Aperçu du cours

Context

The company’s operations team has identified inefficiencies in their current log management system, which is not scalable and fails to provide real-time analytics and alerts. The company has multiple applications running across different environments, and the team needs a robust solution to aggregate, analyze, and visualize logs effectively.

Problem Statement

The operations team is facing challenges in identifying and diagnosing issues quickly due to the scattered nature of log data. There is a need to implement a centralized log management solution that not only aggregates logs from various sources but also provides tools for deep analysis and real-time monitoring.

Objective

Develop and configure a centralized logging system using the ELK Stack to streamline log ingestion, improve search capabilities, and enhance monitoring and alerting mechanisms. The lab will focus on setting up secure, scalable components of the ELK Stack to handle high volumes of log data from diverse sources.

Prerequisites

• Intermediate knowledge of Linux system administration.
• Basic understanding of networking principles and secure communication.
• Familiarity with Docker for setting up isolated environments.
• Prior experience with Elasticsearch, Logstash, Kibana, and Beats.

Lab Environment

The lab environment section outlines the required infrastructure and resources that participants will need to effectively carry out the tasks described in the lab. This ensures participants are well-prepared with all necessary tools and access rights before beginning the lab activities.

Hardware Requirements:

• A computer with at least 8GB of RAM and a quad-core processor to run all components of the ELK stack efficiently.
• At least 20GB of free disk space to accommodate log data, application installations, and necessary Docker images.

Software Requirements:

• Docker: Used for running Elasticsearch, Logstash, and Kibana containers. Ensure Docker Desktop or Docker Engine is installed depending on your operating system.
• Elasticsearch 7.10.2: Docker image will be used for deployment. No separate installation required.
• Logstash 7.10.2: Part of the Docker configuration to process and ingest data into Elasticsearch.
• Kibana 7.10.2: Docker image for visualizations and dashboard creation.
• Filebeat 7.10.2: To ship logs from various sources to Logstash or directly to Elasticsearch.

Network Requirements:

• Reliable internet connection for downloading necessary software and Docker images.
• Configurations to allow inbound connections on ports 9200 and 5601 for Elasticsearch and Kibana respectively.

Access Requirements:

• Administrative access on the workstation for installing Docker and other components.
• Permissions to configure network settings for proper communication between components of the ELK stack.

Development Tools:

• Integrated Development Environment (IDE) like Visual Studio Code or similar for editing configuration files and code.
• Postman or similar API client for testing API endpoints once exposed by Kibana and Elasticsearch.
• Git: To clone the provided repository containing all necessary configuration templates and sample codes.

Virtual Environment:

• If using virtual machines, ensure that the virtualization software (e.g., VMware, VirtualBox) is installed.
• Adequate CPU and RAM allocation for virtual machines if not running directly on host hardware.

This section ensures that all participants prepare their environments adequately before starting the lab, thus minimizing setup issues and focusing on learning objectives. The environment setup aims to mirror a real-world scenario as closely as possible, providing a comprehensive and immersive learning experience.