on. The next section highlights some of the key enterprise NoSQL systems in use today.

Traditional data warehouse systems

Traditional data warehouses might be a misnomer, since most of the traditional systems have also incorporated core concepts of NoSQL. However...

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