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Russell Jurney
Agile Data
Science 2.0
BUILDING FULL-STACK DATA ANALYTICS
APPLICATIONS WITH SPARK
Now with Kafka
and Spark!
Russell Jurney
Agile Data Science 2.0
Building Full-Stack Data Analytics
Applications with Spark
Boston Farnham Sebastopol
Tokyo
Beijing Boston Farnham Sebastopol
Tokyo
Beijing
978-1-491-96011-0
[LSI]
Agile Data Science 2.0
by Russell Jurney
Copyright © 2017 Data Syndrome LLC. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are
also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/insti‐
tutional sales department: 800-998-9938 or corporate@oreilly.com.
Editor: Shannon Cutt
Production Editor: Shiny Kalapurakkel
Copyeditor: Rachel Head
Proofreader: Kim Cofer
Indexer: Lucie Haskins
Interior Designer: David Futato
Cover Designer: Karen Montgomery
Illustrator: Rebecca Demarest
May 2017: First Edition
Revision History for the First Edition
2017-05-26: First Release
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Agile Data Science 2.0, the cover image,
and related trade dress are trademarks of O’Reilly Media, Inc.
While the publisher and the author have used good faith efforts to ensure that the information and
instructions contained in this work are accurate, the publisher and the author disclaim all responsibility
for errors or omissions, including without limitation responsibility for damages resulting from the use of
or reliance on this work. Use of the information and instructions contained in this work is at your own
risk. If any code samples or other technology this work contains or describes is subject to open source
licenses or the intellectual property rights of others, it is your responsibility to ensure that your use
thereof complies with such licenses and/or rights.
Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Part I. Setup
1.
Theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Introduction 3
Definition 5
Methodology as Tweet 5
Agile Data Science Manifesto 6
The Problem with the Waterfall 10
Research Versus Application Development 11
The Problem with Agile Software 14
Eventual Quality: Financing Technical Debt 14
The Pull of the Waterfall 15
The Data Science Process 16
Setting Expectations 16
Data Science Team Roles 17
Recognizing the Opportunity and the Problem 18
Adapting to Change 20
Notes on Process 22
Code Review and Pair Programming 24
Agile Environments: Engineering Productivity 24
Realizing Ideas with Large-Format Printing 26
2.
Agile Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Scalability = Simplicity 30
Agile Data Science Data Processing 30
iii
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