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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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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

Spark architecture


Apache Spark is designed to simplify the laborious, and sometimes error prone task of highly-parallelized, distributed computing. To understand how it does this, let's explore its history and identify what Spark brings to the table.

History of Spark

Apache Spark implements a type of data parallelism that seeks to improve upon the MapReduce paradigm popularized by Apache Hadoop. It extended MapReduce in four key areas:

  • Improved programming model: Spark provides a higher level of abstraction through its APIs than Hadoop; creating a programming model that significantly reduces the amount of code that must be written. By introducing a fluent, side-effect-free, function-oriented API, Spark makes it possible to reason about an analytic in terms of its transformations and actions, rather than just sequences of mappers and reducers. This makes it easier to understand and debug.

  • Introduces workflow: Rather than chaining jobs together (by persisting results to disk and using a third...

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