What is CI/CD? Complete Beginner’s Guide 2025

Introduction — Why CI/CD Matters Today

CI/CD stands for Continuous Integration and Continuous Delivery (or Continuous Deployment). It helps teams deliver software faster and with fewer bugs. In 2025, teams use CI/CD to handle cloud services, containers, microservices, and stricter security rules. Good CI/CD pipelines reduce manual work, catch errors early, and create reliable releases.

This guide explains CI/CD simply. You will learn what CI/CD is, how pipelines work, common tools, testing strategies, security practices, and a step‑by‑step plan to build your first pipeline.

History and Evolution of CI/CD

CI/CD began as a set of practices to make software integration less painful.

• Early 2000s: Continuous Integration emerged to automate builds and basic tests every time code changed.
• 2010s: Continuous Delivery and Deployment grew with cloud and container adoption. Teams automated deployment to staging and production.
• 2020s: GitOps and Infrastructure as Code (IaC) made deployments declarative. Policy-as-code and supply-chain security became crucial.
• 2024–2025: AI-assisted testing, serverless CI runners, stronger SBOM and SLSA compliance, and GitOps-first workflows are common.

The trend: more automation, more security checks earlier in the pipeline, and more declarative, observable deployments.

Core Concepts: Continuous Integration, Continuous Delivery, Continuous Deployment

Continuous Integration (CI)

• Developers merge code frequently to a main branch.
• Each merge triggers automated builds and tests.
• CI finds integration issues early.

Continuous Delivery (CD)

• Builds are tested and packaged automatically.
• Deployments to production are possible at any time with one button or command.
• Teams still choose when to release.

Continuous Deployment (CD)

• Every passing change is deployed automatically to production.
• This requires strong test coverage and monitoring.

Other related ideas

• Pipeline-as-code: pipelines are stored alongside code as configuration.
• Trunk-based development: short-lived branches, frequent merges.
• Feature flags: turn features on or off without deploying new code.

CI/CD Pipeline Components and How They Work

A typical pipeline has stages that run automatically when code changes.

Source

• Code is stored in a version control system like Git.
• Pull requests or pushes trigger the pipeline.

Build

• Compile or assemble the application.
• Produce artifacts such as binaries or container images.

Test

• Run unit tests first (fast).
• Run integration and E2E tests later (slower).
• Include security scans and linting.

Artifact Storage

• Store built artifacts in a registry (Docker Registry, Maven, npm).
• Use versioned artifacts to ensure reproducible deployments.

Deploy

• Deploy artifacts to staging or production.
• Use strategies like blue/green, canary, or rolling updates.

Verify and Monitor

• Run smoke tests after deployment.
• Monitor logs, metrics, and traces.
• Roll back if problems appear.

Feedback Loop

• Pipeline results create tickets or alerts if something fails.
• Developers fix issues quickly and re-run the pipeline.

Popular Tools and Platforms in 2025

By 2025, the landscape includes mature hosted services and strong open-source projects.

Hosted/Managed:

• GitHub Actions — integrated, flexible, strong marketplace.
• GitLab CI/CD — full DevOps platform with built-in registry.
• Azure DevOps — enterprise pipelines with Azure integration.
• AWS CodePipeline / CodeBuild — integrates with AWS services.
• Google Cloud Build — fast cloud-native builders.
• Harness, CircleCI, Buildkite — specialty CI platforms with enterprise features.

Open-source / Cloud-native:

• Jenkins — still widely used, plugin-heavy.
• Tekton — Kubernetes-native pipeline building blocks.
• ArgoCD — GitOps continuous delivery for Kubernetes.
• Flux — GitOps tool focused on reconciliation.
• Spinnaker — large-scale deployment orchestration.
• Drone, Concourse — lightweight alternatives.

Testing and security:

• Snyk, Dependabot, WhiteSource — dependency scanning.
• SonarQube — code quality and static analysis.
• OWASP ZAP, Burp — dynamic testing.
• Trivy — container and image scanning.

IaC and provisioning:

• Terraform, Pulumi, AWS CloudFormation, CDK, Crossplane.

Observability:

• Prometheus, Grafana, Jaeger, Honeycomb, Datadog, New Relic.

Expect more AI helpers in 2025: test generation, flaky test detection, and pipeline optimization tools.

Designing a CI/CD Workflow: Best Patterns and Examples

Choose patterns that match team size, risk tolerance, and architecture.

Trunk-based development + short-lived feature branches

• Merge small changes often.
• Use fast CI checks on PRs and more tests on main.

Pipeline-as-code

• Store pipeline configs in the repo.
• Use templates and reusable steps.

Environments and promotion-based releases

• Use separate environments: dev → staging → production.
• Promote the same artifact across environments to reduce risk.

Deployment strategies

• Canary: route a small percentage of traffic to new version.
• Blue/Green: switch traffic between two environments.
• Rolling: gradually update instances.
• Feature flags: release code behind flags to control exposure.

Monorepo vs polyrepo

• Monorepo can simplify cross-project changes but needs careful build optimization.
• Polyrepo keeps services separate and easier to scale for small teams.

Example simple workflow

• Push to main → run lint + unit tests → build artifact → push to registry → deploy to staging → run integration tests → manual approval → deploy to production.

Example GitOps workflow

• CI builds artifact and updates a GitOps repo with new image tag.
• ArgoCD or Flux notices the change and deploys to cluster automatically.

Testing Strategy in CI/CD: Unit, Integration, E2E, and Shift‑Left

Testing must be layered and fast where possible.

Unit tests

• Fast and isolated.
• Run on every commit.

Integration tests

• Test interactions between services or components.
• Run on feature merges or nightly depending on cost.

End-to-end (E2E) tests

• Test the full user flow.
• Run against staging; keep them focused and reliable.

Shift-left testing

• Bring security and quality checks earlier.
• Run linting, static analysis, dependency checks in CI.

Contract testing

• Use tools like Pact to verify API contracts between services.

Test data and environments

• Use synthetic or mocked data for speed.
• Use ephemeral environments (created per PR) when possible for realistic tests.

Flaky tests

• Detect and quarantine flaky tests.
• Invest in stable tests before scaling deployment automation.

Parallelism and caching

• Run tests in parallel where safe.
• Cache dependencies and build outputs to reduce time.

Mutation testing and coverage

• Use mutation testing for stronger confidence.
• Track coverage but focus on meaningful tests.

Infrastructure as Code and Environment Provisioning

IaC makes environments repeatable and versioned.

Popular IaC tools

• Terraform — cloud-agnostic, declarative.
• Pulumi — code-first IaC using general-purpose languages.
• CloudFormation / CDK — AWS-native.
• Crossplane — Kubernetes-native provisioning.

Ephemeral environments

• Create temporary environments per branch.
• Use short-lived resources to test in production-like settings.

Environment configuration

• Separate config from code using configuration files, secrets managers, or environment variables.
• Use secrets management: HashiCorp Vault, AWS Secrets Manager, Azure Key Vault.

Immutable infrastructure

• Instead of changing servers, deploy new instances with updated images.
• Containers and serverless functions support this well.

Policy-as-code

• Enforce rules (allowed regions, instance sizes) with tools like OPA (Open Policy Agent) or Terraform Sentinel.

Versioning and drift

• Store IaC in Git to track changes.
• Run periodic drift detection to catch unmanaged changes.

Security and Compliance in CI/CD (DevSecOps)

Security is part of every stage in 2025 pipelines.

Shift-left security

• Run SAST (static analysis), secret scanning, and dependency checks early.
• Block merges on critical security findings.

Supply-chain protection

• Generate and verify SBOMs (Software Bill of Materials).
• Aim for SLSA levels where appropriate.
• Sign artifacts and verify signatures during deployment.

Dynamic testing

• Run DAST (dynamic analysis) against staging apps.
• Include interactive application security testing where needed.

Secrets and credentials

• Never commit secrets to Git.
• Use secrets managers and short-lived credentials.
• Limit access with least privilege.

Policy and compliance

• Integrate compliance checks into pipelines.
• Automate evidence collection for audits.

Runtime security

• Use container hardening, image scanning, and runtime protection.
• Monitor for anomalies and suspicious activity.

Observability for security

• Collect logs, network flow data, and metrics to detect security incidents.

Monitoring, Observability, and Feedback Loops

CI/CD does not end at deployment. Observability completes the loop.

Key observability signals

• Metrics: error rates, latency, throughput.
• Logs: structured logs for quick diagnosis.
• Traces: distributed traces to find slow paths.
• Alerts: tuned to reduce noise and signal real issues.

SLOs and SLIs

• Define Service Level Indicators (SLIs) and Objectives (SLOs).
• Use SLOs to decide whether to roll back or pause an automated deployment.

Automated rollback and remediation

• Use health checks or canary analysis to trigger rollbacks.
• Integrate runbooks for common incidents.

Feedback to developers

• Send pipeline and runtime failures directly to the team.
• Connect observability with ticketing and chat tools.

Chaos engineering

• Run controlled experiments to test resilience.
• Incorporate lessons back into pipelines and tests.

Scaling CI/CD and Team Adoption Best Practices

Technical scaling

• Use runner autoscaling or managed CI to handle bursts.
• Cache builds and use remote build caching.
• Split long pipelines into smaller, composable jobs.
• Use monorepo-aware build tools for large codebases.

Organizational scaling

• Create platform teams to maintain shared pipelines, templates, and tooling.
• Provide self-service pipelines that developers can use easily.
• Offer clear guardrails but keep developer autonomy.

Metrics to measure

• Lead time for changes.
• Deployment frequency.
• Mean time to recovery (MTTR).
• Change failure rate.

Change management

• Start small: automate one flow first.
• Train teams and document patterns.
• Share successes and failures openly.

Cultural practices

• Encourage small, reversible changes.
• Use feature flags rather than long-lived branches.
• Celebrate improvements in delivery speed and quality.

Getting Started: Step‑by‑Step Guide to Build Your First Pipeline

This is a simple path to a working pipeline using common tools. Adjust details for your stack.

1. Pick your repo and branch strategy

• Use a Git hosting service (GitHub/GitLab).
• Use trunk-based development or short feature branches.

2. Write pipeline-as-code

• Choose GitHub Actions, GitLab CI, or any pipeline tool.
• Create a simple pipeline file in your repo (e.g., .github/workflows/ci.yml).

3. Add basic steps

• Checkout code.
• Install dependencies.
• Run lint and unit tests.
• Build artifact (binary or container image).
• Push artifact to a registry (Docker Hub, GitHub Container Registry).

4. Deploy to staging

• Use a deployment provider (cloud CLI, Terraform, Kubernetes).
• Keep deploy step gated behind successful tests.

5. Add security and quality checks

• Add dependency scanning and SAST stage.
• Fail the pipeline on critical issues.

6. Add monitoring and health checks

• Deploy a health endpoint.
• Run a basic smoke test after deployment.

7. Introduce a manual approval step (for Continuous Delivery)

• Require a manual approval before production.

8. Iterate and improve

• Add integration and E2E tests.
• Implement canary or blue/green deploys.
• Add ephemeral environments for PRs if needed.

Minimal example (conceptual)

• On push to main:
  • Run unit tests (fast)
  • Build container image
  • Scan image for vulnerabilities
  • Push image to registry
  • Update GitOps repo or trigger deployment to staging
  • Run smoke tests

9. Secure your pipeline

• Store secrets in a secrets manager.
• Limit pipeline access and role permissions.
• Sign artifacts.

10. Measure and automate rollback

• Define SLOs and health checks.
• Automate canary analysis and rollback on failures.

Final tips

• Start with a small, reliable pipeline. Fast feedback beats running every possible test.
• Add more checks gradually.
• Keep pipeline steps independent and well-organized.
• Automate repetitive tasks, and keep manual approvals for high-risk changes until you gain confidence.

Closing

CI/CD is the backbone of modern software delivery. In 2025, pipelines are more automated, more secure, and more integrated with cloud-native tooling than ever. Start small, focus on fast feedback, include security early, and measure outcomes. Over time, you will move from basic automation to a mature CI/CD practice that speeds delivery while keeping users safe.

If you want, I can:

• Create a sample GitHub Actions pipeline for your specific language and framework.
• Help choose tools for your team size and cloud provider.
• Walk through a hands-on example deploying to a Kubernetes cluster.