James Densmore - Data Pipelines Pocket Reference
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by James Densmore
Copyright 2021 James Densmore. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
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- Illustrator: Kate Dullea
- March 2021: First Edition
- 2021-02-10: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781492087830 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Data Pipelines Pocket Reference, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
The views expressed in this work are those of the author, and do not represent the publishers views. 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.
978-1-492-08783-0
[LSI]
Data pipelines are the foundation for success in data analytics and machine learning. Moving data from numerous, diverse sources and processing it to provide context is the difference between having data and getting value from it.
Ive worked as a data analyst, data engineer, and leader in the data analytics field for more than 10 years. In that time, Ive seen rapid change and growth in the field. The emergence of cloud infrastructure, and cloud data warehouses in particular, has created an opportunity to rethink the way data pipelines are designed and implemented.
This book describes what I believe are the foundations and best practices of building data pipelines in the modern era. I base my opinions and observations on my own experience as well as those of industry leaders who I know and follow.
My goal is for this book to serve as a blueprint as well as a reference. While your needs are specific to your organization and the problems youve set out to solve, Ive found success with variations of these foundations many times over. I hope you find it a valuable resource in your journey to building and maintaining data pipelines that power your data organization.
This books primary audience is current and aspiring data engineers as well as analytics team members who want to understand what data pipelines are and how they are implemented. Their job titles include data engineers, technical leads, data warehouse engineers, analytics engineers, business intelligence engineers, and director/VP-level analytics leaders.
I assume that you have a basic understanding of data warehousing concepts. To implement the examples discussed, you should be comfortable with SQL databases, REST APIs, and JSON. You should be proficient in a scripting language, such as Python. Basic knowledge of the Linux command line and at least one cloud computing platform is ideal as well.
All code samples are written in Python and SQL and make use of many open source libraries. I use Amazon Web Services (AWS) to demonstrate the techniques described in the book, and AWS services are used in many of the code samples. When possible, I note similar services on other major cloud providers such as Microsoft Azure and Google Cloud Platform (GCP). All code samples can be modified for the cloud provider of your choice, as well as for on-premises use.
The following typographical conventions are used in this book:
ItalicIndicates new terms, URLs, email addresses, filenames, and file extensions.
Constant width
Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.
Constant width bold
Shows commands or other text that should be typed literally by the user.
Constant width italic
Shows text that should be replaced with user-supplied values or by values determined by context.
Supplemental material (code examples, exercises, etc.) is available for download at https://oreil.ly/datapipelinescode.
If you have a technical question or a problem using the code examples, please send email to .
This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.
We appreciate, but generally do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: Data Pipelines Pocket Reference by James Densmore (OReilly). Copyright 2021 James Densmore, 978-1-492-08783-0.
If you feel your use of code examples falls outside fair use or the permission given above, please feel free to contact us: .
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