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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

The problem, principles and planning

In this section, we will explore why an EDA might be required and discuss the important considerations for creating one.

Understanding the EDA problem

A difficult question that precedes an EDA project is: Can you give me an estimate and breakdown of your proposed EDA costs, please?

How we answer this question ultimately shapes our EDA strategy and tactics. In days gone by, the answer to this question typically started like this: Basically you pay by the column.... This rule of thumb is based on the premise that there is an iterable unit of data exploration work, and these units of work drive the estimate of effort and thus the rough price of performing the EDA.

What's interesting about this idea is that the units of work are quoted in terms of the data structures to investigate rather than functions that need writing. The reason for this is simple. Data processing pipelines of functions are assumed to exist already, rather than being new work...

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