"Looker Data Modeling and Analytics"
"Looker Data Modeling and Analytics" is an authoritative guide for analytics engineers, data modelers, and BI professionals seeking to unlock the full potential of Looker as a modern data platform. With a rich, structured approach, the book commences by establishing a solid foundation in Looker's architecture, semantic modeling with LookML, API integrations, and scalable deployment strategies. Readers are led from platform fundamentals through advanced topics, demystifying Looker's SQL generation, security model, and the intricacies of deploying robust, multi-tenant analytics environments.
The book drills deep into advanced LookML data modeling, equipping practitioners with the techniques to design performant models, handle complex relationships, optimize for time series analysis, and automate validation within Git-based CI/CD workflows. Beyond technical modeling, it explores holistic data architecture considerations including star, snowflake, and data vault patterns, strategies for multi-source federation, and governance essentials for enterprise analytics. Cost and performance optimization receive dedicated attention, with pragmatic guidance on warehouse tuning, aggregate awareness, caching, and monitoring the health and fiscal footprint of analytics workloads.
Practical implementation is at the core of every chapter, informed by real-world advanced use cases spanning vertical industry solutions, AI/ML integrations, embedding Looker into external applications, and engineering highly secure, compliant BI environments. The book's treatment of DevOps, CI/CD, and automated operations ensures readers are equipped to sustain and scale analytics ecosystems with confidence. Whether architecting self-service platforms, developing custom extension frameworks, or engineering for global, multi-region deployments, "Looker Data Modeling and Analytics" is an essential reference for building resilient, scalable, and future-ready data solutions on Looker.