Automating Devops With Gitlab Ci/cd Pipelines Read Online [ Chrome ]

variables: DOCKER_REGISTRY: registry.gitlab.com APP_NAME: myapp job: script: - docker build -t $DOCKER_REGISTRY/$APP_NAME .

container_scan: stage: scan image: docker:latest script: - docker run --rm $IMAGE_TAG trivy image $IMAGE_TAG

test_job: stage: test script: - npm run test automating devops with gitlab ci/cd pipelines read online

include: - template: Security/SAST.gitlab-ci.yml GitLab automatically runs SAST scanners for your language (Python, Java, Go, etc.) and shows vulnerabilities in merge requests. include: - template: Security/Dependency-Scanning.gitlab-ci.yml Secret Detection Prevents accidental commits of passwords/keys:

DevOps emerged to bridge the gap between development and operations. Its core philosophy is — automating builds, tests, deployments, and monitoring. Among the many CI/CD tools available today (Jenkins, CircleCI, GitHub Actions), GitLab CI/CD has emerged as a powerful, integrated, and scalable solution. Why? Because GitLab isn't just a Git repository manager. It's a complete DevOps platform with CI/CD built directly into the same application that hosts your code. variables: DOCKER_REGISTRY: registry

(set in GitLab UI → Settings → CI/CD → Variables) can be masked or protected for specific branches. 3.2 Conditional Logic with rules The only/except keywords are deprecated in favor of rules .

test: script: npm test artifacts: reports: junit: junit.xml paths: - coverage/ expire_in: 1 week GitLab can even display test reports directly in merge requests! One of the most powerful automation patterns is building and pushing Docker images. Example: Build and Push to GitLab Container Registry stages: - build-image - scan - deploy variables: IMAGE_TAG: $CI_REGISTRY_IMAGE:$CI_COMMIT_SHORT_SHA Its core philosophy is — automating builds, tests,

Introduction: The Shift from Manual to Automated DevOps In the early days of software development, deploying code was a manual, nerve-wracking affair. System administrators would SSH into servers, pull code from repositories, run build commands, and restart services. This process was error-prone, slow, and unscalable.