Automating Data Quality Monitoring 1st Edition

Automating Data Quality Monitoring, 1st Edition by Jeremy Stanley and Paige Schwartz is an essential guide for data teams who need reliable, scalable ways to keep their pipelines healthy. Clear, practical, and forward-looking, this book grabs your attention with real-world problems—missed SLAs, undetected regressions, and trust erosion—and shows how automation removes the guesswork.

You’ll find approachable strategies for instrumenting pipelines, defining measurable quality metrics, and building automated detectors and alerts that surface issues before they reach stakeholders. The authors translate complex topics—observability, testing, anomaly detection, and remediation—into actionable patterns and repeatable workflows that data engineers, SREs, and analytics leaders can implement across cloud and on-prem environments.

Imagine fewer incident-driven firefights and more confidence in every report and model. This book explains how to prioritize quality rules, integrate monitoring into CI/CD, and scale practices across teams so data becomes an enterprise asset rather than a liability. Whether you’re working in startups, Fortune 500s, or public-sector organizations across North America, Europe, or APAC, the approaches inside are adaptable and global in scope.

Readable yet rigorous, the authors balance technical detail with strategic guidance to help you move from manual checks to fully automated data quality programs. For teams aiming to reduce risk, accelerate insight delivery, and build trustworthy data Books, this is the how-to roadmap. Order your copy today and take the first step toward dependable, automated data quality monitoring.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.