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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

General principles

Throughout this book we have demonstrated many data science techniques that, by using the power of Spark, will scale across petabytes of data. Hopefully, you have found these techniques sufficiently useful that you want to start using them in your own analytics and, indeed, have been inspired to create data science pipelines of your own.

Writing your own analytics is definitely a challenge! It can be huge fun at times and it's great when they work well. But there are times when getting them to run at scale and efficiently (or even at all) can seem like a daunting task.

Sometimes, with scarce feedback, you can get stuck in a seemingly endless loop waiting for task after task to complete not even knowing whether your job will fail at the very last hurdle. And let's face it, seeing a dreaded OutOfMemoryError at the end of a 20-hour job is no fun for anyone! Surely there must be a better way to develop analytics that run well on Spark and don't lead to wasted...

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