What is Kibana and what are its features
Kibana is a web interface that was introduced by Elasticsearch in 2015. Its features are extensive and has been known to help in log analysis and data visualization. It lets users maintain a list of tables and logs manually or with support for different sources, such as supported APIs, Apache Kafka, Elasticsearch, Node.js and with integrations to popular tools like Grafana.
Data visualization and analytics are some of the things Kibana does well. It lets users create real-time charts and monitor log data. Other functions of Kibana include getting system metrics, alarms, graph visualizations, etc. Kibana comes with a number of built-in visualizations that can be used for monitoring different data types like numeric, geo location and timeseries. It also has a handy, intuitive tabular data visualization that lets users select different fields and view the selected fields in a table.
This guide will provide an overview of Kibana and its features, how to install it in Elasticsearch, how to set up Kibana inspections and visualizations as well as how to get started with creating dashboards.
How to export data from Kibana into a CSV file
Popular data analysis tools are also capable of exporting data into CSV files. And while there are templates to do simple exports, they are often lacking in flexibility. The easiest way to export data from Kibana into a CSV, then, is by using a script. The script ensures you get the most flexibility when exporting.
So what does Kibana export to CSV look like? The format contains three columns: Timestamp, Index and Value. This column format appears in numerous other data analysis tools, as well as spreadsheets and databases.
If you wanted to create a field in Kibana for, say, an IP address, you would name the field ip and give it a value index of 1. A field with the name “domain” would have a value of 2.
Some tips on how to use the exported CSV data
It is best to use a script for exporting data from Kibana into a CSV file as offloading the work on dedicated scripts will save time. For example, if there is a lot of data to process, consider using an automated script that does the work for you. This can be particularly useful if the data has to be analyzed by a large number of users.
The script that extracts the data from Kibana looks as follows:
An example of how to use the exported CSV data in a Python program
Once the data has been exported, it can be further analyzed with a program like Python. One way to do this is to use a script for gathering and filtering the exported data. The collected data can then be exported into a database or a spreadsheet, both of which are more user-friendly interfaces and allow for more in-depth analysis.
This article shows how to use the exported data in a program that gathers and ultimately outputs data for further filtering and analysis. In this case, we are going to gather the data into a SQL database, but you can use any database or spreadsheet where the results can be exported.