Manual deployments, inconsistent environments, and slow release cycles hold teams back. Every hour spent on manual processes is an hour not spent building features. The right CI/CD setup lets you deploy multiple times per day with confidence—catching issues before production and giving developers autonomy to ship their own code.
Key DevOps Areas
CI/CD Pipelines
Automated build, test, and deployment workflows using CodePipeline, GitHub Actions, or GitLab CI. Blue/green and canary deployments for zero-downtime releases.
Infrastructure as Code
Version-controlled infrastructure with CloudFormation, CDK, or Terraform. Eliminate environment drift and enable reproducible deployments across accounts.
Observability
CloudWatch dashboards, centralized logging, X-Ray distributed tracing, and automated alerting. Know what's happening in your systems before users do.
Security in Pipeline
Shift-left security with SAST, container scanning, IaC security checks, and secrets management. Catch vulnerabilities before they reach production.
Pipeline Patterns That Work
After building pipelines for dozens of teams, certain patterns consistently deliver results:
- Trunk-based development — Short-lived feature branches merged frequently to main. Long-lived branches create merge hell and deployment bottlenecks.
- Environment promotion — Same artifact deployed to dev → staging → production. Never rebuild between environments.
- Automated rollback — CloudWatch alarms trigger automatic rollback on error rate spikes. Don't wait for humans to notice problems at 2am.
- Infrastructure and application together — CDK or CloudFormation in the same repo as application code. Deploy infrastructure changes through the same pipeline.
- Feature flags over feature branches — Deploy incomplete features behind flags. Decouple deployment from release.
My Approach
I start with your current workflow and pain points—not a theoretical ideal state. The goal is incremental improvement: get one pipeline working well, then expand. Teams that try to boil the ocean end up with half-finished automation that nobody trusts.
For .NET and Node.js teams specifically, I focus on patterns that work: containerized builds for consistency, proper artifact management, and deployment strategies that match your risk tolerance. Lambda deployments need different patterns than ECS services.
The handoff matters as much as the implementation. I document everything, pair with your team during setup, and make sure you can maintain and extend the pipelines after I'm gone.
Advanced: Automated SDK Generation
For teams with public or internal APIs, I've built pipelines that automatically generate TypeScript and C# SDKs from OpenAPI specs at build time. Every API change triggers SDK regeneration, version bumping, and publishing to npm/NuGet—no manual steps required.
See the Automated SDK Generation Pipeline case study for the full architecture.
Frequently Asked Questions
Should I use AWS CodePipeline or GitHub Actions?
Both work well. CodePipeline integrates tightly with AWS services and keeps everything in your AWS account, which some compliance frameworks prefer. GitHub Actions has a larger ecosystem of pre-built actions and is often simpler for teams already using GitHub. For most teams, I recommend GitHub Actions for build/test and CodePipeline or CDK Pipelines for AWS deployments.
What's the difference between CloudFormation and CDK?
CloudFormation is AWS's native infrastructure-as-code service using YAML or JSON templates. CDK lets you define infrastructure using TypeScript, Python, C#, or other languages, then synthesizes to CloudFormation. CDK is more expressive for complex logic and reusable constructs, while CloudFormation templates are more portable. For .NET teams, CDK in C# often feels more natural.
How do I implement blue/green deployments on AWS?
For Lambda, use aliases with weighted traffic shifting—CodeDeploy can automate gradual rollouts with automatic rollback on errors. For ECS, use CodeDeploy with blue/green deployment configuration. For EC2/ASG, create a new Auto Scaling group, shift traffic at the load balancer, then terminate the old group. The key is having health checks that catch problems before full traffic shift.
How do I handle secrets in my CI/CD pipeline?
Never store secrets in your repository or pipeline configuration. Use AWS Secrets Manager or Parameter Store (SecureString) for runtime secrets, and reference them by ARN in your deployment. For build-time secrets, use your CI platform's secret management. Rotate secrets regularly and audit access via CloudTrail.
What should I monitor in my AWS environment?
Start with the basics: error rates, latency percentiles (p50, p95, p99), and resource utilization. For Lambda, track cold starts, duration, and concurrent executions. For APIs, monitor 4xx and 5xx rates by endpoint. Set up CloudWatch alarms for anomalies, not just thresholds. Use X-Ray for distributed tracing to find bottlenecks across services.