Build faster and efficient real-world applications on the cloud with a fitting database model that's perfect for your needs

Key Features
Familiarize yourself with business and technical considerations involved in modeling the right database
Take your data to applications, analytics, and AI with real-world examples
Learn how to code, build, and deploy end-to-end solutions with expert advice

Book Description
In the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently.
The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you'll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You'll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples.
By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.

What you will learn
Understand different use cases and real-world applications of data in the cloud
Work with document and indexed NoSQL databases
Get to grips with modeling considerations for analytics, AI, and ML
Use real-world examples to learn about ETL services
Design structured, semi-structured, and unstructured data for your applications and analytics
Improve observability, performance, security, scalability, latency SLAs, SLIs, and SLOs

Who this book is for
This book is for database developers, data engineers, and architects looking to design, model, and build database applications on the cloud with an extended focus on operational consideration and taking their data to AI. Data scientists, as well ML and AI engineers who want to use Google Cloud services in the data to AI journey will also find plenty of useful information in this book. It will also be useful to data analysts and BI developers who want to use SQL impactfully to generate ML and generative AI insights from their data.