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Architecting Google Cloud Solutions

Release Date: 2021/04/01
ISBN: 9781800563308
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Achieve your business goals and build highly available, scalable, and secure cloud infrastructure by designing robust and cost-effective solutions as a Google Cloud Architect.

Key Features

  • Gain hands-on experience in designing and managing high-performance cloud solutions
  • Leverage Google Cloud Platform to optimize technical and business processes using cutting-edge technologies and services
  • Use Google Cloud Big Data, AI, and ML services to design scalable and intelligent data solutions

Book Description

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.

What you will learn

  • Get to grips with compute, storage, networking, data analytics, and pricing
  • Discover delivery models such as IaaS, PaaS, and SaaS
  • Explore the underlying technologies and economics of cloud computing
  • Design for scalability, business continuity, observability, and resiliency
  • Secure Google Cloud solutions and ensure compliance
  • Understand operational best practices and learn how to architect a monitoring solution
  • Gain insights into modern application design with Google Cloud
  • Leverage big data, machine learning, and AI with Google Cloud

Who this book is for

This book is for cloud architects who are responsible for designing and managing cloud solutions with GCP. You'll also find the book useful if you're a system engineer or enterprise architect looking to learn how to design solutions with Google Cloud. Moreover, cloud architects who already have experience with other cloud providers and are now beginning to work with Google Cloud will benefit from the book. Although an intermediate-level understanding of cloud computing and distributed apps is required, prior experience of working in the public and hybrid cloud domain is not mandatory.

Table of Contents

  1. Architecting Google Cloud Solutions
  2. Contributors
  3. About the author
  4. About the reviewers
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Download the example code files
    5. Code in Action
    6. Download the color images
    7. Conventions used
    8. Get in touch
    9. Reviews
  6. Section 1: Introduction to Google Cloud
  7. Chapter 1: An Introduction to Google Cloud for Architects
    1. Technical requirements
    2. Understanding the motivations and economics of cloud computing
    3. CAPEX versus OPEX
    4. Technology enablement
    5. Making the business case for cloud adoption (and Google Cloud)
    6. Learning about Google Cloud's key differentiators – big data and AI
    7. Why Google?
    8. Multi-cloud friendly
    9. Big data and AI
    10. Open source
    11. Getting an overview of Google Cloud
    12. Regions and zones
    13. Core Google Cloud services
    14. Multi-layered security
    15. Resource hierarchy
    16. Getting started with Google Cloud Platform
    17. Setting up a GCP account and project
    18. Installing the Google Cloud SDK and using gcloud
    19. Summary
  8. Chapter 2: Mastering the Basics of Google Cloud
    1. Technical requirements
    2. Understanding IAM
    3. Principle of least privilege and the IAM Recommender
    4. Segregation of duties
    5. Cloud Identity
    6. Practicing the cost discipline on Google Cloud
    7. Budgets and alerts
    8. Google Cloud Free Tier
    9. Sustained use discounts
    10. Committed use discounts
    11. Preemptible VM instances
    12. Nearline, Coldline, and Archive
    13. Custom Machine Types
    14. Rightsizing recommendations
    15. Pricing Calculator
    16. Getting hands-on – a case study
    17. Case study
    18. Summary
  9. Section 2: Designing Great Solutions in Google Cloud
  10. Chapter 3: Designing the Network
    1. Technical requirements
    2. Designing networks and subnetworks
    3. Multi-project networking
    4. IP addresses
    5. NAT
    6. DNS
    7. Cloud CDN
    8. Network pricing and service tiers
    9. Getting hands-on – deploying a custom VPC network
    10. Understanding routes and firewalls in Google Cloud
    11. Zero trust
    12. Understanding load balancing in Google Cloud
    13. Layer 7 HTTP/HTTPS load balancing
    14. Layer 4 TCP/UDP load balancing
    15. Design considerations for load balancing
    16. Designing for hybrid connectivity
    17. Cloud VPN
    18. Cloud Interconnect
    19. Mastering common network designs
    20. Design considerations and best practices
    21. Common network designs
    22. Summary
  11. Chapter 4: Architecting Compute Infrastructure
    1. Technical requirements
    2. Architecting with Compute Engine
    3. IaaS VMs
    4. Managed instance groups
    5. When to choose IaaS VMs
    6. Deploying an application with high availability on VMs
    7. Deploying an application with autoscaling on VMs
    8. Exploring Compute platforms
    9. App Engine
    10. Cloud Functions
    11. Cloud Run
    12. Understanding when to use Kubernetes
    13. Summary
  12. Chapter 5: Architecting Storage and Data Infrastructure
    1. Technical requirements
    2. Choosing the right storage solution
    3. Types of data
    4. The CAP theorem
    5. Using relational and structured datastores
    6. Cloud SQL
    7. Cloud Spanner
    8. Using non-relational and unstructured datastores
    9. Cloud Bigtable
    10. Cloud Firestore and Firebase Realtime Database
    11. Cloud Memorystore
    12. Cloud Storage for unstructured data
    13. Choosing the right solution for each piece of data
    14. Summary
  13. Chapter 6: Configuring Services for Observability
    1. Technical requirements
    2. Learning the monitoring basics
    3. The SRE approach to monitoring
    4. Monitoring cloud services and analyzing logs
    5. The monitoring landscape in Google Cloud
    6. Hands-on with Cloud Monitoring
    7. Investigating application performance issues
    8. Cloud Debugger
    9. Trace
    10. Profiler
    11. Designing for observability with best practices
    12. Choosing the right observability architecture
    13. Defining an alerting and incident response strategy
    14. Optimizing the costs of monitoring
    15. Summary
  14. Chapter 7: Designing for Security and Compliance
    1. Understanding cloud security
    2. Security in the cloud world
    3. Policy controls
    4. Deployment pipelines and DevSecOps
    5. Securing identities and access to resources
    6. Cloud Identity
    7. Securing networks
    8. Isolating networks by design
    9. Using firewalls
    10. Securing data and ensuring compliance
    11. Classifying your data
    12. Securing data at rest
    13. Securing data in transit
    14. Managing secrets, keys, and certificates
    15. Compliance
    16. Detecting vulnerabilities and malicious activity
    17. Security operations on GCP with Security Command Center
    18. Logging and SIEM
    19. Summary
  15. Section 3: Designing for the Modern Enterprise
  16. Chapter 8: Approaching Big Data and Data Pipelines
    1. Technical requirements
    2. Understanding big data services in Google Cloud
    3. Big data concepts
    4. Big data storage services on GCP
    5. Designing and building data pipelines
    6. Data integration
    7. Data discovery, preparation, and management
    8. Designing pipelines
    9. Getting hands-on – a big data case study
    10. Summary
  17. Chapter 9: Jumping on the DevOps Bandwagon with Site Reliability Engineering (SRE)
    1. Technical requirements
    2. Understanding DevOps and SRE
    3. Blameless postmortems
    4. Share ownership
    5. Reduce the cost of failure
    6. Measuring toil and reliability
    7. Toil automation
    8. Automating all things
    9. Infrastructure as Code (IaC) with Deployment Manager
    10. CI/CD with Cloud Build
    11. DevSecOps
    12. Job scheduling with Cloud Scheduler
    13. Applying SRE
    14. Creating an SRE foundation
    15. Forming SRE teams
    16. Summary
  18. Chapter 10: Re-Architecting with Microservices
    1. Technical requirements
    2. Understanding microservices and when to adopt them
    3. Why microservices?
    4. How to decompose a monolith
    5. Asynchronous messaging
    6. Common design patterns
    7. Building microservices with Kubernetes
    8. Deploying a microservices web application to GKE
    9. Designing and managing APIs for microservices
    10. API Gateway, Cloud Endpoints, and Apigee
    11. Testing your knowledge – microservices design case study
    12. Case study
    13. Summary
  19. Chapter 11: Applying Machine Learning and Artificial Intelligence
    1. Technical requirements
    2. Making the business case for AI and ML
    3. Understanding AI and ML concepts
    4. Making the case for ML
    5. Leveraging pretrained models on GCP with ML APIs
    6. Building custom ML models with Cloud AI Platform and BigQuery ML
    7. BigQuery ML
    8. Productionizing custom ML models with MLOps
    9. Identifying MLOps maturity level
    10. MLOps and CI/CD for ML on GCP
    11. Summary
  20. Chapter 12: Achieving Operational Excellence
    1. Technical requirements
    2. Starting with a cloud strategy
    3. Setting priorities
    4. Determining the cloud operating model
    5. Establishing the organizational culture
    6. Learning and applying operations best practices
    7. Increasing development and release velocity
    8. Monitoring system health and business health
    9. Designing for failure and practicing disaster recovery
    10. Bringing it all together with Cloud Operations Sandbox
    11. Summary
    12. Why subscribe?
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