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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

GDELT dimensional modeling


As we have chosen to use GDELT for analysis purposes in this book, we will introduce our first example using this dataset. First, let's select some data.

There are two streams of data available: Global Knowledge Graph (GKG) and Events.

For this chapter, we are going to use GKG data to create a time-series dataset queryable from Spark SQL. This will give us a great starting point to create some simple introductory analytics.

In the next chapters, Chapter 4, Exploratory Data Analysis and Chapter 5, Spark for Geographic Analysis, we'll go into more detail but stay with GKG. Then, in Chapter 7Building Communities, we will explore events by producing our own network graph of persons and using it in some cool analytics.

GDELT model

GDELT has been around for more than 20 years and, during that time, has undergone some significant revisions. For our introductory examples, to keep things simple, let's limit our range of data from 1st April 2013, when GDELT had a major file...

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