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