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The DataOps Way to Data Quality: A Free Book for Every Data Team
2026
Most data quality advice tells you what to measure. This book tells you why your team keeps failing and what to actually do about it.
- Length
- 270 pages
- Price
- Free
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What's inside
This book is an anthology — most chapters were published here first, and the book's contribution is the selection and the order. Linked chapters go straight to the full text, no form required.
Data Quality Overview
The Seven Deadly Sins of Data Quality
- The Seven Deadly Sins of Data Quality
- The Data Errors Shame Game: Why Data Engineers Avoid Harsh Truths
- Is Your Team in Denial about Data Quality? Here’s How to Tell
- The Ostrich Problem: Your Data Team Thinks Their Job Ends at Deployment
- The Data Quality Revolution Starts with You
- Why Data Quality Isn’t Worth The Effort: Data Quality Coffee With Uncle Chip #1
- Data Quality The DataOps Way
- Data Quality When You Don’t Understand the Data: Data Quality Coffee With Uncle Chip #3
The Five Organizational Sins of Data Quality
- The Five Organizational Sins of Data Teams
- Warring Tribes into Winning Teams: Improving Teamwork in Your Data Organization With DataOps and Relational Coordination
- The Art of Data Buck-Passing 101: Mastering the Blame Game in Data and Analytic Teams
- Unlocking Data Team Success: Are You Process-Centric or Data-Centric?
- You’re Thinking About Data Products All Wrong
- Process Guardianship: The Most Valuable Data Engineering Work You’re Probably Not Doing
Core Data Quality Concepts
- Flip the Script on Data Quality: Shift Left, Shift Down, and Take Control
- Scaling Data Reliability: The Definitive Guide to Test Coverage for Data Engineers
- The Syntax, Semantics, and Pragmatics Gap in Data Quality Validation Testing
- Data Quality Testing: A Shared Resource for Modern Data Teams
- Peter Piper on the Four Ps of AI Data Quality: Purge, Patch, Push Back, or Pass
Data Architecture and Governance
Team Performance and Culture
- Data Quality Circles: The Key to Elevating Data and Analytics Team Performance
- How Data Quality Leaders Can Gain Influence And Avoid The Tragedy of the Commons
- War Rooms Suck
- Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis
- Why Not Hearing About Data Errors Should Worry Your Data Team
Data Quality Dashboards and Assessments
Tools and Technology Landscape
- The 2026 Open-Source Data Quality and Data Observability Landscape
- The 2026 Data Quality and Data Observability Commercial Software Landscape
- No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically
- The Data Consultant’s Growth Playbook: Accelerating Client Acquisition and Retention with DataKitchen’s Open Source TestGen
Real-World Stories and Case Studies
Data Observability With Data Quality
- The Five Use Cases in Data Observability: Overview
- The Five Use Cases in Data Observability: Data Quality in New Data Sources
- The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring
- The Five Use Cases in Data Observability: Mastering Data Production
- The Five Use Cases in Data Observability: Fast, Safe Development and Deployment
- The Five Use Cases in Data Observability: Ensuring Accuracy in Data Migration
We announce our third book, The DataOps Way To Data Quality & Data Observability.
Data quality is not a technology problem. It never has been. The data industry has spent years chasing the next tool, platform, or vendor promise, yet data errors still dominate the daily work of data engineers. Teams keep blaming each other when pipelines break, and business stakeholders quietly distrust the numbers they see.
After working with data teams across many industries, we’re convinced the real barriers to data quality are cultural, organizational, and process-related. Teams get stuck in reactive habits: scrambling to fix errors after they happen, pointing fingers across silos, and treating data quality as someone else’s problem. Our third book, The DataOps Way to Data Quality and Data Observability, aims to identify these patterns and offer a better way honestly.
That better way is DataOps. It applies Agile, DevOps, and lean manufacturing ideas to data and analytics work. DataOps isn’t a product you can buy; it’s a set of habits and disciplines that, when followed consistently, turn data quality from a constant crisis into a managed, measurable, and ever-improving part of your data environment. The book covers organizational failure patterns like warring tribes, the data shame game, and the ostrich problem, alongside technical practices that give teams real control: shift-left testing, systematic test coverage, continuous monitoring, and data observability that catches failures before they reach customers.
The main reason data teams struggle with data quality isn’t a lack of technology; it’s a lack of knowledge, vocabulary, and process frameworks to use that technology well. That’s why we wrote this book. We want every data team, regardless of budget or organizational maturity, to have a clear, honest guide for doing this work well.
We also built two free, open-source tools: DataOps TestGen for data quality testing and profiling, and DataOps Observability for pipeline monitoring. The book gives you the vocabulary, the frameworks, and the organizational playbook. The tools give you the means to act on them the same day. Used together, they close the gap between understanding the problem and actually fixing it. This book and these tools share the same belief: better data quality is achievable for every organization and worth pursuing. We hope you find not just ideas that resonate, but practical steps you can start using today.
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