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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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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Table of Contents (15) Chapters Close

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

Summary

In this chapter, we have concluded our journey by discussing aspects of distributed computing performance, and what to exploit when writing your own scalable analytics. Hopefully, you've come away with a sense of some of the challenges involved, and have a better understanding of how Spark works under the covers.

Apache Spark is a constantly evolving framework and new features and improvements are being added every day. No doubt it will become increasingly easier to use as continuous tweaks and refinements are intelligently applied into the framework, automating much of what must be done manually today.

In terms of what's next, who knows what's round the corner? But with Spark beating the competition yet again to win the 2016 CloudSort Benchmark (http://sortbenchmark.org/) and new versions set to be released every four months, one thing is for sure, it's going to be fast-paced. And hopefully, with the solid principles and methodical guidelines that you've...

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