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Spark for Data Science

You're reading from   Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Table of Contents (12) Chapters Close

Preface 1. Big Data and Data Science – An Introduction FREE CHAPTER 2. The Spark Programming Model 3. Introduction to DataFrames 4. Unified Data Access 5. Data Analysis on Spark 6. Machine Learning 7. Extending Spark with SparkR 8. Analyzing Unstructured Data 9. Visualizing Big Data 10. Putting It All Together 11. Building Data Science Applications

Chapter 1.  Big Data and Data Science – An Introduction

Big data is definitely a big deal! It promises a wealth of opportunities by deriving hidden insights in huge data silos and by opening new avenues to excel in business. Leveraging big data through advanced analytics techniques has become a no-brainer for organizations to create and maintain their competitive advantage.

This chapter explains what big data is all about, the various challenges with big data analysis and how Apache Spark pitches in as the de facto standard to address computational challenges and also serves as a data science platform.

The topics covered in this chapter are as follows:

  • Big data overview - what is all the fuss about?
  • Challenges with big data analytics - why was it so difficult?
  • Evolution of big data analytics - the data analytics trend
  • Spark for data analytics - the solution to big data challenges
  • The Spark stack - all that makes it up for a complete big data solution
You have been reading a chapter from
Spark for Data Science
Published in: Sep 2016
Publisher: Packt
ISBN-13: 9781785885655
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