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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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 Bifet Bifet
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Bifet
 Morgan Morgan
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Morgan
 Amend Amend
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 Hallett Hallett
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Hallett
 George George
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George
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Table of Contents (22) Chapters Close

Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. The Big Data Science Ecosystem FREE CHAPTER 2. Data Acquisition 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

Chapter 1.  The Big Data Science Ecosystem

As a data scientist, you'll no doubt be very familiar with handling files and processing perhaps even large amounts of data. However, as I'm sure you will agree, doing anything more than a simple analysis over a single type of data requires a method of organizing and cataloguing data so that it can be managed effectively. Indeed, this is the cornerstone of a great data scientist. As the data volume and complexity increases, a consistent and robust approach can be the difference between generalized success and over-fitted failure!

This chapter is an introduction to an approach and ecosystem for achieving success with data at scale. It focuses on the data science tools and technologies. It introduces the environment, and how to configure it appropriately, but also explains some of the nonfunctional considerations relevant to the overall data architecture. While there is little actual data science at this stage, it provides the essential platform to pave the way for success in the rest of the book.

In this chapter, we will cover the following topics:

  • Data management responsibilities

  • Data architecture

  • Companion tools

You have been reading a chapter from
Mastering Spark for Data Science
Published in: Mar 2017
Publisher: Packt
ISBN-13: 9781785882142
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