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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

Enterprise data science overview


Data science is a relatively new topic in terms of enterprise IT and analytics. Traditionally, researchers and analysts belonged broadly to one of two categories:

  • Highly technical researchers who used complex computing languages and/or hardware for their professional tasks
  • Analysts who could use tools such as Excel and BI platforms in order to perform both simple and complex data analysis

Organizations started looking into Big Data and, more generally, data science platforms in the late 2000s. It had gained immense momentum by 2013, when solutions such as Hadoop and NoSQL platforms were released. The following table shows the developments in data science:

Year

Developments

1970s to late 1990s

Widespread use of relational database management systems. Entity relationship model, structured query language (SQL), and other developments eventually led to a rapid expansion of databases in the late 90s.

Early 2000s

The anti-climatic, yet expensive, non-event of Y2K, coupled...

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