🛠️ Our DevOps Workflow: Cloud Build, Cloud Run & GitHub Actions for Fast & Secure Deployments

Shipping code is easy.

Shipping secure, tested, auto-deployed code -- on time, at scale, with rollback and monitoring -- is hard.

At Dezoko, we've built a powerful DevOps pipeline using:

• ✅Google Cloud Build
• ✅Cloud Run
• ✅GitHub Actions
• ✅Artifact Registry + Secrets Manager
• ✅Uptime monitoring + Slack alerts

This system powers dozens of our projects -- from startup MVPs to enterprise APIs -- and helps us ship faster, with fewer bugs, and with full confidence.

Here's exactly how it works.

🚀 The Tools We Use

🔄 Our DevOps Pipeline (Step-by-Step)

1. Developer pushes code → GitHub

• ✅Feature branches, PR-based workflow
• ✅Auto run of `unit tests + lint + Dockerfile checks`
• ✅On `main` or `prod` branch merge → triggers GitHub Action

2. GitHub Action triggers Cloud Build

# Example .github/workflows/deploy.yml
on:
  push:
    branches:
      - main

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Trigger Cloud Build
        run: |
          gcloud builds submit --config=cloudbuild.yaml --substitutions=_REGION=asia-south1

3. Cloud Build builds + deploys container

# Example cloudbuild.yaml
steps:
  - name: 'gcr.io/cloud-builders/docker'
    args: ['build', '-t', 'REGION-docker.pkg.dev/PROJECT/image:latest', '.']

  - name: 'gcr.io/cloud-builders/docker'
    args: ['push', 'REGION-docker.pkg.dev/PROJECT/image:latest']

  - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
    args:
      - run
      - deploy
      - service
      - my-service
      - --image=REGION-docker.pkg.dev/PROJECT/image:latest
      - --region=asia-south1
      - --platform=managed

☁️ Image gets pushed to Artifact Registry

🚀 Cloud Run updates without downtime

4. Slack + Email notifications (Optional)

• ✅Slack webhook integration
• ✅Notify on:

• Successful deploy
• Failure
• Rollbacks
• Test coverage drops

5. Post-Deploy Monitoring

• Cloud Monitoring: latency, error %, memory/CPU
• Cloud Logging: structured logs with `requestId`, `userId`
• Alerting policies: if error rate > 5%
• Uptime checks + response time graphs
• Optional: push alerts to Slack or Opsgenie

🧱 Real Use Case: B2B API SaaS

Stack:

• Nuxt frontend → Firebase Auth
• Cloud Run backend (Node.js)
• PostgreSQL (Cloud SQL)
• BigQuery + Firebase for analytics
• Stripe for billing
• Full CI/CD as described above

🚀 We ship to production multiple times daily, and maintain 99.99% uptime with full logging + observability.

💡 Why This Workflow Works for Us

• ✅Zero-downtime deploys
• ✅No DevOps engineer needed for small teams
• ✅Rollback via Git commit revert
• ✅Secure secrets handling
• ✅Full control with YAML and CLI
• ✅Scales with your app and team size

💬 What Clients Say

> "They built a fully automated pipeline for us -- now we can deploy anytime, from anywhere, with no risk."

> -- CTO, Fintech Platform

> "Our time-to-release dropped by 70% after they set up this pipeline."

> -- Product Lead, SaaS App

📞 Want to Automate Your Deployments?

We help you:

• ✅Set up end-to-end CI/CD with Cloud Run + GitHub
• ✅Migrate from legacy deployments
• ✅Integrate test + staging environments
• ✅Add monitoring, logging, alerts, and rollback flows