What stages of data processing or pipeline utilities have you had to implement or maintain in your practice?

In DevOps practice, a pipeline is a sequence of stages for automating data processing or application deployment. The following stages are often implemented:

• Data or source code collection: obtaining artifacts from repositories or sources.
• Validation and testing: quality checks, running unit and integration tests.
• Build and packaging: compilation, creating containers or archives.
• Deployment: automatic deployment to test or production servers.
• Monitoring and notifications: tracking status and alerting the team.

Commonly used utilities include:

• Jenkins, GitLab CI, GitHub Actions for automation.
• Docker and Kubernetes for containerization and orchestration.
• Ansible, Terraform for infrastructure as code.

An example of a simple pipeline in Jenkinsfile:

pipeline {
    agent any
    stages {
        stage('Build') {
            steps {
                sh 'make build'
            }
        }
        stage('Test') {
            steps {
                sh 'make test'
            }
        }
        stage('Deploy') {
            steps {
                sh 'make deploy'
            }
        }
    }
}

This pipeline ensures automatic processing and delivery of the application.