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Modern Network Observability

You're reading from   Modern Network Observability A hands-on approach using open source tools such as Telegraf, Prometheus, and Grafana

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781835081068
Length 506 pages
Edition 1st Edition
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Authors (3):
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Christian Adell Christian Adell
Author Profile Icon Christian Adell
Christian Adell
David Flores David Flores
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David Flores
Josh VanDeraa Josh VanDeraa
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Josh VanDeraa
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1:Understanding Monitoring and Observability
2. Chapter 1: Introduction to Monitoring and Observability FREE CHAPTER 3. Chapter 2: Role of Monitoring and Observability in Network Infrastructure 4. Chapter 3: Data’s Role in Network Observability 5. Part 2: Building an Effective Observability Stack
6. Chapter 4: Observability Stack Architecture 7. Chapter 5: Data Collectors 8. Chapter 6: Data Distribution and Processing 9. Chapter 7: Data Storage Solutions for Network Observability 10. Chapter 8: Visualization – Bringing Network Observability to Life 11. Chapter 9: Alerting – Network Monitoring and Incident Management 12. Chapter 10: Real-World Observability Architectures 13. Part 3: Using Your Network Observability Data
14. Chapter 11: Applications of Your Observability Data – Driving Business Success 15. Chapter 12: Automation Powered by Observability Data – Streamlining Network Operations 16. Chapter 13: Leveraging Artificial Intelligence for Enhanced Network Observability 17. Index 18. Other Books You May Enjoy Appendix A

Summary

In this chapter, we highlighted the significance of data normalization, a practice that’s particularly fundamental in multi-vendor environments with diverse data sources. The need for consistent data representation remarks the relevance of initiatives such as OpenTelemetry and the Elastic Common Schema for observability data models. Telegraf, along with its range of inputs and processor plugins, plays a vital role in this area, especially in the network industry. We delved into how Telegraf achieves data normalization through its processor plugins, and we ran practical labs that guided us in shaping metrics to fit a hypothetical desired metric model.

Similarly, we manipulated log messages with Logstash and introduced filter plugins to help us normalize this type of data. We did a deeper dive into the Grok filter plugin, which provides us with an excellent way to get structured data from the text data of log messages.

Next, we delved into data enrichment, where...

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