Enterprise Infrastructure Portfolio

This mono-repository showcases enterprise-level Linux infrastructure engineering capabilities through three comprehensive projects demonstrating DevOps and Platform Engineering best practices.

Projects Overview

1. Enterprise Infrastructure Simulator

A container-based simulation of enterprise Linux infrastructure with Ansible automation for provisioning, patching, hardening, and decommissioning operations. Includes failure simulation and scaling scenarios.

Key Features:

  • Multi-node Linux simulation using Docker containers
  • Ansible playbooks for infrastructure lifecycle management
  • Automated scaling and failure injection scripts
  • Enterprise-grade inventory and scenario management

2. Migration Validation Framework

A Python CLI tool for validating system migrations by collecting, comparing, and reporting on system state changes. Generates comprehensive before/after snapshots and HTML reports.

Key Features:

  • Automated system data collection (mounts, services, disk usage)
  • JSON snapshot generation and comparison
  • HTML report generation with change visualization
  • CLI interface for enterprise migration workflows

3. Observability Stack

A complete monitoring and logging stack using ELK (Elasticsearch, Logstash, Kibana) and Grafana for comprehensive infrastructure observability.

Key Features:

  • Docker Compose deployment of full observability stack
  • Sample log ingestion and processing pipelines
  • Alerting rules and incident simulation scenarios
  • Real-time dashboards and monitoring capabilities

Architecture

See docs/architecture.md for detailed architecture overview.

Runbooks

Operational procedures and troubleshooting guides available in docs/runbooks.md.

Getting Started

Each project contains its own README.md with setup and usage instructions.

CI/CD

Automated testing and linting via Gitea workflows in .gitea/workflows/.

Prerequisites

  • Docker and Docker Compose
  • Ansible
  • Python 3.8+
  • Make

License

Enterprise Internal Use Only

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Description
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Readme 167 KiB
Languages
Python 69.4%
Shell 23.8%
Makefile 6.8%