Data visualization plays a vital role in many activities. There are several tools, both open-source and paid, that allow us to create intuitive representations of our data. Among them we can't mention Kibana, the data visualization tool by ELK Stack. Let's find out what are its main features and not slo those dedicated to visualization.


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Nowadays, the amount of data generated is ever increasing. Big Data represent a huge source of knowledge, but also a great challenge in the IT field. The various professionals, from Data Architects to Data Scientists, who deal with Big Data need to define methodologies for data collection, processing, storage and analysis. As seen in the articles, Google Cloud Storage: solution for data lakes and Data lakes: GCP solutions, there are several solutions that address a variety of needs and use cases. Some of these include visualization software. Data visualization plays a very important role. In fact, it supports users to detect a pattern, monitor the environment, and take actions when abnormal behavior is detected. Currently, the market has a huge list of data visualization tools that provide the visibility needed for data understanding and analysis. Among them is Kibana.

Kibana is the most popular open source analytics and visualization platform designed to deliver faster and better insights into your data. In this article, we will explain this cutting-edge data visualization tool and its important features.

Kibana overview

Kibana is a tool that allows you to explore, visualize, and build a dashboard on data saved in Elasticsearch. As discussed in article ELK Stack: what it is and what it is used for, Elasticsearch is a document-oriented, text-based NoSQL database. In contrast, Logstash supports collecting, analyzing, and storing data for future use. Using Beats, it is possible to create even a very complex data ingest and processing pipeline.

The main feature of Kibana is data querying and analysis. It also allows you to visualize data in alternative ways using heat maps, line graphs, histograms, pie charts, and geospatial maps. Using various methods, you can search data stored in Elasticsearch for sensor diagnostics in the Internet Of Things (IOT) field or logs from different systems and/or applications.

With Kibana, it’s easy to delve into and understand Big Data. The ability to quickly build and share dynamic dashboards that capture real-time data saved in Elasticsearch makes it a very useful tool in many scenarios. There are many features available and they grow as new versions are released.

Since version 7.x, the side menu has changed significantly from the past. Main headings have been created that group certain features together. Let’s see below the main features and where to find them.


Many features related to data visualization and analysis have been included in the Analytics menu. In particular, the Discover page allows an interactive exploration of the data. It is possible to filter data by time period and/or view only the fields of interest. Using the Keyword Query Language (KQL) it is possible to filter data.

The Dashboard page, on the other hand, lists all the dashboards that have been created. Kibana’s dashboard is extremely dynamic and adaptable. For example, you can filter data on the fly and open the dashboard in full-page format. The customizable dashboard feature allows you to resize, organize, and edit the dashboard content and save it for sharing in different formats.

Unlike previous versions, the functionality for creating and customizing visualizations can be accessed from different menu items. The Canvas page provides the interface to create highly customized visualizations, while the Maps page is dedicated to map-based representation of data. These types of visualizations, in addition to others available on the platform, can be created and managed on the Visualize Library page. Below are the chart types currently available. As you can see there are you can create pie charts, data tables, bar charts, single metric visualizations, time series, geographic maps, and markdowns. These visualizations can then be included in one or more dashboards.

Data analysis plays a key role nowadays. In Kibana, in the Machine Learning section, it is possible to create tasks that detect anomalies or outliers, create linear regression or classification models. It is therefore possible to detect anomalous behaviors on the time series represented by the logs of an application to avoid service interruptions or cyber attacks.

Finally, the Graph page allows you to visualize the relationships that exist between data, as shown in the example below. You can define which fields you want to analyze and filter the data according to your needs.

Enterprise Search

In this section you can create text search solutions. For example, with App Search you can create a search engine and link it directly to your website. In fact, through an internal crawler it is not necessary to manually insert in Elasticsearch the data to be indexed, but everything will be done automatically. Differently Workspace Search create a search engine linked to the resources of an organization. It is possible to connect github repositories, Dropbox, OneDrive and Google Drive cloud storage and even gmail accounts.


The Observability menu is focused on monitoring, analyzing and visualizing the events that are captured and saved in Elastisearch. The Logs section allows you to view real-time log sources that have been defined and filter the data of interest.  You can also define rules for anomaly detection.

Through the pages of the Metrics section you can analyze and visualize the metrics of your architecture (CPU, RAM usage, etc.) as well as set up the reception of alerts in case of anomalies. The Application Performance Monitoring (APM) system helps users monitor applications and services by collecting performance and error metrics. This allows developers to quickly identify performance bottlenecks.

The Uptime section proactively monitors the availability of sites and/or services, and checks the validity of SSL certificates. Alerts can be set up if a site is unavailable or a certificate is about to expire in order to resolve issues faster and thus optimize the user experience.


Elastic Security combines the threat detection capabilities of a security information and event management (SIEM) system with endpoint prevention and response capabilities. These analytical and protection capabilities, leveraging the power of Elasticsearch, allow analysts to defend their organization from threats before damage and loss occur. Features that can be explored here include interactive visualizations to investigate relationships between processes, unsigned attack detection with automatic anomaly learning processes and pre-built detection rules, and integrated case management with automated actions.


Within this section you can perform several operations. In the DevTools page you can submit queries of interest to check the syntax and the results. On the Integrations page you can explore different agents to collect data from more than 100 different services.  Similarly Fleet provides an interface to add and manage integrations for the most popular services and platforms, as well as to manage a fleet of agents.

In Stack Monitoring you can, as shown in the figure, monitor the status of your stack.

Finally, on the Stack Management page you can manage several features including:

  • Index templates
  • backups
  • users with their roles
  • workspaces
  • saved objects
  • alerting and machine learning tasks

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