Data Contracts Developing Production-Grade Pipelines at Scale (Chad Sanderson, Mark Freeman, B.E. Schmidt) (z-library.sk, 1lib.sk, z-lib.sk)

Chad Sanderson, Mark Freeman & B.E. Schmidt Foreword by Joe Reis Data Contracts Developing Production-Grade Pipelines at Scale

9 7 8 1 0 9 8 1 5 7 6 3 0 5 7 9 9 9 ISBN: 978-1-098-15763-0 US $79.99 CAN $99.99 DATA Poor data quality can cause major problems for data teams, from breaking revenue-generating data pipelines to losing the trust of data consumers. Despite the importance of data quality, many data teams still struggle to avoid these issues—especially when their data is sourced from upstream workflows outside of their control. The solution: data contracts. Data contracts enable high-quality, well-governed data assets by documenting expectations of the data, establishing ownership of data assets, and then automatically enforcing these constraints within the CI/CD workflow. This practical book introduces data contract architecture with a clear definition of data contracts, explains why the data industry needs them, and shares real-world use cases of data contracts in production. In addition, you’ll learn how to implement components of the data contract architecture and understand how they’re used in the data lifecycle. Finally, you’ll build a case for implementing data contracts in your organization. • Explore real-world applications of data contracts within the industry • Understand how to apply each component of this architecture, such as CI/CD, monitoring, version control data, and more • Learn how to implement data contracts using open source tools • Examine ways to resolve data quality issues using data contract architecture • Measure the impact of implementing a data contract in your organization • Develop a strategy to determine how data contracts will be used in your organization Chad Sanderson is a data contracts expert and the CEO and cofounder of Gable. He writes extensively on data quality, runs the Data Products newsletter, and founded the Data Quality Camp community. He lives in Seattle. Mark Freeman is a data engineer and former community health advocate who uses data to drive social impact. With a master’s degree from Stanford Medicine, he applies his expertise to improve lives through technology, data, and innovation. B.E. Schmidt is a lifelong Midwesterner, a former creative director, and a writer with nearly two decades of experience in advertising, content marketing, and digital strategy. Data Contracts “This book is a must-read for modern data professionals and developers. In a world where data is the new oil and software development happens at lightning speed, data contracts leveraged in the right way can help achieve an organization’s vision and goals faster.” Akhil Behl, partner account manager, Red Hat

Chad Sanderson, Mark Freeman, and B.E. Schmidt Data Contracts Developing Production-Grade Pipelines at Scale

978-1-098-15763-0 [LSI] Data Contracts by Chad Sanderson, Mark Freeman, and B.E. Schmidt Copyright © 2026 Manifest Data Labs, Inc., and Benjamin Schmidt. All rights reserved. Published by O’Reilly Media, Inc., 141 Stony Circle, Suite 195, Santa Rosa, CA 95401. O’Reilly 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. Acquisitions Editor: Aaron Black Development Editor: Melissa Potter Production Editor: Katherine Tozer Copyeditor: J.M. Olejarz Proofreader: Sonia Saruba Indexer: nSight, Inc. Interior Designer: David Futato Cover Designer: Karen Montgomery Cover Illustrator: Karen Montgomery Interior Designer: David Futato Interior Illustrator: Kate Dullea November 2025: First Edition Revision History for the First Edition 2025-11-04: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781098157630 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Data Contracts, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors 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. This work is part of a collaboration between O’Reilly and Manifest Data Labs. See our statement of editorial independence.

Table of Contents Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Part I. Introduction to the Data Contract Architecture 1. Why the Industry Now Needs Data Contracts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Garbage-In, Garbage-Out Cycle 4 Modern Data Management 4 What Is Data Debt? 5 How Garbage-In, Garbage-Out Compounds 8 The Death of Data Warehouses 9 The Pre-Modern Era 12 Software Eats the World 13 A Move Toward Microservices 15 Data Architecture in Disrepair 16 Rise of the Modern Data Stack 17 The Big Players 18 Rapid Growth 19 Problems in Paradise 20 Data-Centric AI and the Rise of Shift Left Data Practices 23 Diminishing ROI of Improving ML Models 25 Commoditization of Data Science Workflows 26 Data’s Rise Over ML in Creating a Competitive Advantage 27 The Rise of Shift Left Data Practices 28 Conclusion 28 iii

2. Data Quality Isn’t About Pristine Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Defining Data Quality 31 OLTP Versus OLAP and Its Implications for Data Quality 34 A Brief Summary of OLTP and OLAP 34 Translation Issues Between OLTP and OLAP Data Worldviews 36 The Cost of Poor Data Quality 39 Measuring Data Quality 40 Who Is Impacted 45 Conclusion 47 3. The Challenges of Scaling Data Infrastructure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 How Data Development Is Not Like Software Development 49 How Software Engineers Build Products 50 How Data Developers Build Products 50 Core Challenges for Modern Data Engineering Teams 53 Why Data Development Needs a Design Surface 58 The Cost of Large-Scale Refactors 60 Large-Scale Refactor Considerations 60 Use Case: Alan’s Large-Scale Refactor 61 The Dangers of Database Migrations 63 The Role of Change Management in Data Quality 65 The Entropic Behavior of Data 66 How Data Drifts from Established Business Logic 67 Change Management Needs to Align with the Needs of the Business 69 How Infrastructure Needs Change at Scale 70 Dunbar’s Number and Conway’s Law 70 Case Study: Atlassian Engineering Team 72 How Data Contracts Enable Change Management at Scale 72 Conclusion 74 4. An Introduction to Data Contracts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Collaboration Is Different in Data 75 The Stakeholders You Will Work With 79 The Role of Data Producers 79 The Role of Data Consumers 81 The Impact of Producers and Consumers 83 The Trials and Tribulations of Data Consumers Managing Data Quality 86 An Alternative: The Data Contract Workflow 90 Steps of the Data Contract Workflow 90 Where to Implement Data Contracts 94 iv | Table of Contents

The Maturity Curve of Data Contracts: Awareness, Ownership, and Governance 97 Outcomes of Implementing Data Contracts 101 Data Contracts Versus Data Observability 102 Conclusion 105 Part II. Implementation of the Data Contract Architecture 5. The Data Contract Components: Data Assets and Contract Definition. . . . . . . . . . . . . 109 Overview of Components 109 Data Assets 111 Analytics Databases 112 Transactional Databases 113 Event Sourcing and Event Streams 117 First-Party Data, Third-Party Platform 120 Contract Definition 122 Data Contract Spec 122 Business Logic 128 Schema Registry and Data Catalogs 130 Conclusion 133 6. The Data Contract Components: Detection and Prevention. . . . . . . . . . . . . . . . . . . . . 135 Detection 136 Data Quarantining 140 Change Data Capture, Stream Processing, and Live Data Monitoring 141 End-to-End Lineage: Source Code Flow Graphs and Data Lineage 142 Static Code Analysis 146 Prevention 149 Version Control 149 CI/CD 150 Violation Monitoring and Alerts 152 Conclusion 156 7. Implementing Data Contracts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Mock Data Contract Scenario Intro 160 Dataset 160 Museum Application 162 Project Repo Overview 162 Implementing Data Contracts 164 Table of Contents | v

Data Contracts Architecture Overview 164 Component A: Data Assets 166 Component B: Contract Definition 168 Component C: Detection 171 Component D: Prevention 173 Putting It All Together 176 Data Contract Violation Scenario 178 Received Request to Normalize the object_images Table 178 Update Data Migration File for New Schema 178 Check Unit Tests and Trigger Contract Violation 180 Push to Remote Branch and View Error Logs 181 Revert Database Migration Changes 183 Discuss with Downstream Team to Learn Use Case 183 Create New Data Contract for the object_images_normalized Table 184 Run Unit Tests and See Error for Missing Asset in Catalog 186 Fix Database Migration Until All Checks Pass 187 Push to Remote Branch and See It Pass 189 Conclusion 189 8. Real-World Case Studies of Data Contracts in Production. . . . . . . . . . . . . . . . . . . . . . . 191 Convoy Data Contract Story 192 Glassdoor Data Contract Story 196 Adevinta Spain Data Contract Story 200 Conclusion 207 Part III. Getting Leadership Buy-in for the Data Contract Architecture 9. Shift Left: The Cultural Change Needed for Data Contracts. . . . . . . . . . . . . . . . . . . . . . 211 What Needs to Be True: What We Missed About Shift Left Data 212 Understanding the Stakes: Three Real-World Examples of Perception Gap Complications 214 Perception Gaps Fuel Faulty Assumptions 215 Perception Gaps Obscure Real-World Complexity 216 Perception Gaps Can Pit Teams Against Each Other 217 Enabling Adoption: A Strategic Approach for Closing the Software-Data Gap 220 Finding a Bridge and Fostering Trust 220 Charting the Course with Objective Information 222 Evolving Shift Left Data Alongside Your Organization 223 vi | Table of Contents

Measuring SWE Quality-of-Life Improvements During Shift Left Data Adoption 224 What the Rising Need for DevSecOps Demonstrated 225 Seeing (and Embracing) Shift Left Data for What It Truly Is 227 Conclusion 228 10. Change Management: The Crux of People, Process, and Technology. . . . . . . . . . . . . 231 The Importance of Change Management 232 Data as Supply Chains 233 Levels of Data Contract Implementations 236 Airplane and Airline Projects 236 Forward- and Backward-Looking Problems 239 Challenges of Technology-First Implementations 240 The Trap of Standards 241 Requirements of a Successful Data Contract Implementation 242 Developing a Strategy for Introducing Data Contracts 244 Define the Problem 245 Structure the Problem 246 Prioritize the Issues 247 Develop an Issue Analysis Work Plan and Conduct the Analyses 248 Synthesize Your Findings 249 Develop Recommendations 250 Conclusion 251 11. Creating Your First Wins with Data Contracts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Determining Your First Data Contract Use Case 254 Defining Data Products 254 Identifying Your Steel Threads 255 Making Your Final Decision 257 Determining Your Business Case and Requirements 261 Gather Information and Plan to Engage 261 Map Out Where Key Stakeholders Fit in the Data Lifecycle 263 Determine Key Stakeholder Influence, Interest, and Risks 264 Gain Executive Leadership Sponsorship 267 Eight Steps Toward Organization-Wide Shift Left Adoption 269 Establishing a Sense of Urgency 270 Creating the Guiding Coalition 271 Developing a Vision and Strategy 272 Communicating the Change Vision 273 Empowering Employees for Broad-Based Action 274 Generating Short-Term Wins 275 Table of Contents | vii

Consolidating Gains and Producing More Change 276 Anchoring New Approaches in the Culture 277 Attribution in Action: The Importance of Making Success Tangible 278 Conclusion 280 12. Measuring the Impact of Data Contracts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 What’s Worth Measuring? 281 Where to Start If You Have Minimal Existing Measurements 282 Minimal Viable Metric Trees 284 Measuring Across the Three Phases of Adoption 287 Phase 1 Goal: Measuring for Viability 289 Phase 2 Goal: Measuring for Repeatability 291 Phase 3 Goal: Measuring for Scalability 293 Storytelling for Measurement Impact 295 Departure 297 Transformation 297 Return 298 Putting It All Together 298 Conclusion 300 Conclusion: AI and the Future of Data Contracts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Appendix. Additional Resources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 viii | Table of Contents

Foreword I still remember when Chad Sanderson first started writing about data contracts, back when the concept was brand new and the data world was still figuring out ideas like “shift left” and data quality. Around that time, I was also friends with Mark Freeman, an up-and-coming, brilliant data engineer who was also mentoring part-time. When Chad and Mark became friends, I knew something great was going to happen. When I first heard that Chad and Mark were writing a book on data contracts, I was both curious and a bit envious. Curious, because the topic sits right at the intersection of everything we wrestle with daily as data professionals, namely trust, communication, and accountability. Envious, because I knew they’d articulate what so many of us have felt for years but struggled to put into words: that the biggest challenges in data aren’t purely technical, but mostly human. For too long, we’ve treated data quality as a downstream problem to be fixed by “the data team.” Data teams have built monitoring systems, observability dashboards, and clever duct tape patches that help us react more quickly when something breaks. Data debt keeps piling up. However, as this book demonstrates, none of that addresses the underlying issue. At its core, data quality relies on how effectively people communi‐ cate across teams. We nerds forget this. This communication depends on how clearly expectations are defined, how change is managed, and how responsibility is shared. In other words, it’s about relationships. What I love most about this book is that it bridges the gap between theory and practice. The authors don’t just argue for a new paradigm; they show you how to build it, step by step, in the same pragmatic language that data engineers use every day. Through their real-world case studies and hands-on guidance, they make an idea that once felt abstract, “data contracts,” feel both attainable and deeply necessary. ix

Reading this, you’ll see why data contracts represent more than a technical frame‐ work. They’re a cultural shift, a way for us to rebuild trust in our pipelines, our teams, and ultimately, our data itself. I’m proud of my friends for writing this book, and even more proud that it’s now in your hands. — Joe Reis Data engineer, data contracts fan, author October 2025 x | Foreword

Preface If you have picked up this book, it’s likely that you’ve deeply felt the pain of managing data while lacking control of your data’s ingestion and generation. Though at one point our industry centered around well-thought-out on-prem implementations with robust data models, the rise of cloud computing and the explosion of data products within organizations, via AI, have incentivized speed to market at the cost of turning the data layer into chaos. Many data teams within this situation find themselves con‐ stantly being reactive, repeatedly fixing the next data-related fire within the company. At its core, we believe this challenge in our industry stems from the difficulty of change management between historically siloed teams of data producers and con‐ sumers. Specifically, there is a disconnect between upstream application code, which defines how data is captured within a software system, and the downstream data products that leverage this data. We argue that data contracts serve as a mechanism for aligning data producers and consumers through automation and defining expect‐ ations as code. What Are Data Contracts? Data contracts are an architecture pattern that enables an agreement between data producers and consumers that is established, updated, and enforced via an API. They’re part of a larger movement called shift left, where you use automation to enable upstream software developers to account for required enforcement pertinent to their domain—this approach was first validated within DevOps and DevSecOps. Data contracts consist of four key components: • Data assets that need protection via change management • A contract specification file that codifies expectations of data assets as version- controlled code xi

• Detection via an ability to extract, analyze, and take action on changes to meta‐ data related to data assets under contract • Prevention by automating data contract enforcement within the developer work‐ flow, typically during CI/CD pipelines We argue that the data industry is having its shift left moment, and that data contracts are critical for this change. How to Use This Book One of the main drivers of us writing this book stemmed from early pushback that the concept of data contracts was too theoretical. This viewpoint is understandable, as many implementations were not public at the time, yet we knew that data contracts were gaining adoption. We’ve interviewed hundreds of companies and supported numerous teams with their own data contract adoption. Thus, our aim for this book is to serve as a practical guide for 1) framing the problems in our industry that create the need for data contracts, 2) implementing data contracts (including by using a public GitHub repository with a sandbox envi‐ ronment), and 3) building buy-in among executive leadership and scaling adoption organization-wide. We’ve organized the chapters as three distinct parts, so that you can come back and reference this book along your data contract implementation journey. Part I: Introduction to the Data Contract Architecture Chapters 1 to 4 provide historical and market context as to why the challenges of managing data still persist today, while also providing a foundational understanding of data quality, data infrastructure, and the workflow of data contracts for enforce‐ ment of expectations. Here’s the breakdown: • Chapter 1: Why the Industry Now Needs Data Contracts • Chapter 2: Data Quality Isn’t About Pristine Data • Chapter 3: The Challenges of Scaling Data Infrastructure • Chapter 4: An Introduction to Data Contracts Part II: Implementation of the Data Contract Architecture Chapters 5 to 8 detail the technical components of the data contract architecture and provide a walkthrough for implementing data contracts via an accompanying GitHub repository. In addition, we highlight multiple real-world case studies of data contracts in production, ranging from startups to enterprises. This part includes the following: xii | Preface

• Chapter 5: The Data Contract Components: Data Assets and Contract Definition • Chapter 6: The Data Contract Components: Detection and Prevention • Chapter 7: Implementing Data Contracts • Chapter 8: Real-World Case Studies of Data Contracts in Production Part III: Getting Leadership Buy-in for the Data Contract Architecture Chapters 9 to 12 underscore how data contracts solve sociotechnical problems that stem from the difficulty of change management within organizations. Solving such problems requires having tremendous influence to align multiple teams that histori‐ cally have been siloed from one another. These chapters are the result of the lessons we learned helping organizations adopt data contracts, grow their adoption, and measure their impact. Chapters in this part are as follows: • Chapter 9: Shift Left: The Cultural Change Needed for Data Contracts • Chapter 10: Change Management: The Crux of People, Process, and Technology • Chapter 11: Creating Your First Wins with Data Contracts • Chapter 12: Measuring the Impact of Data Contracts Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates 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 in angled brackets> Shows text that should be replaced with user-supplied values or by values deter‐ mined by context. This element signifies a general note. Preface | xiii

This element indicates a warning or caution. Using Code Examples Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/data-contract-book. That includes a sandbox environment, which you can run locally or within the browser, that walks you through how to implement data contracts and the data contract violation workflow. If you have a technical question or a problem using the code examples, please email support@oreilly.com. In addition, the book has an accompanying website that provides additional articles from the authors, as well as corresponding videos to guide your reading throughout the chapters. 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 you’re 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 O’Reilly 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 product’s 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 Contracts by Chad Sanderson, Mark Freeman, and B.E. Schmidt (O’Reilly). Copyright 2026 Manifest Data Labs, Inc., and Benjamin Schmidt, 978-1-098-15763-0.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com. xiv | Preface

O’Reilly Online Learning For more than 40 years, O’Reilly Media has provided technol‐ ogy and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, visit https://oreilly.com. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 141 Stony Circle, Suite 195 Santa Rosa, CA 95401 800-889-8969 (in the United States or Canada) 707-827-7019 (international or local) 707-829-0104 (fax) support@oreilly.com https://oreilly.com/about/contact.html We have a web page for this book, where we list errata and any additional informa‐ tion. You can access this page at https://oreil.ly/DataContracts. For news and information about our books and courses, visit https://oreilly.com. Find us on LinkedIn: https://linkedin.com/company/oreilly-media. Watch us on YouTube: https://youtube.com/oreillymedia. Preface | xv

Acknowledgments While our names are on the cover, this book would not be possible without the support of our colleagues and community. We would like to thank our colleagues at Gable who have been with us throughout the whole process of diving deep into understanding data contracts and making implementations a reality: Aaron Phillips, Adrian Kreuziger, Adrian Kuepker, Alex DeMeo, Andrew Oliver, Chayanne Aranda, Daniel Dicker, Daniil Tiganov, Demetrios Brinkmann, Geoffrey Wukelic, Hana Um, James Frost, Jasmine Simpelo, Jazmia Henry, Jon Shaiman, Karan Banwasi, Kidanekal Hailu, Leonie Jean Parojinog, Maks Sydorenko, Max Zunti, Mike Perrone, Mindaugas Rukas, Nazar Repak, Rachel Rosefigura, Ran Liu, Ravi Vooda, Rebecca Swords, Rus‐ sell Rivera, Sara La Torre, Suzanne Wen, Tom Erwin, Tommy Guy, and Yuhang An. We would also like to thank Gable’s investors, who were some of the first people to not only believe but also back the idea of data contracts: Apoorva Pandhi (Zetta Venture Partners), Scott Sage (Crane Venture Partners), Nick Giometti (B Capital), and our other various angel and follow-on investors. In addition, we would like to thank our amazing editors at O’Reilly Media with whom we worked directly throughout our time writing the book: Aaron Black, Melissa Potter (and Evie the cat), and Katherine Tozer. We thank our friends at Omniscient Digital, who also supported our book-writing efforts: Alex Birkett and Megan Otto. Finally, we would like to thank the members of the Shift Left Data community who provided feedback on the book and many of the ideas around data contracts: Aishvarya Verma, Ali Khalid, Amanda Manley, Ashraf Mohammad, Ben Heron, Bill Coulam, Christian van Eeden, Cristóbal Carvajal Benavides, Eddy Zulkifly, Eric Callahan, Eric Dressler, Erik Dahlberg, Gene Vestel, Ignatius Soputro, Joel Anderson, Jon Yeo, Jonathan Bergenblom, Jose Santos, Kiran S., Lars Nielsen, Luka Stepinac, Mahesh Kumar, Matt Nylin, Michael Day, Mohamed Mansour, Nachiket Mehta, Nar‐ ayanan V., Nidhi Vichare, Oliver Rudolph, Pawel Stradowski, Perry Philipp, Prashant Verma, Rafael Socorro, Raghu Mundru, Rubén Arévalo, Rukmani All, Saqib Ali, Satya Vandrangi, Sharon Sokoloff, Tanya Mackinnon, Thanh Khong, Tony McCray, Wei Hao, Yesh Kaushik, and Zakariah Siyaji. Chad Sanderson This book has been a journey. It wouldn’t have been possible without the support of some incredible people both professionally and personally. My cofounders—Adrian, James, and Daniel—for building in the trenches when data contracts were nothing but an idea. The entire team at Gable for bringing data contracts to life. Our invest‐ ors—Apoorva, Scott, Nick, and so many others—for believing in the power of data contracts when no one else saw the vision. xvi | Preface

Personally, I’d like to dedicate this book to my amazing wife, Laila, who has been a perfect angel through it all, as well my sisters, Cat, Izzy, and Taylor, my wonderful mother, and my dad, who first encouraged me to start writing. Love you all. Mark Freeman I stand firmly in the belief that it takes a village to reach various milestones in your life. Thus, I would like to thank my amazing wife for her unending support, patience, and encouragement over the course of writing this book (as well as our beloved dog, Albus). I would also like to thank my mom, dad, Justin, Charene, and additional family and friends who have shared similar support. Furthermore, I would like to thank all of my mentors who have invested their time in supporting my career in data. The list includes, though it’s not exhaustive, the Stanford Prevention Research Center (Dr. Michael Baiocchi, Dr. Janine Bruce, my Community Health and Prevention Research advisers, my WELL for Life research colleagues), Lisa Tealer, Dr. Joe Orsini, Omead Arami, Dr. Stefanie Tignor, Joe Reis, and Vin Vashishta. Finally, a huge thank-you to my coauthors, Chad and Ben, as it was truly a team effort and I’ve learned so much from both of you. B. E. Schmidt Thank you to my two coauthors for graciously inviting me along for this most excellent adventure, and to the smart and kind folks at Omniscient Digital for the opportunity to work with Chad and Mark in the first place. Thanks more generally to good editors and good friends, and the vital give-and-take they enable with writers in their lives. And, finally, thanks and all my love to my patient wife, our two children, and most of our three dogs. (Ignore all the haters, Gus. You are a good boy.) Preface | xvii