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

Understanding big data services in Google Cloud

As more organizations undergo digital transformation and the number of internet-connected devices entering the market increases, the amount of generated data is growing very rapidly. Traditionally, most businesses have been able to store and use their data entirely using relational database management systems. This is no longer the case. Not only have datasets become so voluminous, but they have also become significantly diversified in their format. Whereas data has traditionally been structured and relational, nowadays, both structured and unstructured data (including images, files, device telemetry data, and so on) has fundamental value for businesses. "Big data" refers to this large, diverse volume of data that is too complex or too big to be managed and processed in a cost-efficient way using traditional methods. In the past few years, however, solutions to this problem have seen mainstream adoption, with technologies such...