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Enterprise Kubernetes Platform

Project Overview

Built a comprehensive self-service Kubernetes platform that transformed how 200+ development teams deploy and manage applications at Commonwealth Bank of Australia. The platform reduced deployment time from weeks to minutes while maintaining enterprise-grade security and compliance standards.

Key Achievements

  • Scale: Supporting 200+ development teams across multiple business units
  • Performance: Reduced application deployment time from 2-3 weeks to under 10 minutes
  • Cost Optimization: Achieved 35% reduction in infrastructure costs through automated resource optimization
  • Reliability: Maintained 99.9% platform uptime with automated failover and disaster recovery

Technical Architecture

Core Components

  • Kubernetes Clusters: Multi-region EKS clusters with automated scaling
  • GitOps Deployment: ArgoCD-based continuous deployment pipeline
  • Service Mesh: Istio for traffic management and security
  • Monitoring Stack: Prometheus, Grafana, and custom alerting
  • Security: Pod Security Standards, Network Policies, and RBAC

Infrastructure as Code

# Example Terraform configuration for EKS cluster
module "eks_cluster" {
  source = "./modules/eks"

  cluster_name    = "platform-${var.environment}"
  cluster_version = "1.28"

  node_groups = {
    general = {
      instance_types = ["m5.large", "m5.xlarge"]
      scaling_config = {
        desired_size = 3
        max_size     = 10
        min_size     = 1
      }
    }
  }

  addons = [
    "vpc-cni",
    "coredns",
    "kube-proxy",
    "aws-load-balancer-controller"
  ]
}

Self-Service Portal

Developed a web-based portal that allows development teams to:

  • Provision Namespaces: Automated namespace creation with proper RBAC
  • Deploy Applications: GitOps-based deployment workflows
  • Monitor Resources: Real-time metrics and logging access
  • Manage Secrets: Integrated secret management with AWS Secrets Manager
  • Cost Tracking: Per-team cost allocation and optimization recommendations

Automation & DevOps

Deployment Pipeline

  1. Code Commit: Developers push to Git repository
  2. CI Pipeline: Automated testing and container image building
  3. Security Scanning: Container vulnerability and compliance checks
  4. GitOps Sync: ArgoCD deploys to appropriate environments
  5. Monitoring: Automated health checks and alerting

Cost Optimization

  • Cluster Autoscaler: Automatic node scaling based on demand
  • Vertical Pod Autoscaler: Right-sizing pod resource requests
  • Spot Instance Integration: 60% cost reduction for non-critical workloads
  • Resource Quotas: Preventing resource waste through governance

Security & Compliance

Security Features

  • Pod Security Standards: Enforced security policies across all workloads
  • Network Segmentation: Calico network policies for micro-segmentation
  • Image Security: Automated vulnerability scanning and policy enforcement
  • Secrets Management: Integration with AWS Secrets Manager and HashiCorp Vault

Compliance

  • Audit Logging: Comprehensive audit trails for all platform activities
  • Policy Enforcement: Open Policy Agent (OPA) for governance
  • Backup & Recovery: Automated backup strategies with point-in-time recovery
  • Disaster Recovery: Multi-region failover capabilities

Monitoring & Observability

Metrics & Alerting

  • Cluster Metrics: Node health, resource utilization, and performance
  • Application Metrics: Custom metrics collection and visualization
  • SLA Monitoring: Automated SLA tracking and reporting
  • Incident Response: Integration with PagerDuty for automated alerting

Dashboards

Created comprehensive Grafana dashboards for: - Platform health and performance - Per-team resource utilization - Cost analysis and optimization opportunities - Security and compliance metrics

Impact & Results

Business Impact

  • Developer Productivity: 10x faster deployment cycles
  • Cost Savings: $2.4M annual infrastructure cost reduction
  • Reliability: 99.9% platform availability
  • Security: Zero security incidents related to platform vulnerabilities

Technical Metrics

  • Deployment Frequency: From monthly to multiple times per day
  • Lead Time: Reduced from 2-3 weeks to under 10 minutes
  • Recovery Time: Mean time to recovery under 15 minutes
  • Resource Efficiency: 35% improvement in resource utilization

Lessons Learned

What Worked Well

  • GitOps Approach: Declarative configuration management simplified operations
  • Self-Service Model: Empowering teams reduced operational overhead
  • Automation First: Investing in automation paid dividends at scale
  • Observability: Comprehensive monitoring enabled proactive issue resolution

Challenges Overcome

  • Cultural Change: Extensive training and documentation for adoption
  • Legacy Integration: Gradual migration strategy for existing applications
  • Security Concerns: Collaborative approach with security teams for compliance
  • Scale Challenges: Iterative improvements to handle growing demand

Future Enhancements

  • Multi-Cloud Support: Extending platform to Azure and GCP
  • AI/ML Workloads: Specialized support for machine learning pipelines
  • Edge Computing: Extending platform to edge locations
  • Advanced Networking: Service mesh expansion and traffic optimization

Technologies Used

  • Container Orchestration: Kubernetes, Docker, Amazon EKS
  • Infrastructure: AWS, Terraform, CloudFormation
  • CI/CD: ArgoCD, GitHub Actions, Helm
  • Monitoring: Prometheus, Grafana, DataDog
  • Security: OPA, Falco, AWS Security Services
  • Programming: Go, Python, Bash scripting

This project demonstrates expertise in large-scale platform engineering, DevOps automation, and enterprise Kubernetes management.