Smart Building Management Platform - CI/CD Modernization and Release Control

Business Problem

Release activities depended heavily on manual coordination and undocumented pipeline behavior. The team needed clearer build visibility, repeatable artifact handling, safer image promotion, and a migration path toward AWS-native CI/CD controls.

Architecture

flowchart LR
  Repo[Git Repository] --> Trigger[Pull Request or Release Tag]
  Trigger --> Jenkins[Jenkins Pipeline]
  Jenkins --> Test[Build and Test]
  Test --> Image[Docker Build and Tag]
  Image --> ECR[Amazon ECR]
  Jenkins --> Artifacts[S3 Build Artifacts]
  ECR --> Deploy[Deployment Stage]
  Deploy --> EKS[EKS or ECS Runtime]
  Deploy --> CW[CloudWatch Logs]
  IAM[IAM Roles] --> Jenkins
  IAM --> ECR
  IAM --> Artifacts
  Approval[Release Approval] --> Deploy
  Rollback[Rollback Runbook] --> Deploy

My Responsibilities

• Mapped existing Jenkins release behavior and documented ownership boundaries.
• Designed CodePipeline and CodeBuild-oriented stages for future AWS-native automation.
• Documented Docker image tagging, ECR publishing, artifact handling, IAM roles, and CloudWatch log review.
• Prepared deployment validation and rollback notes for release support.

Implementation

• Separated build, test, image publish, and deploy concerns into reviewable stages.
• Defined release metadata conventions so deployments could be traced to a commit and image tag.
• Captured workflow output and operational notes for troubleshooting failed deployments.
• Aligned IAM permissions with least-privilege pipeline responsibilities.

Challenges

• Existing release knowledge was distributed across people and pipelines.
• Build logs and deployment evidence needed to be easier to review.
• Pipeline design had to support both current Jenkins use and future AWS-native services.

Solution

The solution combined automation, clear infrastructure boundaries, operational validation, and documentation-first handover. The design focuses on traceable releases, least-privilege access, predictable runtime behavior, and observable production systems.

Code Snippet

Representative public-safe snippet showing the type of implementation pattern used.

pipeline {
  agent any
  stages {
    stage('Build') { steps { sh 'docker build -t app:${BUILD_NUMBER} .' } }
    stage('Publish') { steps { sh 'docker push ${ECR_REPOSITORY}:app-${BUILD_NUMBER}' } }
    stage('Deploy') { steps { sh 'kubectl rollout status deployment/application-service' } }
  }
  post {
    failure { sh './scripts/collect-deployment-diagnostics.sh' }
  }
}

Deployment Flow

1. 1

   Pipeline discovery

2. 2

   Build and artifact mapping

3. 3

   IAM and logging design

4. 4

   Release validation

5. 5

   Handover documentation

Validation

• Verified deployment health before routing user traffic.
• Reviewed logs, metrics, events, and runtime configuration.
• Documented rollback and post-deployment checks for support teams.

Monitoring and Security

• Monitoring focused on logs, runtime status, health checks, alarms, and deployment evidence.
• Security guidance covered IAM roles, security groups, secrets handling, and private network boundaries where applicable.
• Operational notes included backup, restore, rollback, and incident response expectations.

Outcome and Impact

• Created a clear migration path from Jenkins-heavy release workflows to structured AWS CI/CD.
• Improved deployment traceability through image tagging and artifact documentation.
• Reduced release dependency on undocumented manual steps.

Lessons Learned

• Reliable DevOps delivery depends on validation, not only deployment automation.
• Runbooks and handover notes reduce operational risk during production support.
• Health checks, logs, access boundaries, and rollback steps should be designed before go-live.

Related Incident Experience

• Kubernetes deployment issue
• Storage lifecycle recovery
• Service startup failure
• Pipeline rollback

Pipeline Screenshots Placeholder