Member-only story

Building a Log Analysis Data Pipeline Using Kafka, Elasticsearch, Logstash, and Kibana — ELK

Sai Parvathaneni
Towards Dev
Published in
8 min readSep 11, 2024

--

When it comes to analyzing logs, having a real-time, centralized, and automated solution is a game changer. Instead of sifting through individual log files on multiple servers, you can use a log analysis pipeline to ingest, store, and visualize logs in real-time. In this guide, we’ll build such a pipeline using Kafka, Logstash, Elasticsearch, and Kibana, which are often referred to together as the “ELK stack” (plus Kafka).

But first, let’s take a closer look at what each component in this pipeline does:

Key Components of the Pipeline

  • Kafka: Think of Kafka as the message bus or real-time log aggregator. It’s responsible for collecting log data from multiple sources and streaming it to other components in the pipeline.

Learn more about Kafka here:

  • Logstash: Logstash acts as a processor that ingests the logs from Kafka, transforms the data into a readable format, and sends it to Elasticsearch.
  • Elasticsearch: This is where logs are stored, indexed, and made searchable. Elasticsearch is a distributed search engine that’s optimized for fast searches and…

--

--

Published in Towards Dev

A publication for sharing projects, ideas, codes, and new theories.

Written by Sai Parvathaneni

Data Engineer on a mission to dumb down complex data engineering concepts. https://www.datascienceportfol.io/saiparvathaneni

No responses yet

What are your thoughts?