Feast-Spark Engineering Essentials (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Feast-Spark Engineering Essentials" Feast-Spark Engineering Essentials is a comprehensive guide that bridges the latest advances in feature engineering with production-grade machine learning operations. The book delves deep into the architectural foundations of Feast as a feature store and Apache Spark as a distributed data processing engine, offering a detailed understanding of how their integration empowers scalable, reliable ML pipelines. Readers are introduced to the critical motivations driving Feast-Spark synergy, with clear explanations of data modeling, entity design, and the practica...
"Feast-Spark Engineering Essentials" Feast-Spark Engineering Essentials is a comprehensive guide that bridges the latest advances in feature engineering with production-grade machine learning operations. The book delves deep into the architectural foundations of Feast as a feature store and Apache Spark as a distributed data processing engine, offering a detailed understanding of how their integration empowers scalable, reliable ML pipelines. Readers are introduced to the critical motivations driving Feast-Spark synergy, with clear explanations of data modeling, entity design, and the practicalities of end-to-end pipeline orchestration that meet the demands of modern MLOps. Through meticulously structured chapters, the book covers the entire feature engineering lifecycle, from creation, extraction, and transformation to advanced topics like automated validation, versioning, and drift detection. It discusses robust engineering practices for both batch and real-time ingestion, optimized transformations, and operational best practices required to build and maintain large-scale feature pipelines. Special attention is given to storage backends, high availability, resource scaling, and multi-region deployments, ensuring that enterprises can confidently implement reliable and cost-effective solutions. Feast-Spark Engineering Essentials stands out by addressing not only technical integration but also the operational realities of security, privacy, and compliance in regulated industries. Real-world case studies and emerging patterns provide actionable insight for both engineers and architects, encompassing governance, observability, cross-team collaboration, and the future evolution of feature store technology. The book is an indispensable resource for anyone building, operating, or scaling feature engineering infrastructure at the intersection of data and machine learning.
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