Together, these different components are most commonly used for monitoring, troubleshooting and securing IT environments (though there are many more use cases for the ELK Stack such as business intelligence and web analytics).
And last but not least - Beats are lightweight agents that are installed on edge hosts to collect different types of data for forwarding into the stack. Kibana is a visualization layer that works on top of Elasticsearch, providing users with the ability to analyze and visualize the data. Logstash is a log aggregator that collects data from various input sources, executes different transformations and enhancements and then ships the data to various supported output destinations. The introduction and subsequent addition of Beats turned the stack into a four legged project.Įlasticsearch is an open source, full-text search and analysis engine, based on the Apache Lucene search engine. Up until a year or two ago, the ELK Stack was a collection of three open-source products - Elasticsearch, Logstash, and Kibana - all developed, managed and maintained by Elastic. Intro to the ELK Stack What is the ELK Stack? Some features are unavailable in one version and available in the other. Much of our content covers the open source ELK Stack and the iteration of it that appears within the Logz.io platform. We will help you understand what role they play in your data pipelines, how to install and configure them, and how best to avoid some common pitfalls along the way. In this guide, we will take a comprehensive look at the different components comprising the stack. What exactly is ELK? Why is this software stack seeing such widespread interest and adoption? How do the different components in the stack interact? In contrast, Splunk - the historical leader in the space - self-reports 15,000 customers in total. With millions of downloads for its various components since first being introduced, the ELK Stack is the world’s most popular log management platform.
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