Log Analysis with Elasticsearch

Why Log Analysis through Elasticsearch is Important

You will learn why log and pipeline monitoring is essential for data engineers. In the introductory section, we will examine the architecture of Elasticsearch and compare it with relational databases, so you can understand the key differences and advantages.

Deploying Elasticsearch in Docker

Before diving into practice, you will learn how to run Elasticsearch and Kibana on your computer using Docker. We will use images from Docker Hub and create a Docker Compose file to launch the entire system. You will also get acquainted with the Kibana interface and its main features for log and data visualization.

Sending Logs to Elasticsearch

In the practical portion, you will create a new index in Elasticsearch and write a Python script to generate and send log events. These data will be indexed and become available for quick search.

Log Visualization and Analysis in Kibana

After loading the data, you will start working with Kibana: performing searches, setting up visualization elements, and assembling dashboards. You will learn to track what is happening in your pipelines and identify areas with data loss.

In the final section, we will tackle error detection in logs - you will learn how to quickly find issues and resolve them with minimal time expenditure.