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

Using non-relational and unstructured datastores

In general, non-relational data falls under the category of semi-structured or unstructured data. This means that they don't follow any strict tabular structure of data models (as is the case for relational databases), but, in the case of semi-structured data, they do contain elements that enforce a hierarchy of records and fields within the data. The most notable example is JSON, short for JavaScript Object Notation, which uses attribute-values pairs to describe objects and in which values can potentially be another JSON block, thus allowing for a nested data structure that enables data models to represent complex relationships between entities. The way objects (or entities) are described in JSON is akin to how objects are described in object-oriented programming languages. In addition, the support for lists of objects simplifies data models by avoiding complex translations from lists to a corresponding object in a relational data...