Project on Logstash - Large-Scale Logging Mechanism

Overview

This hands-on course equips learners with the practical skills to design, deploy, and debug end-to-end data processing workflows using the ELK (Elasticsearch, Logstash, Kibana) stack. Through guided case studies and real-world scenarios, participants will install and configure Elasticsearch, build and manage containerized applications using Docker and Docker Compose, and analyze service data with Logstash and Kibana. Learners will progressively construct multi-service pipelines, manage configurations using YAML, and monitor application behavior across interconnected containers. By the end of the course, participants will be able to construct scalable logging infrastructures, orchestrate services across environments, and evaluate and troubleshoot real-time application logs using Kibana. Whether you're an aspiring DevOps engineer or a backend developer, this course will help you apply theoretical concepts in real-world deployment scenarios such as define, configure, construct, implement, analyze, and evaluate.

Syllabus

Setting Up the Project Environment

This module introduces learners to the foundational components of the Logstash project environment, guiding them through Elasticsearch installation, database setup, application configuration, and container orchestration essentials.

Docker and Application Orchestration

Learners explore the principles of container-based deployment using Docker and docker-compose, gaining practical insights into orchestrating multi-service applications and automating service definitions.

Deep Dive into Case Study 1 and ELK Stack

This module dissects a real-world use case to illustrate data flow within the ELK stack, including installation, configuration, and visualization of service logs using Logstash and Kibana.

Case Study 2 and Debugging

Building on previous modules, this final module guides learners through a second case study involving advanced orchestration, entrypoint and volume management, and log inspection using Kibana for debugging.