Every organization requires data analysis and monitoring solutions to gain insights into their data. Grafana and Kibana are two of the most popular open-source dashboards for data analysis, visualization, and alerting.
Grafana is an open-source, powerful, feature-rich data visualization tool for creating, exploring, and sharing dashboards. Some of the uses of Grafana are server/architecture health monitoring, visualization, and analysis of business data and metrics. It's a powerful tool for individuals and businesses alike to analyze business metrics and monitor infrastructure. Sign up for a free trial with MetricFire so you can check out what it's like to make some Grafana dashboards.
Kibana is the data navigation and visualization application that is used with the open-source ElasticSearch. Kibana is the method through which users control their ElasticStack as well as visualize the data coming from it. Kibana is represented as the K in the common phrase "ELK stack", where E stands for Elasticsearch, and L stands for Logstash.
The primary use of Grafana is the analysis and visualization of metrics. Metrics are time-series data that express changes over time for things like memory usage, system CPU, I/O, or disk usage. Kibana, on the other hand, is most often used to analyze the log messages from various data sources, as it operates on Elasticsearch.
Logs are system generated data sets that list and describe system events. Every event comes with a different set of data in the log messages. While the focus of logs is particular events, Metrics are the measure of a system over fixed intervals of time.
It is easy to use Kibana to analyze logs or other log dependent uses like forensics, security, and development. Grafana also provides capabilities for log analysis, but they're not as developed as Kibana's. Grafana has a logs panel for data sources that provide log information (such as Elastic, Influx, and Loki), however it is usually used in conjunction with a metric-based graph to expand on the depth of information in the dashboard.
Both Kibana and Grafana are considered very convenient and user-friendly to install and set up. Although Kibana supports more diverse installation options for operating systems, both the dashboards are available for all major OSs, including Windows, Mac, and Linux, as well as major platforms such as Docker. Both products can be built directly from the open-source project.
Grafana dashboards can be configured using a .ini configuration file. Configuring Grafana is a much easier process than configuring Kibana. The users can also alter the configuration options using environment variables. The documentation for Grafana data sources is very concise and specific. Each data source comes with its own configuration instructions. The users can access their Grafana dashboards at http://localhost:800/, where 800 is the user-specified port number during configuration.
As Kibana only uses Elasticsearch data sources, an Elasticsearch instance of the same version as your Kibana is required to use a Kibana dashboard. Users can configure their dashboards using syntax-sensitive YAML configuration files. While working with an instance of the dashboards on localhost, the users need to update their .yml configuration files with the URL.
Grafana supports over 30 data sources, including built-in integration with Graphite, Prometheus, Elasticsearch, PostgreSQL, MySQL, InfluxDB, and AWS Cloudwatch. Even more data sources can be accessed using plugins. Grafana provides specific query editors for various data sources customized according to the features and capabilities of the data sources.
Kibana supports only Elasticsearch data and no other data source. But, it has a feature-rich and well-developed integration with Elasticsearch. It provides excellent data searching and exploration functionalities for Elasticsearch sources.
Grafana comes with a built-in user control and authentication mechanism. It allows the users to restrict and control access to their dashboards, including a Lightweight Directory Access Protocol (LDAP) or an external SQL server.
With Grafana, users can create organizations, allowing them to create groups and teams for different projects. The users are not able to restrict access to individual dashboards and charts. Every member of an organization can access the dashboards of their organization. Every member has a specific role with a variety of permissions. The users can also set up unique API keys.
The Kibana dashboard, on the other hand, can be made openly accessible to the public. Dashboards don't have to exist within an organization. Kibana provides its users with a lot of flexibility in the dashboard configuration, as well as coming with default user authentication options. There are a variety of security plugins available to change the access control of a dashboard. The most commonly used is X-Pack, Elastic's commercial plugin. It is a bundle of ELK add-ons, and it provides the users with access control and authentication capabilities for their dashboards. Users can also check out SearchGuard, a popular security plugin compatible with Kibana.
Grafana comes with a built-in alerting engine that allows users to have control over their alerts. Users can create personalized alerts for any time series metric. Users can configure their alerts by applying conditional rules to dashboard panels.
The Grafana alerting engine also allows users to handle special-cases like data unavailability or failed connections. Grafana sends notifications to a user-selected endpoint on the trigger of an alert. The users can get their notifications to email, Slack, PagerDuty, or even a custom webhook.
On the contrary, Kibana does not come with an out-of-the-box alerting system. The users can implement Elastalert, work with a hosted ELK stack like Logz.io, or use the X-Pack plugin. The users can configure alerts in Elasticsearch through the API and through functions called watchers. A watcher is a function that periodically runs a query & performs a specific task based on the result.
Grafana's interface is better optimized for analyzing time-series data, making it best suited for monitoring things that change over time. It is not optimized for exploring other kinds of data and provides fewer data querying and refining capabilities when compared with Kibana. However, Grafana does provide a Query Editor to explore the data from its various data sources. Different data sources come with their own query editors tailored to the requirements of specific data sources. The query editor uses variables and a pre-set list of values to filter the data visualizations.
Querying and data exploration is considered one of the most powerful features of Kibana. Users have a variety of methods for querying, including Lucene syntax, Elasticsearch Query DSL, and Kuery (experimental - first introduced in Kibana 6.3).
Kibana users can search and query the Elasticsearch indices, which store the user data and display the results chronologically in the main log menu. Lucene and Elasticsearch Query DSL are powerful querying languages but might not be intuitive to a first time user. These languages have a steep learning curve.
The Kibana user interface comes with a search box for Elasticsearch queries and supports charts, maps, and graphs generated by querying logs based on HTTP requests. The users can also save the queries that provide results. Users can refine the set of presented data with the help of additional search parameters.
Both Grafana and Kibana are known for their powerful visualization capabilities. Grafana dashboards are very feature-rich and versatile. The visualizations in Grafana are called panels and panels are displayed on the dashboard. Users can create dashboards with multiple panels, where each panel has a different data sources.
Grafana supports a plethora of visualization types, including time series, histogram, gauge, heat map, text panel, single stat, tables, and more. A vast ecosystem of ready-to-use dashboards for a variety of data types and sources are also available to the users. Grafana also offers a significant amount of flexibility in data formatting and automatic axis scaling based on the units.
Kibana also offers a large variety of visualization tools. These visualization types include pie charts, time series, geo maps, markdown visualizations, time charts, data tables, and single metric visualizations. Kibana's visualization tools also include tag clouds, which are not supported by Grafana.
Both the dashboards provide a multitude of customization options for the data visualizations, allowing users to explore the data in their own way. They can customize the panel colors, labels, size of axes, X & Y axes, and many more. Grafana has a wider variety of customization capabilities when compared to Kibana, with collapsible rows and panel editors.
Both tools have a massive lively community of users and developers. Grafana has over 23000 commits by over 1000 contributors. Grafana takes the edge in its Github community, but it has a lot fewer StackOverflow questions than Kibana. The project has 32,000+ stars and 6000+ forks on GitHub. Grafana is very actively managed by its developers, having 2000+ issues and 100+ active Pull Requests.
Kibana, on the other hand, has over 29,000 commits by 450+ contributors. Its community is active, but smaller than Grafana's. The project has 13000+ stars and 6000+ forks on GitHub with a very productive development team, currently with 5000+ issues and 400+ active Pull Requests. Kibana is more efficient for business use because Elastic, the parent company of Kibana, is better resourced with a much larger community. The community is very active in conferences, meetups, and other events.
Both tools possess an impressive set of capabilities for data visualization and analysis but they're primarily used for different purposes. Grafana uses time-series data sources like InfluxDB and Graphite to analyze business metrics, while Kibana, a part of the ELK stack, is used for exploring log data. Organizations even use both as a part of their monitoring infrastructure.
Grafana is a monitoring tool, optimized primarily for infrastructure and data monitoring. It supports various application metric storage data sources and provides monitoring and instant real-time alerts. Grafana works effectively with time-series data for analysis, pattern identification, and predictions based on the data. It's an excellent choice for smaller and larger organizations alike for data monitoring.
Kibana, on the contrary, is a data visualization tool designed to facilitate the exploration and analysis of log data. It focuses on the analysis of logs and provides users ways to extract metrics from the existing raw log data.
Kibana users can query and apply relevant filters to collected logs to get visualizations and display them in their own way. Users can also use Kibana with Logstash, a server-side framework, which provides various plugins to create data pipelines. It's a fabulous choice for organizations of all sizes with networks that use Elasticsearch to manage their data.
Even though Grafana and Kibana are meant to be used for different purposes, and work with particular types of data, both are incredible analysis and visualization tools for any organization. Grafana and Kibana are great choices for organizations of any size, working with different types of data sources, for their monitoring infrastructure. Get a free trial with MetricFire and start making Grafana dashboards right away. Feel free to book a demo if you have questions about what Grafana can do for you.
This article was co-written by Nikhil Maan, & Vipul Gupta.
Vipul Gupta and Nikhil Maan are open-source developers and writers at Mixster. Mixster is an initiative with an aim to write better content for the open web. Starting back in 2017, we have collaborated and worked with early-stage startups, major open-source organizations, and on projects just like with the amazing folks at MetricFire. Let’s connect!