> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/ai-engineering/full-stack-and-ops-1/elk.md).

# ELK

Logs need a stack that can search and visualize them. This page explains Elasticsearch, Logstash, and Kibana from Elastic's own definition, then gives two guides that go further.

The same notes are in [Logs](/ai-engineering/full-stack-and-ops-1/logs.md).

The definition comes from the vendor. [Elastic on ELK](https://www.elastic.co/what-is/elk-stack) describes the stack as a way to reliably and securely take data from any source, in any format, then search, analyze, and visualize it in real time, and names the three parts:

> **So, what is the ELK Stack?** "ELK" is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch. The Elastic Stack is the next evolution of the ELK Stack.

With the three parts named, [Logz.io on ELK](https://logz.io/learn/complete-guide-elk-stack) is the complete ELK Stack: Elasticsearch, Logstash & Kibana Guide (2026). For an absolute beginner, [Hackernoon intro](https://hackernoon.com/elastic-stack-a-brief-introduction-794bc7ff7d4f) is "Elastic Stack — A Brief Introduction", which explains what the stack is and why you need it, and opens with its audience: You are reading the right blog post if you have heard of [Elastic Stack](https://hackernoon.com/tagged/elastic-stack) and want to explore or if you are an absolute dummy.


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