DevOps Learning Path: From Fundamentals to Cloud-Native

This repository documents my systematic approach to mastering DevOps tools and practices through hands-on projects

Table of Contents

• Learning Methodology
• Medium Blog Posts
• Technical Progression Roadmap
• • [reopened, closing the bases] Phase 1: System Fundamentals
  • [in progress] Phase 2: Containerization & Local Orchestration
  • [planned] Phase 3: Networked Deployment
  • [planned] Phase 4: Automation Pipeline
  • [planned] Phase 5: Cloud-Native Implementation
• Core Project: Intelligent Recipe System
• Technology Stack
• Metrics & Success Criteria
• Documentation Philosophy

Learning Methodology

• Project-driven: Skills acquired through building real systems
• Incremental complexity: Each phase builds on previous knowledge
• Documentation: All lessons learned captured in code and articles
• Observability-first: All projects include monitoring from inception

Medium Blog Posts

• Installing Void Linux for DevOps: Why I Chose It and How to Avoid My Mistakes - June 8, 2025
• Void Linux Post-Install: i3wm, Dotfiles, and DevOps Tools (upcoming)
• Slack installing via xbps (upcoming)
• neko-planner-for-i3 (upcoming)

Technical Progression Roadmap

Phase 1: System Fundamentals

Completed:

• Void Linux installation as primary OS
• i3wm customization (config pending publication)

Key Outcomes:

• Deepened Linux administration skills
• Created reproducible system configurations

Phase 2: Containerization & Local Orchestration

Current Focus:

• Project: Microservice Cookbook Application
  • Frontend: Svelte
  • Backend: FastAPI
  • Database: PostgreSQL

Implementation Steps:

1. Docker containerization for each component
2. Service linking via Docker Compose
3. Metrics collection with Prometheus + Grafana
4. Log management (evaluating Loki vs ELK stack)

Technical Challenges:

• Cross-container networking
• Persistent storage configuration

Phase 3: Networked Deployment

Planned Work:

• Edge deployment to OrangePi device
• Secure external access via ngrok tunnels
• Kubernetes cluster implementation (k3s)
• Network policy configuration:
  • Service discovery
  • Ingress controllers
  • Port forwarding rules

Phase 4: Automation Pipeline

Next Stages:

• Git workflow design (feature branches, semantic versioning)
• CI/CD implementation:
  • GitHub Actions workflows
  • Automated testing suite
  • Artifact management
• Infrastructure as Code proof of concept (Terraform)

Phase 5: Cloud-Native Implementation

Future Objectives:

• Multi-cloud deployment
• Managed database services evaluation
• Production-grade monitoring:
  • Uptime monitoring
  • Alerting systems
  • Security hardening
• Cost optimization strategies

Core Project: Intelligent Recipe System

Technical Specifications:

• Authentication: OAuth 2.0 (Google + Email)
• AI Integration: GPT-based recipe generation engine
• Data Pipeline:
  • User input processing
  • API response validation
  • Fallback humor subsystem
• Observability:
  • Application performance metrics
  • Error logging and tracing
  • User interaction analytics

Architecture Goals:

1. Demonstrate container orchestration skills
2. Implement full CI/CD lifecycle
3. Showcase monitoring/alerting implementation
4. Validate infrastructure automation

Technology Stack

Core Competencies:

• Systems: Void Linux, QEMU virtualization
• Containers: Docker, Docker Compose, Kubernetes
• Monitoring: Prometheus, Grafana, Loki
• Cloud: AWS, GCP
• Development: Svelte, FastAPI, PostgreSQL

Metrics & Success Criteria

Category	Target	Measurement Method
System Reliability	99.95% lab environment uptime	Prometheus alerts
Deployment Speed	<5 minute build/deploy cycle	CI/CD pipeline metrics
Incident Response	<30 minute MTTR	Incident documentation
Automation Rate	85% tasks automated	Process inventory analysis

Documentation Philosophy

All projects include:

• Technical write-ups (Medium/blog)
• Annotated configuration files
• Failure post-mortems
• Improvement roadmaps