By Eleanor Bennett

Resources

4 min read

Kibana is a popular user interface used for data visualisation and for creating detailed reporting dashboards. This piece of software notably makes up a key part of the Elastic Stack alongside Elasticsearch and the extract, transform and load (ETL) tool, Logstash.

In this comprehensive introduction to Kibana, we are covering all of the basics that you will need to know as a user considering using Kibana for your log data visualisation and reporting needs.

This guide includes a brief history of the tool and clarification on its open-source status, as well as the use cases that you will likely encounter Kibana being well suited to handle.

What Is Kibana?

Kibana is the formerly open-source (we’ll get into this in more detail later in the article) visualisation user interface that allows users to produce visualisations, reports and dashboards from a variety of data sources.

Kibana was initially developed in 2013 by Rashid Khan, who in addition to Kibana is also the creator of the tools, Timelion and Canvas. Kibana makes up the data visualisation arm of the ELK stack (whose two remaining components include Logstash and Elasticsearch).

What Is A Kibana Dashboard?

Kibana dashboards provide an intuitive way of relaying data to the user by allowing them to combine a variety of different data visualisations and saved searches into a dynamically updating view that can be referred to at any point.

Kibana dashboards are fully customisable and compatible with displaying data in of the following formats; line and pie charts, gauges, data tables, heat maps, line graphs, coordinate maps and tag clouds.

What Is Kibana Used For?

Due to its support for unstructured and semi-structured data, one of the leading use cases for Kibana is log and metrics analysis. Some of the most popular additional use cases for Kibana include the following, which have been listed alongside visual examples in our guide to Kibana dashboard examples:

  • Centralised analytics dashboard for microservices
  • Understanding user behaviour
  • Jenkins application monitoring
  • Measuring sales performance
  • Resource allocation reporting
  • Data streaming dashboard
  • Monitoring website uptime
  • Automated test tracking
  • Global data monitoring
  • Vulnerability scanning
  • SIEM as a Service
  • Firewall monitoring
  • Tracking sign ups
  • Linux monitoring

Some other more underutilised use cases for Kibana include its data visualisation capabilities for compliance auditing, IT operations monitoring and application performance monitoring.

How Many Versions Of Kibana Have There Been?

It was announced on the 18th of March 2014, that Kibana 3.0.0 was made generally available, from here there have been over thirty-five new releases of the data visualisation solution. Prior to the general release of Kibana, there is no support documentation being maintained for any versions prior to 3.0.0.

Is Kibana Open Source?

As of version 7.11 and onwards of Kibana, the solution is no longer being considered as open-source due to being released under server-side public licensing (SSPL) based upon the Open Source Initiative's requirements for Apache licensing.

What are Open Search Dashboards?

If you have recently been searching for an open-source alternative to Kibana then you may have heard about Open Search Dashboards in your pursuit to discover a suitable solution for logs and metrics visualisation

Open Search dashboards are built upon the best features from previous versions of Kibana and aims to build upon the solution further by adding a number of new improvements (these can be observed via AWS’s publicly viewable roadmap to give you an idea of what is coming next).

You can continue learning about the differences between the open-source, closed-source and commercial offerings of the various components of the ELK Stack (and its derivatives) in our article; Opensearch vs Elasticsearch vs Open Distro.

What Is KQL?

KQL (Kibana Query Language) is Kibana’s dedicated query language that allows you to write short and concise search queries to filter data. Any search query entered is then automatically translated into the equivalent Elasticsearch query.

The main purpose of KQL is to support a far more concise syntax as an alternative to Elasticsearch's query DSL which tends to be excessively verbose and hard to understand. While KQL syntax is much shorter than Elasticsearch's query DSL, it still provides much of the necessary flexibility to write the majority of queries required for log and metrics analysis.

To help you get started with learning KQL, we've created a helpful infographic that serves as a reference to help you learn the commands you’ll need to get started with making the most out of Kibana; KQL Cheat Sheet.

How Can I Still Use Older Versions Of Kibana?

One way to gain access to older versions of Kibana (as well as the rest of the Elastic Stack) is to use a solution such as the one offered by Logit.io, which enables users to launch legacy versions of Kibana, alongside Open Search Dashboards and even Open Distro, all from a single account.

Get started with compliant, horizontally scalable observability stacks within minutes by using the Logit.io platform’s hosted Kibana platform.

If you enjoyed this article then why not check out our latest guide on data visualisation tools or our cheat sheet covering the top Linux commands?

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