Database Design and Modeling with Google Cloud

Cloud applications are expected to ship quickly and scale widely, and the choice of database shapes how well they meet those goals. Because no single database design suits every project, this guide helps data professionals plan and model their databases with care. It walks through the business and technical factors that should guide a database choice before turning to design.

The middle of the book works through structured data in depth, using transactional and analytical examples built on Cloud SQL, Spanner and BigQuery. It then covers semi-structured and unstructured data, with practical work on Firestore, Cloud Storage and related services. Later chapters address operational concerns such as observability, performance, security, scalability, latency targets and SLIs and SLOs, and show how to prepare data for AI, including Vertex AI APIs and generative AI use cases. The final chapter looks at designing for large language model applications.

Readers will learn to work with document and indexed NoSQL databases, model data for analytics, AI and machine learning, and use ETL services through real-world examples. The book is aimed at database developers, data engineers and architects who build database applications on Google Cloud. Data scientists, ML and AI engineers, data analysts and BI developers who want to use SQL to produce machine learning and generative AI insights will also find it useful.