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Continuous Integration and Continuous Delivery
In modern software development, CI/CD is a central practice that connects code, automation, and operations into a repeatable and reliable workflow. On Linux systems this often takes the form of automated pipelines that run on servers or containers, triggered whenever code changes.
This chapter focuses on the ideas behind CI/CD, not on specific tools, which are covered later.
From Manual Releases to Automated Pipelines
Before CI/CD, teams often wrote code for days or weeks, then tried to merge everything together at once, and finally deployed with a long checklist of manual steps. This process was slow and error prone. CI/CD changes this by turning integration, testing, and delivery into small, frequent, automated steps.
In a CI/CD workflow, every change to the codebase travels through a pipeline of automated checks. These checks run on shared infrastructure, very often Linux servers or containers. The goal is to discover problems early, while each change is still small and easier to fix.
Continuous Integration
Continuous Integration, often called CI, is the practice of frequently integrating code changes into a shared repository and automatically verifying them.
With CI, developers commit their work regularly, for example several times per day. Each commit triggers automated processes that run on a CI server. These processes typically include fetching the latest code, building it, and running tests. If something fails, the commit is flagged and the team is notified.
The main ideas of CI are simple. Integrate often, test automatically, and fix problems quickly. This reduces the risk of large, conflicting changes and helps keep the main branch of the repository in a working state.
In CI, every change to the shared main branch must pass automated checks before it is considered healthy.
On Linux based CI systems, this usually runs inside a controlled environment. The CI job uses scripts, often written in shell or YAML based configuration, to define the build and test steps. Because the environment is automated and reproducible, the same tests can run consistently for every change.
Continuous Delivery and Continuous Deployment
Continuous Delivery, or CD, builds on CI. Where CI focuses on building and testing, Continuous Delivery focuses on preparing software so it can be deployed to production at any time.
In Continuous Delivery, the pipeline goes beyond tests. After the build and automated tests pass, the pipeline may create versioned artifacts, run more advanced testing, and prepare deployment packages. The end result is that the software is always in a releasable state. A human can trigger a deployment when the business is ready.
Continuous Deployment is a stricter practice. In Continuous Deployment, the pipeline does not stop after preparing a release. If all automated checks pass, the system automatically deploys the change to production, without a manual approval step.
The ideas can be summarized as follows. Continuous Integration answers the question, does the code build and do the tests pass for every change. Continuous Delivery extends this to, can we safely release this build at any time. Continuous Deployment answers, should every passing change go live automatically.
Every Continuous Deployment pipeline is a Continuous Delivery pipeline, but not every Continuous Delivery pipeline performs automatic production deployment.
Linux often plays a key role in CD. Many deployment targets, such as web servers, application servers, or container clusters, run Linux. The same scripting and automation approaches used in CI can be applied to deployment stages as well.
Stages of a Typical CI/CD Pipeline
A CI/CD pipeline is a sequence of automated stages that every change passes through. The exact structure varies between projects, but most pipelines on Linux systems share common stages.
The pipeline usually starts with source retrieval. The CI server checks out the code from the version control system into a clean workspace. This ensures that each run starts from a known state, with no leftover files.
Next comes the build stage. Here, the code is compiled or otherwise transformed into a usable form. For example, a C program might be compiled with gcc, a JavaScript application might be bundled, or a Python package might be prepared. On Linux based pipelines, this is commonly done using standard command line tools and build systems.
After the build, automated tests run. Unit tests check small pieces of functionality, integration tests check larger flows, and in some cases, system tests or smoke tests run as well. These tests help ensure that new changes do not break existing behavior.
Many pipelines then perform static analysis and quality checks. This can include code style checks, security scans, or analysis tools that look for common mistakes. Since these tools are often command line utilities, they integrate naturally into Linux based scripts.
Once the tests and checks pass, the pipeline may create artifacts. Artifacts are versioned build outputs, such as binaries, Docker images, or packages. They are stored so they can be deployed or reused later. For projects that use containers, the pipeline frequently builds an image and pushes it to a registry.
In more advanced CD pipelines, staging and production deployment stages appear. The pipeline first deploys to a staging environment for extra validation. After further checks or a manual approval, the same artifact is deployed to production.
Finally, monitoring and feedback complete the pipeline. Automated checks can observe deployed systems and alert the team if something goes wrong. Logs and metrics help the team improve tests and deployment rules over time.
A key CI/CD rule is that the same artifact that passed all tests must be the one that is deployed to production.
Triggers and Workflow
For CI/CD to be effective, pipelines need clear triggers. The most common trigger is a commit or merge to a version control branch. For example, every push to the main branch might run a full pipeline, while pushes to feature branches run a shorter set of checks.
Other useful triggers include pull or merge requests. When a developer proposes merging a feature branch into main, the CI system can automatically build and test the proposed changes, even before they are merged. This reduces the chance of breaking the main branch.
Scheduled triggers are also common. For example, a nightly build can run a more extensive test suite or perform system wide security scans. On Linux servers, these scheduled jobs often use cron or the CI tool’s internal scheduler.
Manual triggers exist too. A pipeline might require a human to click an approval button before deploying to production. This is typical in Continuous Delivery setups where teams want automation, but also want human control over release timing.
Underlying all of this is the idea of pipelines as code. Instead of describing the process in documents, the pipeline configuration lives alongside the source code. In many CI/CD tools, this configuration is a text file, often written in YAML, that describes the stages and steps. On Linux CI runners, these steps usually execute shell commands, scripts, or container based tasks.
Benefits and Trade-offs
The main benefit of CI/CD is faster and more reliable delivery of software. By catching defects early and automating repetitive tasks, teams reduce the pain of integration, testing, and deployment. Small, frequent releases are easier to reason about and to roll back if something goes wrong.
Another important benefit is reproducibility. Since builds and deployments are scripted and run in consistent environments, the chance of a change working on a developer’s machine but failing on the server is reduced. Linux based runners and containers help provide this consistency across different stages.
CI/CD also provides clear feedback. When a pipeline fails, the logs, test results, and reports show exactly where the problem occurred. Developers can investigate using familiar Linux tools and commands.
However, CI/CD also introduces trade-offs. Setting up pipelines requires time and initial effort. Tests must be written and maintained. Build times can grow if pipelines are not optimized. In large projects, teams must balance the depth of checks against the need for fast feedback.
Another trade-off is the level of automation. Continuous Deployment brings very rapid delivery, but it requires strong test coverage and reliable rollback strategies. Without these, automatic deployment of every change can increase risk.
Despite these trade-offs, CI/CD is widely adopted because it aligns well with Linux based development environments. The same scripting, automation, and tooling that power Linux systems also power CI/CD pipelines, making Linux a natural platform for implementing these principles.
Culture and Collaboration
CI/CD is not only about tools. It also influences team culture and collaboration. Since changes are integrated and tested frequently, communication about code quality and stability becomes more continuous.
Developers learn to keep changes small and focused so that they pass through the pipeline quickly. When a pipeline fails, there is a shared responsibility to investigate and fix it, especially for the main branch. This helps keep the main branch in a releasable state.
CI/CD also encourages transparency. Pipeline results are visible to the whole team. Everyone can see which builds passed, which failed, and which version is currently deployed. This shared visibility helps operations, developers, and other stakeholders stay aligned.
Linux fits naturally into this culture, because it is widely used for shared servers, automation scripts, and infrastructure. Teams often use Linux based runners, containers, and servers to implement the pipelines that support their collaborative practices.
CI/CD in the DevOps Context
Within DevOps, CI/CD is a core mechanism that connects development and operations activities. Development teams focus on writing code and tests, while operations teams ensure that the infrastructure and deployment processes are reliable, secure, and scalable. CI/CD pipelines bridge these roles by expressing deployment and operational checks as code, running on shared Linux infrastructure.
By applying CI/CD principles, DevOps teams can move from occasional large releases to frequent, incremental updates. Automation in CI/CD pipelines reduces manual handoffs and misunderstandings between teams. This supports the broader DevOps goal of delivering value quickly and safely.
As you explore specific tools for CI/CD on Linux later in the course, keep these principles in mind. Regardless of whether you use one platform or another, the essential ideas remain the same. Integrate early and often, automate your checks, keep your software always releasable, and let the pipeline provide continuous feedback to your team.