"Metaplane for Data Reliability Engineering"
"Metaplane for Data Reliability Engineering" is an authoritative and practical guide for modern data teams seeking to master the art and science of data reliability. The book begins by establishing a firm foundation in data reliability engineering, tracing its evolution, core principles, and unique challenges compared to traditional software reliability. Readers are equipped with a clear understanding of observability, consistency, data contracts, and the critical failure modes affecting today’s data systems, as well as how to apply SLOs and SLAs in data product contexts. The introductory chapters set the stage by examining the modern data stack and the essential competencies that drive dependable data operations.
At the heart of the book is a comprehensive exploration of the Metaplane platform—its architecture, extensibility, and integrations with the broader data ecosystem. Detailed technical discussions cover everything from schema discovery and change tracking to real-time anomaly detection, automated alerting, and data lineage modeling for root cause analysis. Practical deployment strategies are addressed, including multi-tenant scalability, performance optimization, compliance, access security, and environmental isolation. Expert guidance is provided for monitoring, validation, incident management, automated remediation, and building robust audit and reporting capabilities to meet both business and regulatory requirements.
The closing chapters look to the future of data reliability engineering: extending Metaplane with custom checks, automation, and AI-augmented insights; integrating with orchestration platforms, BI systems, and governance tools; and adapting to emerging paradigms such as data mesh, self-healing data systems, and federated reliability models. Rich with best practices, illustrative case studies, and community-driven innovation, "Metaplane for Data Reliability Engineering" is an indispensable resource for data engineers, SREs, and platform architects aiming to build and operate resilient, trustworthy, and automated data systems at scale.