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Official Google Cloud Certified Associate Cloud Engineer Study Guide

You're reading from   Official Google Cloud Certified Associate Cloud Engineer Study Guide The only official Google Cloud study guide

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Product type Paperback
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119564416
Length 560 pages
Edition 1st Edition
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Author (1):
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Dan Sullivan Dan Sullivan
Author Profile Icon Dan Sullivan
Dan Sullivan
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Toc

Table of Contents (28) Chapters Close

1. Cover FREE CHAPTER
2. Acknowledgments
3. About the Author
4. Introduction
5. Assessment Test
6. Answers to Assessment Test
7. Chapter 1 Overview of Google Cloud Platform 8. Chapter 2 Google Cloud Computing Services 9. Chapter 3 Projects, Service Accounts, and Billing 10. Chapter 4 Introduction to Computing in Google Cloud 11. Chapter 5 Computing with Compute Engine Virtual Machines 12. Chapter 6 Managing Virtual Machines 13. Chapter 7 Computing with Kubernetes 14. Chapter 8 Managing Kubernetes Clusters 15. Chapter 9 Computing with App Engine 16. Chapter 10 Computing with Cloud Functions 17. Chapter 11 Planning Storage in the Cloud 18. Chapter 12 Deploying Storage in Google Cloud Platform 19. Chapter 13 Loading Data into Storage 20. Chapter 14 Networking in the Cloud: Virtual Private Clouds and Virtual Private Networks 21. Chapter 15 Networking in the Cloud: DNS, Load Balancing, and IP Addressing 22. Chapter 16 Deploying Applications with Cloud Launcher and Deployment Manager 23. Chapter 17 Configuring Access and Security 24. Chapter 18 Monitoring, Logging, and Cost Estimating 25. Index
26. Advert
27. End User License Agreement
Appendix Answers to Review Questions

Cloud Computing vs. Data Center Computing

Although it may seem that running VMs in the cloud is not much different from running them in your data center, there are significant differences between operating IT environments in the cloud and an on-premise or colocated data center.

Rent Instead of Own Resources

Corporate data centers are filled with servers, disk arrays, and networking equipment. This equipment is often owned or leased for extended periods by the company, a model that requires companies to either spend a significant amount of money up front to purchase equipment or commit to a long-term lease for the equipment. This approach works well when an organization can accurately predict the number of servers and other equipment it will need for an extended period and it can utilize that equipment consistently.

The model does not work as well when companies have to plan for peak capacity that is significantly higher than the average workload. For example, a retailer may have an...

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