The Differences Between Grafana and Kibana
In business, data is king. However, any gathered information is useless without an insightful understanding of the data. Fortunately, there are two robust, open source visualization tools: Kibana and Grafana.
Both platforms allow users to see, sort, decipher, analyze, run diagnostics, and monitor data in customized ways. Since the two database helpers have so much in common, this article will dive into a deep discussion revealing the differences between Grafana and Kibana.
A little something about Grafana
Grafana is compatible with a variety of storage units. The analytics tool enables users to create complex charts, edit functions and metrics, format smart axis points and lines. Users decide how they want to picture the data so that they can make strategic determinations in business matters.
A little something about Kibana
Kibana works with the Elasticsearch, the analytics search engine. After data is indexed in a cluster in Elasticsearch, queries come alive with color geographical maps, tables, charts, and more. It’s all based on how users want to visualize the data in order to gain knowledge that results in thoughtful and profitable decision making.
The main distinctions between Grafana and Kibana
1. Supported data sources
Garfana is compatible with Elasticsearch too, but also other data sources such as InfluxDB, Graphite, Logz io, Prometheus, OpenTSDB, MySQL, PostgreSQL, Microsoft SQL Server, and AWS Cloudwatch.
As noted earlier, Kibana supports the Elasticsearch data source and was made to complement it.
2. Specific functionalities of the tools
Grafana’s functionality is geared towards tasks monitoring. It is also effective for use with time-series data and analysis for identifying patterns and making predictions based on that data. It supports application metric storage data sources. Since monitoring is a core functionality, alerts occur as they happen. There’s no full-text querying availability from Grafana.
Kibana is for analyzing existing raw log data and then displaying it in a variety of ways. For complete, sophisticated functionality, Kibana can be combined with Logstash, a server-side framework with plugins for creating pipelines. These pipelines process data from different sources. The data can then be sent to an Elasticsearch cluster also called a “stash.” Kibana alone has enhanced functionality to complete a search that’s full-text.
3. Analyzation purposes of either logs or metrics
Grafana’s analyzation and visualization purposes are metrics based. It’s used for memory, I/O and disk utilization, system CPU, and the like.
Unlike Grafana, Kibana’s analyzation and visualization are geared towards log messages. Use cases include development, forensics, security, and troubleshooting.
4. Variety of visualizations capabilities
Grafana and Kibana have the following kinds of visualizations:
- Single statistic
- Time Series (time order data points indexed)
However, in addition, these forms of visualization are specific to Kibana:
- Geospatial data and maps
- Tag clouds
5. Querying, searching, and dashboard abilities
Grafana has a Query Editor uses a different syntax for each data source. For example, Graphite querying won’t be the same as Logz.io querying.
Kibana querying and searching in Elasticsearch indices is accomplished with either Elasticsearch Query DSL, syntax Lucene, or Kuery (first introduced in Kibana 6.3). The display area of the main log is where the results appear in the order of when they were added.
The user interface (UI) dashboards of Grafana are focused on time series data. It’s enhanced for that and this makes it best suited for monitoring. Grafana’s panels are visualizations. A panel can be created for diverse data sources. It may be possibly subjective, but it’s worth mentioning as an observation that Grafana’s panel customization selections are many compared to Kibana’s personalization options for displaying data.
Easy editing and formatting describe Kibana’s dashboards. It’s easy to set up, format, and edit. Kibana’s UI dashboards support charts, graphs, and maps from querying lines of logs based on HTTP requests. A search box on the dashboard is where users make queries, see the results, and then save what they like. In Kibana, users can make additional queries to refine their search results. The results they saved appear on the dashboard.
6. Direct and in-direct alerting
Alert support is direct as a built-in with Grafana. Users can set queries for any time series metric. When there’s a failed connection to a database or a situation where data can’t be found, the alerting engine could be set up to trigger. In that case, it will notify the service such as PagerDuty or another one, or even send a web callback.
Alerting for Kibana is in-direct. Users can use ElastAlert or X-Pack, or use Logz.io, a hosted ELK Stack. With Kibana, users can configure alerts by means of the API using functions called “watchers.” Queries run on occasion when a data watcher is configured.
7. Setup, installation, and configuration
Grafana configuration includes an .ini file. Users can add environment variables to quash options in the configuration.
Kibana has YAML files for configuration but their syntaxes are a bit on the touch-sensitive side. Users create an Elasticsearch instance to connect with Kibana.
8. Authentication and controlling access
Grafana is known for its built-ins, and its structure for authentication and user control has a built-in. It has the capability to add a Lightweight Directory Access Protocol (LDAP) or SQL server that is external to restrict access and control access to dashboards.
The public has access to open source dashboards in Kibana unless the user adds a security plug-in. The commercial bundle X-Pack contains such a plug-in. SearchGuard is an open source security plug-in, and it’s also compatible with Kibana.
There are notable differences between Grafana and Kibana, and you can’t lose with either visualization tool. Consider selecting the one that best suits your business’s use case. To help you decide, keep in mind these tips. If you’re more into analyzing metrics and real-time monitoring of databases which are time-series, Grafana persuades. On the other hand, if your analyzing goals depend on quick searching of existing databases, more precisely, the deciphering of log data of which you manage, Kibana impresses.
Grafana and Kibana represent dynamic instruments that offer DBAs the means to derive analytical solutions of which any business can benefit. In reality, you may not even have to favor one over the other if you pick them both for contrasting reasons. Either or both. You’ve made the right decision.
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