CI/CD Pipeline Tutorial with Example
Thomas Hamilton
⚡ Smart Summary
CI/CD Pipeline is the automated path that takes a code change from a commit to production. It builds, tests, and deploys software through four stages — Source, Build, Test, and Deploy — slashing manual errors and shortening the loop between developers and end users.
What is a CI/CD Pipeline?
A CI/CD pipeline automates the path that a code change takes from a developer’s commit to a deployed release. It builds the code, runs the test suite, and safely promotes the binary into one or more environments. A well-designed pipeline reduces manual errors, returns fast feedback to developers, and enables small, frequent product iterations.
The pipeline ties together automation and continuous monitoring across the whole software lifecycle — from integration and testing to delivery and deployment. The connected practices are collectively referred to as a CI/CD pipeline.
What is Continuous Integration, Continuous Delivery, and Continuous Deployment?
- Continuous Integration (CI): a development practice in which team members integrate their work at least once a day. Every integration is verified by an automated build and test run that surfaces errors quickly.
- Continuous Delivery (CD): an engineering practice in which the team keeps the product in a release-ready state at the end of every iteration. The deployment to production is typically a manual approval.
- Continuous Deployment (CD): takes Continuous Delivery one step further — every change that passes automated checks is deployed to production automatically, without a human gate.
Stages of a CI/CD Pipeline
A CI/CD pipeline is an executable specification of the steps required to ship a new version of the software. A failure at any stage triggers a notification — via email, Slack, or another channel — so that the responsible engineer is alerted immediately.
Stages of a CI/CD pipeline.
Source Stage
The pipeline is triggered by a change in the code repository. Any commit notifies the CI/CD tool to run the matching pipeline. Other common triggers include user-initiated workflows, scheduled runs, and the results of upstream pipelines.
Build Stage
The build stage compiles the source code and resolves its dependencies into a runnable artefact. Compiled languages such as C++, Java, C, and Go must produce binaries here. Interpreted languages such as JavaScript, Python, and Ruby still benefit from this stage to package dependencies, lint, and produce container images.
A failure in the build stage usually indicates a fundamental project misconfiguration, so the issue must be addressed immediately.
Test Stage
The test stage executes the automated test suite to validate code correctness and software behaviour. It catches easily reproducible bugs before they reach customers. Writing and maintaining the tests is the developer’s responsibility — the pipeline only enforces that they run.
Deploy Stage
The deploy stage promotes the verified artefact into a target environment such as staging, UAT, or production. Once the build has passed every required test, the deployment can be triggered automatically (Continuous Deployment) or after a manual gate (Continuous Delivery).
Example of a CI/CD Pipeline
The walkthrough below shows a concrete pipeline that uses GitHub, CircleCI, and AWS.
- Source Code Control: host the application code in a private GitHub repository so it integrates with build, scan, and deploy services.
- Continuous Integration: connect CircleCI to the repository so that every push pulls the new code, builds it, and executes the test suite.
- Deploy to UAT: configure CircleCI to deploy successful builds to an AWS UAT environment for stakeholder testing.
- Deploy to Production: reuse the same CI/CD steps to promote the artefact to production, gated by manual approval if you are practising Continuous Delivery.
CI/CD Pipeline Best Practices
The practices below keep pipelines fast, reliable, and trusted by the team:
- Document the current delivery process before you automate, so you know what to keep, change, or remove.
- Start with a small proof of concept rather than automating the entire pipeline at once.
- Structure the pipeline as multiple stages so fast, fundamental checks run first.
- Start every workflow from a clean, isolated environment to keep builds reproducible.
- Add open-source tooling that covers everything from code style to security scanning.
- Use a code quality hub that runs the same tests against every branch.
- Peer-review every pull request and treat the pipeline output as part of the review.
- Define success metrics before you begin the transition — they drive continuous improvement.
Advantages of CI/CD Pipelines
A mature CI/CD pipeline delivers several measurable benefits:
- Replaces error-prone manual builds and releases with reliable automation.
- Improves the consistency and quality of every artefact shipped.
- Increases flexibility — new functionality can be shipped on demand.
- Streamlines communication between developers, QA, and operations.
- Accelerates customer feedback loops with frequent releases.
- Increases product visibility through dashboards, logs, and notifications.
- Removes manual errors and reduces release cost.
- Shortens the software development lifecycle.
- Provides a rapid feedback loop from developer to end user.
- Highlights changes that break the build so they can be reverted quickly.
- Pairs automated tests with light manual exploratory testing to keep quality high.
Important CI/CD Tools
Modern teams choose from a healthy ecosystem of CI/CD tools. The most widely adopted are described below.
1) Jenkins
Jenkins is an open-source Continuous Integration server that automates build, test, and release workflows. Written in Java and supported by a large plugin ecosystem, Jenkins powers hundreds of thousands of installations worldwide.
Features:
- Builds and tests code many times per day.
- Automates the entire build/test pipeline, saving time and reducing defects.
- Deploys code after every successful build and test.
- Speeds up the development cycle through fast feedback.
Link: https://www.jenkins.io/download/
2) Bamboo
Bamboo is a continuous-integration build server that bundles automatic build, test, and release in a single tool. It integrates seamlessly with Jira Software and Bitbucket.
Features:
- Runs parallel batch tests.
- Quick to install and configure.
- Per-environment permissions let developers and QA deploy to their own environments.
- Built-in Git branching workflows that automatically merge branches.
Link: https://www.atlassian.com/software/bamboo
3) CircleCI
CircleCI is a flexible CI tool that runs builds for cross-platform mobile apps, Python APIs, Docker clusters, and more. It reduces bugs and improves application quality through fast feedback.
Features:
- Choice of build environments.
- Supports many languages including C++, JavaScript, .NET, PHP, Python, and Ruby.
- Docker support for custom build images.
- Automatically cancels queued or running builds when a newer build is triggered.
Link: https://circleci.com/
4) GitHub Actions, GitLab CI, and Argo CD
GitHub Actions and GitLab CI integrate CI/CD directly into the source-control platform, removing the need for an external build server. Argo CD adds GitOps-style continuous deployment for Kubernetes clusters, reconciling the cluster to the desired state declared in Git.
Why Does the CI/CD Pipeline Matter for IT Leaders?
- Improves release reliability and predictability.
- Makes the engineering organisation more attractive to developers.
- Pulls code from version control and executes the build with one click.
- Moves binaries safely into target environments.
- Lets project leaders manage environment variables and configuration per environment.
- Publishes application components to web, database, API, and other services.
- Surfaces log data and alerts on the state of each release.
- Verifies code changes before they progress, reducing defects in production.
CI/CD Pipeline KPIs
- Cycle or Deployment Time: the time it takes to move from build to production. Trends reveal bottlenecks in your delivery process.
- Deployment Frequency: how often you ship. More frequent, smaller releases reduce risk and make failures easier to fix.
- Change Lead Time: the time from the start of development to a successful deployment. Reflects how well planning, coding, and delivery work together.
- Change Failure Rate: the percentage of deployments that cause incidents or require rollback. Tracks the safety of the pipeline.
- MTTR (Mean Time to Recovery): the average time to restore service after a failure. Measures resilience.
- MTTF (Mean Time to Failure): the average time between failures. Tracks long-term reliability.
