Book Image

Architecting Google Cloud Solutions

By : Victor Dantas
Book Image

Architecting Google Cloud Solutions

By: Victor Dantas

Overview of this book

Google has been one of the top players in the public cloud domain thanks to its agility and performance capabilities. This book will help you design, develop, and manage robust, secure, and dynamic solutions to successfully meet your business needs. You'll learn how to plan and design network, compute, storage, and big data systems that incorporate security and compliance from the ground up. The chapters will cover simple to complex use cases for devising solutions to business problems, before focusing on how to leverage Google Cloud's Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) capabilities for designing modern no-operations platforms. Throughout this book, you'll discover how to design for scalability, resiliency, and high availability. Later, you'll find out how to use Google Cloud to design modern applications using microservices architecture, automation, and Infrastructure-as-Code (IaC) practices. The concluding chapters then demonstrate how to apply machine learning and artificial intelligence (AI) to derive insights from your data. Finally, you will discover best practices for operating and monitoring your cloud solutions, as well as performing troubleshooting and quality assurance. By the end of this Google Cloud book, you'll be able to design robust enterprise-grade solutions using Google Cloud Platform.
Table of Contents (17 chapters)
1
Section 1: Introduction to Google Cloud
4
Section 2: Designing Great Solutions in Google Cloud
10
Section 3: Designing for the Modern Enterprise

Choosing the right storage solution

A fundamental skill for any cloud architect is to know how to choose the right storage solution for the various types of data an organization possesses. In this section, you will start by learning a mental framework that will make it easier for you to make the right choice of data solution. Let's start by understanding and identifying the different types of data that exist.

Types of data

Data can be categorized in a few different "dimensions," so let's look at each one of them separately.

Relational versus non-relational

This first distinction applies to whether or not datasets are organized according to the relational model for databases. A collection of tables of data is considered relational when the relationship between the different tables is important. For example, suppose you have a table containing employees' data, such as their name, department, and salary, and you have another table containing department...