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Deployment Pipelines

Simple Explanation​

What it is​

A deployment pipeline is an automated set of steps that tests, packages, and releases your code every time you make a change.

Why we need it​

Manual deployments are slow and risky. Pipelines make releases predictable and repeatable so teams can ship frequently with confidence.

Benefits​

  • Fast feedback from automated tests.
  • Consistent releases across environments.
  • Clear audit trail of what changed and when.
  • Safer rollbacks when something goes wrong.

Tradeoffs​

  • Setup time to build the pipeline.
  • Discipline required around tests and environment parity.
  • Credential management must be done securely.

Real-world examples (architecture only)​

  • Git push -> CI tests -> deploy to dev -> promote to prod.
  • IaC change -> plan -> approval -> apply -> smoke tests.

What Is a Pipeline?​

Automated workflow for testing and deploying code:

Developer pushes code -> GitHub
                |
                v
         Tests run automatically
                |
                v
         Deploy to dev
                |
                v
         Deploy to staging
                |
                v
         Approval -> Deploy to production

Core Stages​

  1. Source: Code change triggers the workflow
  2. Test: Unit and integration tests
  3. Build: Package artifacts
  4. Deploy: Release to an environment
  5. Verify: Health checks and metrics

GitHub Actions Example (Python + AWS SAM)​

name: Deploy Serverless App

on:
  push:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - name: Install dependencies
        run: pip install -r requirements.txt
      - name: Run tests
        run: pytest

  deploy:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - name: Configure AWS credentials (OIDC)
        uses: aws-actions/configure-aws-credentials@v4
        with:
          role-to-assume: arn:aws:iam::ACCOUNT_ID:role/GitHubActionsRole
          aws-region: AWS_REGION
      - name: Build
        run: sam build
      - name: Deploy
        run: sam deploy --no-confirm-changeset --stack-name maarifa-dev

If you deploy to GCP instead, replace the deploy step with gcloud or your preferred tooling.

Multi-Stage Deployments​

Promote the same artifact across environments (dev -> staging -> prod) with approvals before production.

Testing in the Pipeline​

Unit test example (Python)

from handler import handler


def test_handler_ok():
    result = handler({"name": "test"}, None)
    assert result["statusCode"] == 200

Deployment Strategies​

Blue/Green: Run two environments, switch traffic after validation.

Canary: Send a small percentage of traffic to the new version, then increase if healthy.

Monitoring and Rollback​

  • Add health checks and error rate alerts
  • Roll back quickly to the previous version if errors spike

Best Practices​

  • Keep environments identical
  • Use OIDC or short-lived credentials
  • Automate rollbacks
  • Monitor after every deploy
  • Document changes

Project​

Create a pipeline that:

  • Runs Python unit tests
  • Deploys to a dev environment
  • Requires manual approval for production
  • Documents the release steps

Email your work to [email protected].

References​