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Platform Engineering

Master plan for building internal developer platforms (IDPs) that enable self-service infrastructure and improve developer experience. This skill covers platform design principles, golden paths, abstraction layers, and measuring platform adoption and effectiveness.

Status: 🟢 Master Plan Available

Key Topics​

  • Platform Vision: Developer experience goals, golden paths, self-service capabilities, platform as product
  • Platform Architecture: Control plane, data plane, API design, extensibility patterns
  • Service Catalog: Service templates, scaffolding, standardization, best practices enforcement
  • Developer Portal: Unified interface, documentation, service discovery, status dashboards
  • Infrastructure Abstraction: Hiding complexity, sensible defaults, escape hatches for advanced users
  • Automation: Self-service provisioning, automated workflows, policy enforcement
  • Observability: Platform metrics, developer experience metrics, cost visibility
  • Platform Adoption: Onboarding, training, feedback loops, measuring success

Primary Tools & Technologies​

  • Platform Frameworks: Backstage, Port, Humanitec, Kratix, Crossplane
  • Service Mesh: Istio, Linkerd, Consul Connect
  • API Gateway: Kong, Ambassador, Traefik, Nginx
  • Developer Portals: Backstage, Cortex, OpsLevel
  • Templating: Cookiecutter, Yeoman, Copier, custom scaffolding
  • Policy Engines: OPA, Kyverno, Cloud Custodian
  • Documentation: Docusaurus, MkDocs, GitBook, Confluence
  • Workflow Automation: Temporal, Airflow, Argo Workflows

Integration Points​

  • Building CI/CD Pipelines: Platform-provided pipeline templates
  • GitOps Workflows: Platform manages GitOps configurations
  • Kubernetes Operations: Kubernetes as platform substrate
  • Infrastructure as Code: IaC abstracted through platform
  • Security Operations: Security policies enforced by platform
  • Observability: Observability built into platform services
  • Cloud Cost Management: Cost tracking and optimization in platform
  • Incident Management: Platform-assisted incident response

Use Cases​

  • Building internal developer platform from scratch
  • Implementing golden path for microservices
  • Creating self-service database provisioning
  • Designing developer onboarding experience
  • Standardizing CI/CD across teams
  • Building service catalog with templates
  • Implementing platform observability
  • Measuring platform adoption and ROI

Decision Framework​

Platform scope based on organization:

  • Small teams (< 50 devs): Focus on automation, documentation, simple self-service
  • Medium teams (50-200 devs): Developer portal, service catalog, standardized templates
  • Large teams (200+ devs): Full IDP, multi-tenancy, advanced abstractions, dedicated platform team

Build vs buy:

  • Build: Unique requirements, existing tools, full control, engineering capacity
  • Buy/Adopt: Standard needs, faster time-to-value, limited resources, proven solutions

Abstraction level:

  • Low abstraction: Expose Kubernetes directly, Terraform modules, manual configuration
  • Medium abstraction: Service templates, guided workflows, sensible defaults, escape hatches
  • High abstraction: Fully managed services, minimal configuration, opinionated workflows

Golden path design:

  1. Identify most common use cases (80/20 rule)
  2. Create opinionated, frictionless path for common cases
  3. Provide escape hatches for advanced needs
  4. Document both paths clearly
  5. Measure adoption and iterate

Platform Success Metrics​

  • Developer satisfaction: NPS, surveys, feedback
  • Time to first deploy: Onboarding effectiveness
  • Lead time for changes: Platform impact on velocity
  • Platform adoption rate: Services using platform vs total
  • Self-service usage: Tickets avoided through automation
  • Mean time to recovery: Platform impact on reliability
  • Cost efficiency: Resource utilization, waste reduction

Platform Anti-Patterns to Avoid​

  • Building without developer input (ivory tower platform)
  • Forcing migration without clear value (adoption resistance)
  • Over-abstracting and hiding too much (loss of control)
  • Under-abstracting and exposing too much complexity (no improvement)
  • Lack of documentation and training (poor adoption)
  • No feedback loops (platform drift from needs)
  • Platform team as gatekeeper (bottleneck)