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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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Amend
 Hallett Hallett
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Hallett
 George George
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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

Summary


In this chapter, we have seen why datasets should always be thoroughly understood before too much exploration work is undertaken. We have discussed the details of structured data and dimensional modeling, particularly with respect to how this applies to the GDELT dataset, and have expanded the GKG model to show its underlying complexity.

We have explained the difference between the traditional ETL and newer schema-on-read ELT techniques, and have touched upon some of the issues that data engineers face regarding data storage, compression, and data formats - specifically the advantages and implementations of Avro and Parquet. We have also demonstrated that there are several ways to explore data using the various Spark API, including examples of how to use SQL on the Spark shell.

We can conclude this chapter by mentioning that the code in our repository pulls everything together and is a full model for reading in raw GKG files (use the Apache NiFi GDELT data ingest pipeline from Chapter...

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