Preface

This is the age of lightning-speed delivery. Whether it is the physical world of consumer products or the technological world that enables digitization, we want everything developed, built, and delivered at speed and at scale. Yes, I am talking about the software products and applications that are powering several million businesses these days. We all know that the knowledge, design, and choice of the database are critical in that journey. Is there a cookie-cutter template solution that gets you there quickly and correctly? The answer is no. But there are ways to get your idea into a product relatively faster and efficiently with the right design, and technical and business knowledge of your data. That is what I have addressed in this book.

This book focuses on taking the readers on a journey of taking idea through product, with a structured approach to guide you into asking the right questions and knowledge about your own data and business. It will also have a deep-dive discussion on each type and structure of data, and an immersive walk along the entire lifecycle of data in your application. You will also learn about database design best practices, considerations for data security, privacy and compliance with real-world examples and solutions with Google Cloud Database options for each category.

Who this book is for

This book is targeted towards database developers, data engineers, data architects, and data analysts who are looking to build solutions and insights using databases and Google Cloud services tailored to their organizational needs.

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.

To get the most out of this book

Basic understanding of data journey, types, databases, role in the product development cycle, cloud computing, and analytics basics would be useful in getting started with database design and modeling concepts and hands-on implementations.

Software/hardware covered in the book	Operating system requirements
SQL (BigQuery)	Windows, macOS, or Linux
Java 11
Google Cloud services (on browser): Spanner, Cloud SQL, Firestore, BigQuery, Cloud Storage, Cloud Functions, Cloud Run, Cloud Shell

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

If you manage to complete all the exercises in the book, head over to https://codevipassana.dev for more hands-on resources and references.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Database-Design-and-Modeling-with-Google-Cloud. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Enter bigquery-public-data in the Type to Search field again and once it appears, Expand this project.”

A block of code is set as follows:

SELECT country_name, new_confirmed, date, cumulative_confirmed, population FROM `bigquery-public-data.covid19_open_data.covid19_open_data` LIMIT 10

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

SELECT country_name, MAX(cumulative_confirmed) AS TOTAL_CONFIRMED_COVID19
FROM `bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE cumulative_confirmed > 0
GROUP BY country_name
ORDER BY MAX(cumulative_confirmed) DESC
LIMIT 5;

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “To start, click Activate Cloud Shell from Google Cloud Console in the top-right corner.”

Tips or important notes

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Get in touch

Feedback from our readers is always welcome.

General feedback: If you have questions about any aspect of this book, email us at [e-mail] and mention the book title in the subject of your message.

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