CI/CD Pipeline And Workflow

What this diagram shows

A CI/CD pipeline visualises the automated journey from code commit through to production deployment. It shows how source code changes trigger a chain of stages: version control system detects a commit, build tools compile and package the code, test suites run unit and integration tests, artefacts move to staging or pre-production for further validation, and finally approved builds deploy to production. The diagram clarifies decision points (pass/fail gates), parallel execution paths, and feedback loops where failures halt progression and notify developers.

Key components

• Source Control Repository — Receives code commits from developers and triggers the pipeline via webhooks or polling.
• Build Server — Compiles source code, resolves dependencies, and creates executable artefacts or container images.
• Automated Test Suite — Runs unit tests, integration tests, and security scans to validate code quality before advancement.
• Artifact Registry — Stores built packages, Docker images, or binaries for retrieval during deployment stages.
• Staging Environment — Mirrors production infrastructure where pre-release versions undergo final smoke tests and performance validation.
• Deployment Service — Orchestrates the release of validated artefacts to production infrastructure or Kubernetes clusters.
• Monitoring and Feedback — Tracks production health and application metrics, sending alerts back to the team if issues emerge post-deployment.

When to use it

Use this template when planning or documenting your team's continuous integration and deployment workflow, especially for software teams adopting DevOps practices. It is essential for onboarding new engineers, reviewing bottlenecks in your release process, identifying where manual approvals are needed, and communicating automation scope to stakeholders. Works well for teams using Jenkins, GitLab CI, GitHub Actions, CircleCI, or cloud-native platforms.

Common mistakes

• Treating the pipeline as linear only and omitting parallel test stages, which delays feedback and wastes build time.
• Forgetting to show manual approval gates or environment-specific constraints, leaving teams unsure where human decisions occur.
• Excluding monitoring and rollback paths, implying that deployment is the final stage rather than showing ongoing observability and failure recovery.

Adapting it to your system

Replace generic stage names with your actual tools: name your repository platform (GitHub, GitLab, Bitbucket), specify your build system (Maven, Gradle, npm, Go modules), list the actual test types you run, name your container registry if applicable, and identify your deployment targets (AWS, Kubernetes, on-premise). Add environment-specific branches if you deploy to dev, staging, and production separately. Include any custom approval workflows, security scanning tools, or database migration steps unique to your system.