Kibana highlights

This list summarizes the most important enhancements in Kibana 8.2.

Document Explorer, the powerful new way to analyze and explore your data with Discover, is now on by default. Document Explorer helps you quickly sort, select, and compare data, resize columns, and view documents in fullscreen. For more information, check Explore your documents.

By default, Lens has always automatically applied the changes you make to visualizations. Now you can control when Lens applies your changes and generates suggestions with the new ability to turn on and off auto-apply. To start manually applying your changes, click Settings in the toolbar, then deselect Auto-apply visualization changes. For more information, check Create visualizations.

To call out significant changes and trends in your data, you can now add annotations to Lens visualizations. Customize the appearance of annotations with text, icons, colors, and more. For more information, check Add annotations.

Open and edit your Time Series TSVB visualizations in Lens, the drag-and-drop visualization editor that provides you with additional visualization types, reference lines, and more. For more information, check Edit visualizations in Lens.

For further exploration of your data, you can now open Lens visualization data in Discover. With Discover you can view the documents in the fields you used to create the visualization, then navigate back to Lens to finish creating your visualization. For more information, check Explore the data in Discover.

Kibana now offers even more ways to create visualizations:

In Maps, you can now display your clusters as hexagons instead of rectangles. This feature adds another beautiful, and more precise way, to visualize your geo_point fields.

You can now create region maps (also called choropleth maps) from Lens.

Upload an SVG image and use it as icons in your maps. Edit your maps layer, and then select Layer Style> Symbol style > Icon > Add custom icon.

Alerting introduces the ability to snooze rules to suppress notifications and actions for a user-defined duration. You’ll never miss an action because you forgot to unmute a rule while handling problems that arose unexpectedly or during known downtimes. For more information, check Snooze and disable rules.

For better observability and monitoring of your rules, Kibana now displays the execution history of your rules and actions. You can customize the history view to include granular information with an array of impactful data points, including the underlying alerting query durations, the number of actions that were triggered or errored, alert context, rule execution timeouts, and more.

Alerting introduces configurable guardrails so that as an admin, you can apply your policies on a level deeper than role-based access control (RBAC) and fully stay on top of the stress that the alerting system may put on your instance, in case your users misconfigured rules. This way you have peace of mind that your instance remains performant.

Specifically, in the kibana.yml file:

For more information, refer to Action settings.

With the new xMatters connector, you can configure the severity of a rule and assign it tags, so the right resources are notified to take action on the corresponding incidents. With the xMatters action, you can leverage schedules and escalations to engage with the right resources. The xMatters connector supports Basic and URL authentication.

This release brings Cases into the Stack, so that you can open and track issues outside Elastic Solutions. Access Cases in Stack Management under Alerts and Insights. For more information, check Cases.

From 8.2, users are able to test certain trained models in Kibana. The new action is introduced under Machine learning > Trained Models and it accepts user-specified input to test models. Initially supported models are lang_ident_model_1 (language identification) and third party named entity recognition (NER) models.

Machine learning trained models are space-aware starting in 8.2. Spaces enable you to organize your Kibana saved objects into meaningful categories. Once a trained model is limited to a Kibana space, users only see it if they are assigned to the same space. A warning is shown if any trained models are missing their saved objects; automatic synchronization also occurs periodically to address any missing objects.