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Google Cloud for DevOps Engineers

You're reading from   Google Cloud for DevOps Engineers A practical guide to SRE and achieving Google's Professional Cloud DevOps Engineer certification

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781839218019
Length 482 pages
Edition 1st Edition
Concepts
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Author (1):
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Sandeep Madamanchi Sandeep Madamanchi
Author Profile Icon Sandeep Madamanchi
Sandeep Madamanchi
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Site Reliability Engineering – A Prescriptive Way to Implement DevOps
2. Chapter 1: DevOps, SRE, and Google Cloud Services for CI/CD FREE CHAPTER 3. Chapter 2: SRE Technical Practices – Deep Dive 4. Chapter 3: Understanding Monitoring and Alerting to Target Reliability 5. Chapter 4: Building SRE Teams and Applying Cultural Practices 6. Section 2: Google Cloud Services to Implement DevOps via CI/CD
7. Chapter 5: Managing Source Code Using Cloud Source Repositories 8. Chapter 6: Building Code Using Cloud Build, and Pushing to Container Registry 9. Chapter 7: Understanding Kubernetes Essentials to Deploy Containerized Applications 10. Chapter 8: Understanding GKE Essentials to Deploy Containerized Applications 11. Chapter 9: Securing the Cluster Using GKE Security Constructs 12. Chapter 10: Exploring GCP Cloud Operations 13. Mock Exam 1 14. Mock Exam 2 15. Other Books You May Enjoy Appendix: Getting Ready for Professional Cloud DevOps Engineer Certification

Points to remember

The following are some important points to remember:

  • Cloud Monitoring is a GCP service that collects metrics, events, and metadata from multi-cloud and hybrid infrastructures in real time.
  • A workspace provides a single pane of glass related to GCP resources.
  • A workspace can monitor resources from multiple monitored projects.
  • A monitored project, however, can only be associated with a single workspace.
  • Dashboards provide a graphical representation of key signal data, called metrics, in a manner that is suitable for end users or the operations team.
  • Metrics represent numerical measurements of resource usage that can be observed and collected across the system at regular time intervals.
  • MQL can be used to create a chart with a text-based interface and uses an expressive query language to execute complex queries against time series data.
  • Uptime checks test the availability of an external facing service within a specific timeout interval...
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