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

Securing data and ensuring compliance

The first step toward efficiently securing your data is classifying it. After all, not all data is created equal, and you don't want to spend the same measure of effort and money on protecting every type of data.

Classifying your data

As discussed in Chapter 5, Architecting Storage and Data Infrastructure, data can be classified according to sensitivity levels. Example levels could be restricted, sensitive, and unrestricted. Alternatively, the levels could be confidential, internal use, and public. A three-tier classification system works well in most cases, but it could be different for your organization. The basis for classification can either be the data's content itself, the context surrounding the data (for example, which application or business function created it?), or a manual classification. A data classification policy could look like the following:

On GCP, the Cloud Data Loss Prevention (Cloud...