Introduction to DevOps & Platform Engineering

Duration: 90 min

DevOps is a cultural and technical movement that breaks down silos between development and operations teams. It emphasizes automation, collaboration, and continuous improvement to deliver software faster and more reliably. Platform Engineering extends this by building internal developer platforms that enable teams to self-serve infrastructure and deployment capabilities.

DevOps Culture & Principles

DevOps is fundamentally about culture before tools. The core principles include:

• Collaboration: Developers and operators work together from day one
• Automation: Eliminate manual, repetitive tasks
• Measurement: Track metrics to drive decisions
• Sharing: Knowledge and responsibility are distributed
• Continuous Improvement: Iterate on processes and tools

The DevOps lifecycle flows through Plan → Code → Build → Test → Release → Deploy → Operate → Monitor, then back to Plan. Each phase is automated and integrated.

DevOps vs Traditional Operations

Traditional operations teams manage infrastructure reactively—responding to incidents, manually deploying code, and maintaining systems in isolation. DevOps flips this model:

The Three Ways of DevOps

The First Way: Flow — Optimize the entire system for fast, smooth delivery. Reduce batch sizes, minimize handoffs, and automate repetitive work.

The Second Way: Feedback — Amplify feedback loops. Monitor production, share metrics with teams, and use data to drive decisions.

The Third Way: Continuous Learning — Foster experimentation, blameless postmortems, and a culture of improvement.

Platform Engineering

Platform Engineering builds on DevOps by creating self-service platforms that abstract infrastructure complexity. Instead of each team managing their own infrastructure, a platform team provides:

• Golden paths: Opinionated, pre-configured deployment patterns
• Service catalogs: Discoverable, reusable infrastructure components
• Developer experience: Simple APIs and CLIs for common tasks
• Compliance & security: Built-in guardrails and policies

This reduces cognitive load on product teams and accelerates delivery.

AWS in DevOps

AWS provides native services for every stage of the DevOps lifecycle:

# CodePipeline orchestrates CI/CD workflows
# CodeBuild compiles and tests code
# CodeDeploy automates deployments
# CloudFormation and Terraform manage infrastructure
# CloudWatch monitors applications and infrastructure
# Systems Manager automates operational tasks

Key Metrics: DORA Metrics

The DevOps Research and Assessment (DORA) team identified four metrics that predict software delivery performance:

1. Deployment Frequency: How often code is deployed to production
2. Lead Time for Changes: Time from code commit to production
3. Mean Time to Recovery (MTTR): Time to restore service after incident
4. Change Failure Rate: Percentage of deployments causing incidents

High-performing teams deploy multiple times per day with low failure rates.

Getting Started with DevOps

A practical DevOps journey typically follows this path:

1. Automate builds — Use CI to catch issues early
2. Automate deployments — Use CD to reduce manual work
3. Monitor everything — Instrument applications and infrastructure
4. Incident response — Establish runbooks and blameless postmortems
5. Infrastructure as Code — Manage infrastructure like code
6. Platform engineering — Build self-service capabilities