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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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Toc

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

GIS lookup


In the previous section, we were covering an interesting use case, how to extract location entities from unstructured data. In this section, we will make our enrichment process even smarter by trying to retrieve the actual geographical coordinate information (such as latitude and longitude) based on the locations of entities we were able to identify. Given an input string London, can we detect the city of London - UK together with its relative latitude and longitude? We will be discussing how to build an efficient geo lookup system that does not rely on any external API and which can process location data of any scale by leveraging the Spark framework and the Reduce-Side-Join pattern. When building this lookup service, we will have to bear in mind many places around the world might be sharing the same name (there are around 50 different places called Manchester in the US alone), and that an input record may not use the official name of the place it would be referring to (the official...

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