How to organize a full CI/CD cycle for three teams…

To organize a full CI/CD cycle for three teams, the following approaches and tools can be used:

Unified CI/CD Platform: Using a centralized platform such as Jenkins, GitLab CI/CD, GitHub Actions, Azure DevOps, or CircleCI. This allows standardizing processes, ensuring transparency, and managing all pipelines from a single place.
Monorepo or Polyrepos:
- Monorepo: All teams work within one repository. It requires more complex branching and trigger setups to run pipelines only on changes in relevant parts of the codebase. Simplifies dependency management and versioning.
- Polyrepos: Each team has its own repositories. Simplifies isolation but complicates dependency management between teams and standardization of processes.
Process Standardization:
- Unified pipeline formats: Using declarative pipelines (Jenkinsfile, .gitlab-ci.yml, .github/workflows/*.yml) to describe CI/CD steps in code.
- Shared libraries/templates: Creating reusable steps or templates for common tasks (build, test, deploy) to reduce duplication and ensure consistency.
- Version control system (VCS): Using a single VCS (Git) with clear branching policies (Gitflow, Trunk-based Development).
CI/CD Pipeline:
- CI (Continuous Integration):
  - Trigger on push to development branch (e.g., develop or feature branches).
  - Fetch source code.
  - Build the application (compile, package).
  - Automated testing (unit, integration tests).
  - Code analysis (static analysis, standards check).
  - Create artifact (Docker image, JAR, WAR, etc.) and publish it to artifact repository (Nexus, Artifactory, Docker Registry).
  - Notify about success/failure.
- CD (Continuous Delivery/Deployment):
  - Trigger after successful CI completion and/or on merge to main branch (e.g., main/master).
  - Retrieve artifact from repository.
  - Deploy to testing environment (dev/staging).
  - Conduct deeper testing (functional, smoke, performance).
  - For Continuous Delivery: Stop and wait for manual confirmation to deploy to production.
  - For Continuous Deployment: Automatic deployment to production after successful tests.
  - Use configuration management and deployment tools (Ansible, Chef, Puppet) or orchestration (Kubernetes with Helm/Argo CD/Flux).
  - Check application health post-deployment.
  - Rollback if issues occur.
Tools:
- VCS: Git (GitHub, GitLab, Bitbucket).
- CI/CD Platforms: Jenkins, GitLab CI/CD, GitHub Actions, Azure DevOps, CircleCI.
- Build Tools: Maven, Gradle, npm, yarn, pip, Go Modules, Docker build.
- Testing Frameworks: JUnit, TestNG, Pytest, Mocha, Jest.
- Static Analysis: SonarQube, Linters (ESLint, Pylint).
- Artifact Repository: Nexus, Artifactory, Docker Registry.
- Configuration Management: Ansible, Chef, Puppet.
- Containerization: Docker.
- Orchestration: Kubernetes, Docker Swarm.
- Deployment Strategies: Blue/Green, Canary, Rolling Update.
- Monitoring & Logging: Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana).
- Notification: Slack, Email, Microsoft Teams.
Responsibility and Access Control:
- Clearly define roles and access rights to tools and environments for each team.
- Use secrets and environment variables for secure credential storage.
Feedback:
- Integrate pipelines with notification systems and monitoring tools so teams are quickly informed of issues.

Example of a pipeline structure for one team (using GitLab CI/CD):

# .gitlab-ci.yml
stages:
  - build
  - test
  - deploy

variables:
  DOCKER_IMAGE: $CI_REGISTRY_IMAGE:$CI_COMMIT_SHORT_SHA
  TEST_IMAGE: $CI_REGISTRY_IMAGE/testrunner:$CI_COMMIT_SHORT_SHA

build_job:
  stage: build
  image: docker:latest
  services:
    - docker:dind
  script:
    # Build application (example for Java with Maven)
    # ./mvnw clean package -DskipTests
    # Build Docker image
    docker build -t $DOCKER_IMAGE .
    # Login to GitLab Container Registry
    docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
    # Push Docker image
    docker push $DOCKER_IMAGE
  artifacts:
    paths:
      - target/*.jar # Or other artifacts

test_job:
  stage: test
  image: $DOCKER_IMAGE # Use the built image or a separate test image
  script:
    # Run unit and integration tests
    # For Java:
    # java -jar target/my-app.jar --run-tests
    # For Node.js:
    # npm test
    echo "Running tests..."
    sleep 10 # Placeholder
  needs:
    - build_job

deploy_staging_job:
  stage: deploy
  image: alpine/curl # Or an image with deployment tools
  script:
    # Deploy to staging environment
    # For example, curl API or kubectl command
    echo "Deploying $DOCKER_IMAGE to staging..."
    # kubectl apply -f k8s/staging.yaml --namespace <team-namespace>
    sleep 10 # Placeholder
  environment:
    name: staging
    url: https://staging.your-app.com
  only:
    - main # Trigger on merge to main

deploy_production_job:
  stage: deploy
  image: alpine/curl # Or an image with deployment tools
  script:
    # Deploy to production environment
    echo "Deploying $DOCKER_IMAGE to production..."
    # kubectl apply -f k8s/production.yaml --namespace <team-namespace>
    sleep 10 # Placeholder
  environment:
    name: production
    url: https://your-app.com
  when: manual # Manual confirmation for production (Continuous Delivery)
  only:
    - main
  needs:
    - deploy_staging_job

For three teams, this can be scaled by creating separate pipelines (or sections/directories in a monorepo), possibly using shared templates or libraries for common steps like container build or Kubernetes deployment. Each team manages its own code and pipelines but within a unified platform and standards.

How to organize a full CI/CD cycle for three teams?