"Great Expectations for Data Teams: Data Quality Testing That Fits CI/CD"
Data teams are done treating quality as a best-effort dashboard and hoping bad data doesn't ship. This book is for experienced analytics engineers, data engineers, and platform teams who want the same confidence software teams get from CI/CD—without turning pipelines into flaky, expensive bottlenecks. It provides a rigorous, production-minded approach to data validation where failures are actionable, reproducible, and aligned with real delivery constraints.
You'll learn how to translate CI/CD fundamentals to data workflows, build a non-flaky test pyramid for data systems, and convert quality dimensions into executable checks and versioned data contracts. The book goes deep on Great Expectations (GX) Core: modeling datasources and assets, choosing deterministic batch strategies, engineering expectation suites that scale and remain reviewable, and running validations with clear pass/fail semantics. It also covers checkpoints, actions, Data Docs, and artifact publishing, then ties everything together with CI integration patterns and CD release gates—progressive delivery, promotion rules, rollback strategies, and an operating model with ownership, SLOs, incident response, and runbooks.
Expect a practical, systems-level perspective: trade-offs, failure modes, and migration-safe setups (including 0.x?1.x) designed to keep gates stable as your platform evolves.
