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Learn Grafana 10.x

Learn Grafana 10.x

By : Salituro
3 (3)
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Learn Grafana 10.x

Learn Grafana 10.x

3 (3)
By: Salituro

Overview of this book

Get ready to unlock the full potential of the open-source Grafana observability platform, ideal for analyzing and monitoring time-series data with this updated second edition. This beginners guide will help you get up to speed with Grafana’s latest features for querying, visualizing, and exploring logs and metrics, no matter where they are stored. Starting with the basics, this book demonstrates how to quickly install and set up a Grafana server using Docker. You’ll then be introduced to the main components of the Grafana interface before learning how to analyze and visualize data from sources such as InfluxDB, Telegraf, Prometheus, Logstash, and Elasticsearch. The book extensively covers key panel visualizations in Grafana, including Time Series, Stat, Table, Bar Gauge, and Text, and guides you in using Python to pipeline data, transformations to facilitate analytics, and templating to build dynamic dashboards. Exploring real-time data streaming with Telegraf, Promtail, and Loki, you’ll work with observability features like alerting rules and integration with PagerDuty and Slack. As you progress, the book addresses the administrative aspects of Grafana, from configuring users and organizations to implementing user authentication with Okta and LDAP, as well as organizing dashboards into folders, and more. By the end of this book, you’ll have gained all the knowledge you need to start building interactive dashboards.
Table of Contents (23 chapters)
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1
Part 1 – Getting Started with Grafana
5
Part 2 – Real-World Grafana
16
Part 3 – Managing Grafana

Summary

There you have it! We’ve accomplished a lot in this chapter, probably without even realizing it. Of course, we learned about a new Grafana capability in Grafana Live, and we learned a little bit about streaming data with Telegraf. We also picked up a sampling from the world of message brokering in IoT.

But I also slipped in a couple of other aspects you may not have noticed. We learned about using requirements to drive software development. Our code was cleaner and easier to understand when we adopted object orientation and the principles of delegation and separation of responsibilities. Finally, we also developed our script as a minimum viable product (MVP) by implementing only those aspects deemed important at this stage.

In our next chapter, we’ll look at how to monitor our real-time data for anomalous metrics and how we might not only detect them but also trigger warning alerts. We’ll even learn how to leverage Grafana’s alerting system...

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