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Observability with Grafana

You're reading from   Observability with Grafana Monitor, control, and visualize your Kubernetes and cloud platforms using the LGTM stack

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781803248004
Length 356 pages
Edition 1st Edition
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Concepts
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Authors (2):
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Rob Chapman Rob Chapman
Author Profile Icon Rob Chapman
Rob Chapman
Peter Holmes Peter Holmes
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Peter Holmes
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Toc

Table of Contents (22) Chapters Close

Preface 1. Part 1: Get Started with Grafana and Observability
2. Chapter 1: Introducing Observability and the Grafana Stack FREE CHAPTER 3. Chapter 2: Instrumenting Applications and Infrastructure 4. Chapter 3: Setting Up a Learning Environment with Demo Applications 5. Part 2: Implement Telemetry in Grafana
6. Chapter 4: Looking at Logs with Grafana Loki 7. Chapter 5: Monitoring with Metrics Using Grafana Mimir and Prometheus 8. Chapter 6: Tracing Technicalities with Grafana Tempo 9. Chapter 7: Interrogating Infrastructure with Kubernetes, AWS, GCP, and Azure 10. Part 3: Grafana in Practice
11. Chapter 8: Displaying Data with Dashboards 12. Chapter 9: Managing Incidents Using Alerts 13. Chapter 10: Automation with Infrastructure as Code 14. Chapter 11: Architecting an Observability Platform 15. Part 4: Advanced Applications and Best Practices of Grafana
16. Chapter 12: Real User Monitoring with Grafana 17. Chapter 13: Application Performance with Grafana Pyroscope and k6 18. Chapter 14: Supporting DevOps Processes with Observability 19. Chapter 15: Troubleshooting, Implementing Best Practices, and More with Grafana 20. Index 21. Other Books You May Enjoy

Developing a proof of concept

The best place to prove a theoretical design is in an environment that has customers actually interacting with it, that is, a production environment. This is because any other environment is a mock environment and may miss some nuance of customer interactions. This is a recommendation to get the pathway to production created early and use it regularly. Having made that recommendation, it is still very important to have spaces for testing designs.

We will discuss compute containerization and virtualization tools, as well as simulated data production tools, which can be used to validate designs quickly.

Containerization and virtualization

Using containerization and virtualization locally and as part of a deployment pipeline can be a huge boost to provide quick feedback on whether a collection or storage architecture is achievable. Let’s consider some of the tools that will help in this space:

  • Containerization: The tools k3d, KinD...
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