How GitHub Actions and GitLab CI/CD accelerate software delivery

In today's software development, speed and reliability aren't optional. Companies need to constantly release updates, fix bugs quickly and ensure that everything works properly. Traditionally, this process involved many manual tasks prone to human error.

Nowadays, tools such as GitHub Actions and GitLab CI/CD they have standardized a methodology called CI/CD (Continuous Integration and Continuous Delivery).

This blog explains what these tools are, how they actually work, and why they are essential for an agile methodology.

What is a CI/CD Pipeline?

Un Pipeline is an automated structure that moves code from the developer's machine to the production environment. This process eliminates human intervention in repetitive tasks, ensuring that every change to the software is:

1. Verified: Automatic tests are run.
2. Packaging: The application is compiled.
3. Deployed: It is sent to the final servers.

To understand the tools, we must first define the process they automate. CI/CD stands for two concepts that occur one after the other:

1. Continuous Integration (CI): It's the practice where developers save and combine their code into a central repository several times a day. Every time the code is saved, an automatic system runs tests to verify that the new changes do not generate conflicts or errors in what was already done.
2. Continuous Delivery (CD): It's the next step. Once the code has passed the integration tests, the system automatically prepares it to be released to production (the environment that users see). The goal is that the software is always ready to be deployed at any time.

The role of GitHub Actions and GitLab CI/CD

Both GitHub and GitLab are platforms where source code is stored and managed. GitHub Actions and GitLab CI/CD are the automation engines integrated into these platforms.

Its function is simple: execute a list of predefined tasks each time a specific event occurs. For example, when someone saves a file, these tools read instructions (written in configuration files) and execute step by step what the engineering team has defined.

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How does the step-by-step workflow work?

Instead of relying on a person remembering to execute commands, the flow looks like this:

1. The Trigger It all starts when a developer completes a task and submits their code to the cloud repository (GitHub or GitLab).

2. Execution of Automatic Tests (Testing) Immediately, the tool (GitHub Actions or GitLab CI/CD) starts a temporary server and downloads the project. There it runs a series of automated tests:

• Verify that the logic of the code is correct.
• Check basic safety standards.
• It ensures that the new functions have not broken old functions.

If any test fails, the process stops and notifies the computer exactly where the error is.

3. Construction (Build) If the tests pass, the tool “builds” the application. This means that it takes all the code files and packages them in a final executable format, ready to be installed or uploaded to a server.

4. Deploy Finally, the tool takes that verified package and sends it to the company's servers. In a matter of minutes, the update is available to end users without anyone having to touch the servers manually.

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1. Automation with GitHub Actions

GitHub uses an event-based approach. Its workflows are defined in .yaml files and allow for great modularity.

Example of implementation: The following snippet shows a configuration for an application that must be tested every time code is uploaded.

 

# Archivo: .github/workflows/main.yml

name: Producción CI/CD
on:
  push:
    branches: [ "main" ] # Disparador: Solo se activa en la rama principal

jobs:
  build-and-test:
    runs-on: ubuntu-latest # Entorno de ejecución virtualizado
    
    steps:
    - name: Obtener código
      uses: actions/checkout@v3
      
    - name: Configurar entorno (Node.js)
      uses: actions/setup-node@v3
      with:
        node-version: 18
        
    - name: Ejecutar Test Unitarios
      run: npm test # Validación de calidad
      
    - name: Desplegar a Servidor
      if: success() # Condicional: Solo ejecuta si los tests pasaron
      run: ./scripts/deploy.sh

Flow analysis:

• Trigger (on): The system monitors the main branch. Any changes there start the process.
• Jobs: Define the tasks. In this case, the system provisions a Linux server (ubuntu-latest), downloads the code and runs the tests.
• Safety: The” if: success ()” statement is critical. It ensures that deployment never occurs if previous quality validation has failed.

Here is an example of a pipeline where the trigger was successfully activated when a change was made; the pipeline was executed and the Unit Test and Build are viewed as successful and deployed in Docker Hub.

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2. Automation with GitLab CI/CD

GitLab offers an integrated architecture based on “Stages”. It's ideal for organizations looking for a unified view of the entire DevOps cycle.

Example of implementation: In GitLab, the .gitlab-ci.yml file defines a strict execution sequence.

 
# Archivo: .gitlab-ci.yml

stages:          
  - build
  - test
  - deploy

compilacion:
  stage: build
  script:
    - echo "Iniciando compilación de binarios..."
    - npm install
    - npm run build

validacion:
  stage: test
  script:
    - npm run test-security
    - npm run test-unit

produccion:
  stage: deploy
  script:
    - echo "Desplegando versión estable..."
    - ./deploy-to-cloud.sh
  only:
    - main # Restricción de entorno

Flow analysis:

• Sequential Structure: The pipeline progresses in stages. If the build stage fails, the process stops immediately, saving resources and preventing errors in later stages.
• Traceability: Each step (script) is recorded in the system logs, allowing a complete audit of what happened, when and who authorized the change.

Business Impact

The implementation of these tools transforms technical operations into tangible results for the company:

1. Reduced Time-to-Market: We went from weekly deployment cycles to daily or even hourly deployments.
2. Operational Stability: By automating testing, the rate of human error in production is dramatically reduced.
3. Cost Efficiency: Developers stop spending hours manually configuring servers and focus on developing valuable functionality.

Conclusion

Both GitHub Actions and GitLab CI/CD are robust tools that allow a agile and secure continuous delivery. The choice between one or the other will depend on the company's current infrastructure, but the objective is the same: to build a system where quality and speed are not exclusive, but complementary.

Automation isn't the future, it's the present standard for any high-performance engineering team.

Does your team need to deploy faster without sacrificing stability and security?
In Kranio we help companies design and implement CI/CD pipelines aligned to the business, reducing production errors and accelerating time-to-market.

👉 Contact us and let's evaluate together the best automation strategy for your operation.

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