Introducing Kibana
Kibana was created primarily as a visualization tool for data residing on Elasticsearch and is bundled together as part of the stack. Since its inception, Kibana has evolved to cater to use cases such as alerting, reporting, and monitoring Elastic Stack components, as well as administrating and managing the Elasticsearch cluster in use.
More importantly, Kibana provides the interface and functionality for the solutions that Elastic Stack offers, in addition to administration and management options for the core components. Functionality in Kibana is organized using applications (or apps, for short).
Apps on Kibana can be solution-specific or part of the general stack. The SIEM app, for example, powers the security solution, enabling security analysts and threat hunters to defend their organization from attacks. The APM app is another solution-specific app that, in this case, allows developers and SREs to observe their applications to look for issues or performance bottlenecks.
On the other hand, general Kibana apps such as Discover, Visualize, and Dashboard can be used to explore, interrogate, and visualize data, regardless of the solution the data enables. Ingest Manager is another example of an app that allows you to configure Elastic Agent to collect any kind of data from across an environment, agnostic of the solution the data may be used in.
Solution-specific apps on Kibana provide a great out-of-the-box user experience, as well as targeted features and functionality for the solution in question. General or stack-based applications provide powerful but unified capabilities that can be used across all solutions, even custom solutions that you might build on the Elastic Stack. General Kibana apps such as Discover and Dashboard are useful for all use cases, while solution-specific apps such as Observability and Security provide curated out-of-the-box experiences for the solution area. Kibana is usually considered a core component of the Elastic Stack and is often installed, even if the cluster is not used for data analysis.
We will dive deeper into Kibana's features in Chapter 8, Interacting with Your Data on Kibana. Now, let's look at how data can be collected and ingested into Elasticsearch using Logstash and Beats.