Publisher's Synopsis
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
- Purchase of the print or Kindle book includes a free PDF eBook
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
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.
Table of Contents- Data, Databases, and Design
- Handling Data on the Cloud
- Database Modeling for Structured Data
- Setting Up a Fully Managed RDBMS
- Designing an Analytical Data Warehouse
- Designing for Semi-structured Data
- Unstructured Data Management
- DevOps and Databases
- Data to AI - Modeling Your Databases for Analytics and ML
- Looking Ahead - Designing for LLM Applications
