Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines

This book is a game-changer for any data engineering team struggling with "good pipelines, bad data." Written by industry experts Barr Moses, Lior Gavish, and Molly Vorwerck, it provides practical solutions to tackle data quality and trust at scale.

Learn how to build more trustworthy and reliable data pipelines, write scripts for data checks, identify broken pipelines, set and maintain data SLAs, SLIs, and SLOs, and automate data lineage graphs across your data ecosystem.

With real-world examples and actionable advice, this book will help you treat data services and systems with the diligence of production software and build anomaly detectors for critical data assets.

Don't let poor data quality hold your team back - get the tools and knowledge you need to succeed with "Data Observability: Building Trustworthy Data Pipelines."