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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

Storage Data Models

There are three broad categories of data models available in GCP: object, relational, and NoSQL. In addition, we will treat mobile optimized products like Cloud Firestore and Firebase as a fourth category, although these datastores use a NoSQL model. Their mobile supporting features are sufficiently important to warrant their own description.

Object: Cloud Storage

The object storage data model treats files as atomic objects. You cannot use object storage commands to read blocks of data or overwrite parts of the object. If you need to update an object, you must copy it to a server, make the change, and then copy the updated version back to the object storage system.

Object storage is used when you need to store large volumes of data and do not need fine-grained access to data within an object while it is in the object store. This data model is well suited for archived data, machine learning training data, and old Internet of Things (IoT) data that needs to be saved...

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