"Great Expectations for Modern Data Quality"
In today’s rapidly evolving data landscape, "Great Expectations for Modern Data Quality" provides a comprehensive and insightful guide for professionals navigating the challenges and opportunities of enterprise data management. The book begins by tracing the evolution of data quality practices, offering context from historical standards to the latest in cloud-native, distributed architectures. With detailed coverage of critical data quality dimensions, governance frameworks, regulatory compliance, and KPIs, readers gain a foundational understanding necessary for building trustworthy and resilient data systems.
Moving beyond theory, the book delivers practical architectural foundations, from data lakes to real-time streaming pipelines, with advanced strategies for embedding data quality at every layer of modern ecosystems. Through in-depth explorations of Great Expectations, readers learn how to implement modular, expectation-based validation rules, leverage open-source and commercial solutions, and operationalize data quality checks within CI/CD and DevOps workflows. The integration of machine learning techniques, sophisticated anomaly detection, and synthetic data further empowers organizations to enable robust, scalable validation at enterprise scale.
Packed with real-world case studies and forward-looking perspectives, this book is an essential resource for data engineers, architects, and governance leaders. It explores decentralized data mesh patterns, collaborative business-IT practices, and emerging trends like AI-driven quality assurance and Data Quality as a Service. By bridging technical depth with strategic guidance, "Great Expectations for Modern Data Quality" equips organizations to maximize the value of their data—turning quality from a reactive cost into a competitive advantage.
