In this live session, we explored how to build a fully automated end-to-end CI/CD pipeline using the Argo suite—ArgoCD, Argo Rollouts, and Argo Workflows. This is part of the Open Courseware Project by the School of DevOps, and we're excited to share this knowledge with you.
If you've ever wondered how to implement progressive delivery strategies like blue-green deployments and canary releases, this article along with the live session recording above will guide you through the practical step.
Overview of the Pipeline
The pipeline we built in this session follows a structured approach to deploying a Python application with:
Continuous Integration (CI) to build and test the container image.
Continuous Delivery (CD) to deploy updates progressively.
GitOps-driven automation using ArgoCD for managing deployments.
Here’s a high-level view of how it works:
Source Code Push: Developers push code to GitHub.
CI Process: The pipeline builds and tests the container image.
Image Deployment: The container is pushed to a registry.
ArgoCD Synchronization: Detects changes in the Git repo and deploys updates.
Progressive Deployment Strategies:
Blue-Green Deployment for staging.
Canary Deployment for production with traffic shifting.
Understanding GitOps with ArgoCD
ArgoCD enables GitOps workflows, where your infrastructure and application configurations are stored as code in Git and automatically applied to Kubernetes clusters. The four core principles of GitOps are:
Declarative Configuration: Everything is written as code.
Version Control: The desired state is stored in Git.
Automated Application: Approved changes are applied automatically.
Continuous Reconciliation: An agent ensures the cluster matches the declared state.
In our demo, we used ArgoCD to continuously synchronize the Kubernetes manifests, ensuring that any Git change results in an automated deployment.
Blue-Green Deployment with Argo Rollouts
For staging deployments, we used blue-green deployment, a release strategy that minimizes downtime by maintaining two versions of the application:
The current (blue) version is active.
A new (green) version is deployed in parallel.
Traffic is switched to the new version after testing.
The old version is retired after a cooldown period.
This ensures zero downtime deployment while providing a rollback option if issues arise.
Canary Deployment for Production
For production, we implemented a Canary Deployment using Argo Rollouts. The canary approach allows us to gradually shift traffic from the existing version to a new one in phases:
Start with 10% of traffic on the new version.
Monitor system health metrics like latency and error rates.
Increase traffic in increments (e.g., 20%, 40%, 60%, 100%).
If issues are detected, rollback automatically.
This approach helps mitigate risks associated with deploying changes to production while ensuring controlled exposure to a small subset of users first.
Using Argo Workflows for CI
Argo Workflows was used to automate the CI pipeline, handling:
Container image builds
Automated tests
Artifact packaging
The pipeline was structured as a Directed Acyclic Graph (DAG), allowing for task dependencies and parallel execution where needed.
The Power of GitOps in Production
With ArgoCD, Argo Rollouts, and Argo Workflows, we demonstrated how modern DevOps teams can automate software delivery at scale while ensuring stability and reliability. This approach aligns perfectly with the Platform Engineering mindset, allowing developers to focus on coding while infrastructure automation handles deployments.
Take Your GitOps Skills to the Next Level
🚀 If you found this session insightful and want hands-on guidance in setting up similar pipelines, enroll in our Ultimate Argo Bootcamp!
✅ Learn to build an end-to-end ArgoCD pipeline from scratch
✅ Master progressive delivery techniques with Argo Rollouts
✅ Get step-by-step lab guides and real-world use cases
🔥 Join the Ultimate Argo Bootcamp today! 👉 Enroll Now
Stay tuned for more open courseware and deep-dive workshops from the School of DevOps!
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