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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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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

Time series

Time series data is the data that collectively represents how a system's behavior changes over time. Essentially, applications relay a form of data that measures how things change over time. Time is not only regarded as a variable being captured; time is the primary focal point. Real-world examples of time series data include the following:

  • Self-driving cars that continuously collect data to capture the ever-changing driving conditions or environment
  • Smart homes that capture events such as a change in temperature or motion

    Metric versus events

    Metrics are time series measurements gathered at regular intervals. Events are time series measurements gathered at irregular time intervals.

The following are some characteristics that qualify data as time series data:

  • Data that arrives is always recorded as a new entry.
  • Data arrives in time order.
  • Time is the primary axis.

    Adding a time field to the dataset is not the same as time series data...

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