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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

Streaming real-time data from Telegraf to Grafana

In previous chapters, we typically sourced a collection of metrics from, say, a website or a data file, and after storing that data in a time-series database, we then used Grafana to query that data for analysis and visualization. This process of working a corpus of data as a single entity is sometimes referred to as batch handling. By contrast, in this chapter, we will be continuously moving time samples of data as they are generated, or streaming the data from source to destination.

This style of handling data is suitable for many applications where the data needs to be visualized or otherwise processed in real time for monitoring and alerting. It is also most useful in those cases where it would be difficult or impractical to wait for all the data to arrive before analyzing, or where sampled, non-transactional data is the norm.

To get an idea about how to distinguish between these two use cases, imagine the case where you need...

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