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Observability with Grafana

Observability with Grafana

By : Rob Chapman, Peter Holmes
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Observability with Grafana

Observability with Grafana

4 (4)
By: Rob Chapman, Peter Holmes

Overview of this book

To overcome application monitoring and observability challenges, Grafana Labs offers a modern, highly scalable, cost-effective Loki, Grafana, Tempo, and Mimir (LGTM) stack along with Prometheus for the collection, visualization, and storage of telemetry data. Beginning with an overview of observability concepts, this book teaches you how to instrument code and monitor systems in practice using standard protocols and Grafana libraries. As you progress, you’ll create a free Grafana cloud instance and deploy a demo application to a Kubernetes cluster to delve into the implementation of the LGTM stack. You’ll learn how to connect Grafana Cloud to AWS, GCP, and Azure to collect infrastructure data, build interactive dashboards, make use of service level indicators and objectives to produce great alerts, and leverage the AI & ML capabilities to keep your systems healthy. You’ll also explore real user monitoring with Faro and performance monitoring with Pyroscope and k6. Advanced concepts like architecting a Grafana installation, using automation and infrastructure as code tools for DevOps processes, troubleshooting strategies, and best practices to avoid common pitfalls will also be covered. After reading this book, you’ll be able to use the Grafana stack to deliver amazing operational results for the systems your organization uses.
Table of Contents (22 chapters)
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1
Part 1: Get Started with Grafana and Observability
5
Part 2: Implement Telemetry in Grafana
10
Part 3: Grafana in Practice
15
Part 4: Advanced Applications and Best Practices of Grafana

Summary

In this chapter, we have explored the process of architecting the data fields that will be collected. You will be able to use this knowledge to structure data in a Grafana platform so it is easy to use across your organization. We have discussed the process of architecting data production by applications and offering standard guidance on the best application structure to use. This will account for most needs of the developers in the organization. We shared several levels of complexity for the data collection architecture. You can use these as a starting point for architecting your own system. We discussed the various tools that are available to validate an architectural design: both tools for running local infrastructure and tools to simulate data that is being collected. This will help in producing a pipeline for delivering the infrastructure for an observability platform that you can rely on. Finally, we briefly discussed how to share data with other consumers, either in...

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