What this book covers
Chapter 1, Data, Databases, and Design, will help us explore all the basics related to data, database, and modeling. You will learn all the general considerations you need to have while working with them.
Chapter 2, Handling Data on the Cloud, will help us dive into the details of cloud computing, and its different types, and explore the use cases and applications. By the end of this chapter, you’ll have a clear understanding of cloud computing, its types, use cases, benefits, applications, and considerations.
Chapter 3, Database Modeling for Structured Data, discusses structured data, its properties, types, use cases, key considerations, data modeling best practices, SQL basics, and some hands-on data modeling and query experiments.
Chapter 4, Setting up a Fully Managed RDBMS, takes the structured database design to hands-on learning with a fully managed cloud relational database. You will learn how to set up and configure your instance, how to create databases and objects in the database, and how to programmatically connect to the database and access data.
Chapter 5, Designing an Analytical Data Warehouse, will move on to designing for analytical data and take it to hands-on learning with a fully managed cloud data warehouse. You will learn how to set up and configure, create datasets and objects, query, and perform sample analytics on the data.
Chapter 6, Designing for Semi-structured Data, will show you the fundamentals of semi-structured data with examples, real-world use cases, characteristics of semi-structured data, design considerations, and components of a document database.
Chapter 7, Unstructured Data Management, will show you the fundamentals of unstructured data with examples, real-world use cases, how to store, manage, and perform analytics and with unstructured data.
Chapter 8, DevOps and Databases, discusses DevOps and operational attributes of database management like upgrades, security, monitoring, scalability, performance, SLA and SLOs, data federation, CI/CD, migration, and so on. We will also discuss how Google Cloud simplifies the design decisions for these operational considerations.
Chapter 9, Data to AI – Modeling Your Databases for Analytics and ML, explores some key considerations and best practices while designing for analytics, ML, and AI with cloud databases, covering topics like modeling considerations for analytics and ML, analytics, ETL, and the journey of data to AI.
Chapter 10, Looking Ahead – Designing for LLM Applications, will set the stage for data modeling for LLM applications by covering the evolution and basics of LLM, the difference between ML and generative AI applications, the ethical and responsible practices and considerations, and finally the real-world use cases and hands-on implementation to extend your database application to include LLM insights.
