DevOps

Reusable, **project-agnostic** blueprint for **DevOps** — the discipline of bridging development and operations to enable continuous, reliable delivery through culture, automation, measurement, and sh

DevOps

Reusable, project-agnostic blueprint for DevOps — the discipline of bridging development and operations to enable continuous, reliable delivery through culture, automation, measurement, and sharing.

DevOps answers "how do we bridge development and operations for continuous, reliable delivery?" — a question that spans the entire SDLC (especially Build → Verify → Release → Operate) and connects to PDLC launch (P4) and growth (P5).

Document Purpose
DEVOPS.md CALMS framework, Three Ways, DORA metrics, maturity model, cultural principles, competencies
DevOps ↔ SDLC ↔ PDLC bridge How DevOps maps across SDLC phases A–F and PDLC phases P1–P6 — emphasis on Build/Verify/Release/Operate
practices/ Deep guides: CI/CD pipelines, infrastructure as code, GitOps, observability, incident management, chaos engineering
tooling/ Container orchestration, artifact management, secrets management, deployment strategies

Relationship to other packages

Package How DevOps relates
SDLC blueprint DevOps practices underpin SDLC phases D–F (Build, Verify, Release) and extend into Operate. The DevOps — deep-dive package (blueprint) package is the methodology lens — how DevOps shapes SDLC phases, ceremonies, and roles. This discipline package is the broader knowledge base.
Product development lifecycle (PDLC) PDLC P4 (Launch) relies on DevOps deployment pipelines. P5 (Grow) depends on observability and incident management to measure outcomes and maintain reliability.
Testing & quality assurance DevOps CI/CD pipelines automate test execution. The test pyramid informs pipeline stage design. Shift-left testing is a shared concern between testing and DevOps.
Software architecture Architecture decisions (microservices, containers, cloud-native) enable or constrain DevOps practices. Infrastructure as code is an architecture-DevOps intersection.
Big data & data engineering DataOps applies DevOps principles to data pipelines — CI/CD for data, data quality gates, pipeline observability.

Scope

This package covers DevOps as a discipline — not just CI/CD tooling. It includes:

  • Culture — collaboration between dev and ops, shared responsibility, blameless postmortems
  • Automation — CI/CD pipelines, infrastructure as code, configuration management
  • Measurement — DORA metrics (deployment frequency, lead time, change failure rate, MTTR)
  • Continuous delivery — deployment strategies (blue-green, canary, feature flags), release management
  • Observability — monitoring, logging, tracing, alerting, SLOs/SLIs/SLAs
  • Incident management — on-call, incident response, postmortem process
  • Site reliability engineering (SRE) — error budgets, toil reduction, capacity planning
  • Security integration (DevSecOps) — shift-left security, supply chain security, compliance automation

The DevOps methodology lens (how DevOps shapes SDLC phases, ceremonies, and roles) remains in DevOps — deep-dive package (blueprint). This package provides the broader discipline knowledge base that the methodology lens references.

Reference bodies of knowledge: DORA State of DevOps, Google SRE handbook, DevOps Institute, The Phoenix Project / The Unicorn Project.


Keep project-specific CI/CD configuration in docs/development/CI-CD.md and .github/workflows/, not in this file.

Canonical source

Edit https://github.com/autowww/blueprints/blob/main/disciplines/engineering/devops/README.md first; regenerate with docs/build-handbook.py.