deployment workflow explained

Author:toolflowguide Date:2026-02-08 Views:120 Comments:0

Deployment Workflow Explained

A deployment workflow is the structured process of releasing software updates from development to production environments. Here's a breakdown of a modern deployment workflow:

Core Components

1. Code Repository (Git, SVN)

   • Version-controlled source code
   • Main branches: main/master, develop, feature branches

2. CI/CD Pipeline (Continuous Integration/Continuous Deployment)

   Automated process triggered by code changes

Typical Deployment Stages

Development Phase

Code → Commit → Pull Request → Code Review → Merge to Main Branch

• Developers work on feature branches
• Automated linting and basic checks
• Peer code reviews required

Build Phase (Continuous Integration)

Source Code → Dependency Installation → Compilation → Build Artifacts

• Creates executable application package
• Runs in isolated environment
• Generates versioned artifacts (Docker images, binaries, packages)

Testing Phase

graph LR
    A[Unit Tests] --> B[Integration Tests]
    B --> C[E2E Tests]
    C --> D[Security Scans]
    D --> E[Performance Tests]

• Automated test suites run
• Quality gates determine if deployment continues
• Security vulnerability scanning (SAST/DAST)

Staging/Pre-Production

• Mirrors production environment
• Final validation with:
  • Integration testing
  • Performance testing
  • User acceptance testing (UAT)
  • Smoke tests

Deployment to Production

Common Strategies:

• Blue-Green: Two identical environments, switch traffic instantly
• Canary: Gradual rollout to small user percentage
• Rolling Update: Incrementally replace instances
• Feature Flags: Deploy code but activate selectively

Post-Deployment

• Health checks and monitoring
• Rollback procedures if issues detected
• Logging and observability
• Performance metrics collection

Key Workflow Types

Workflow	Description	When to Use
Manual	Human intervention at each stage	Legacy systems, critical deployments
Automated	Fully automated pipeline	Frequent releases, microservices
GitOps	Git as single source of truth	Kubernetes environments
Trunk-based	Short-lived branches, frequent merges	Continuous delivery

Essential Tools in Stack

• Version Control: Git, GitHub, GitLab, Bitbucket
• CI/CD Servers: Jenkins, GitLab CI, GitHub Actions, CircleCI
• Containerization: Docker, Kubernetes
• Configuration Management: Ansible, Terraform, Chef
• Monitoring: Prometheus, Grafana, New Relic, Datadog
• Artifact Repositories: Nexus, Artifactory, Docker Registry

Best Practices

1. Infrastructure as Code (IaC): Version control infrastructure
2. Immutable Infrastructure: Replace rather than modify
3. Everything Automated: Minimize manual steps
4. Small, Frequent Releases: Reduce risk
5. Comprehensive Monitoring: Metrics, logs, tracing
6. Rollback Capability: Quick recovery from failures
7. Environment Parity: Keep environments similar

Example GitFlow Workflow

feature/ → develop → release/ → main → production
          (CI tests)   (QA)     (staging)

Modern Trends

• GitOps: Declarative infrastructure via Git
• Serverless Deployments: Function-as-a-Service platforms
• Progressive Delivery: Canary, feature flags, A/B testing
• ChatOps: Deploy via chat commands (Slack, MS Teams)

The goal is to create a reliable, repeatable process that minimizes downtime and risk while enabling rapid, frequent releases.

Permalink: https://toolflowguide.com/deployment-workflow-explained.html

Source:toolflowguide

Copyright:Unless otherwise noted, all content is original. Please include a link back when reposting.