Introduction For those who don’t know, Elastic Stack (ELK Stack) is an infrastructure software program made up of multiple components developed by Elastic. The components include: Beats: open-source data shippers working as agents on the servers to send different types of operational data to Elasticsearch. Elasticsearch: a highly scalable open source full-text search and analytics engine. It allows you to store, search, and analyze big volumes of data quickly and in near real time. It is generally used as the underlying engine/technology that powers applications that have complex search features and requirements. Kibana: open source analytics and visualization platform designed to work with Elasticsearch. It is used to interact with data stored in Elasticsearch indices. It has a browser-based interface that enables quick creation and sharing of dynamic dashboards that display changes to Elasticsearch queries in real time. Logstash: logs and events collection engine, which provides a real-time pipeline. It can take data from multiple sources and convert them into JSON documents. This tutorial will take you through
The ELK Stack ELK stands for Elasticsearch Logstash and Kibana which are technologies for creating visualizations from raw data. Elasticsearch is an advanced search engine which is super fast. With Elasticsearch, you can search and filter through all sorts of data via a simple API. The API is RESTful, so you can not only use it for data-analysis but also use it in production for web-based applications. Logstash is a tool intended for organizing and searching logfiles. But it can also be used for cleaning and streaming big data from all sorts of sources into a database. Logstash also has an adapter for Elasticsearch, so these two play very well together. Kibana is a visual interface for Elasticsearch that works in the browser. It is pretty good at visualizing data stored in Elasticsearch and does not require programming skills, as the visualizations are configured completely through the interface.
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