Running microservices on Amazon EC2 instances with Docker Swarm restricted the client's ability to scale during peak travel periods.
The application was experiencing variability in performance, which impacted the user experience, resulting in a lack of confidence in the platform.
The reliance on manual processes for deployments led to delays, increasing the time-to-market for new features and updates.
High latency levels were affecting the platform's overall responsiveness, causing delays in load times.
Inefficient resource allocation within the existing architecture resulted in higher operational costs, as resources were often underutilized or over-provisioned.
The existing database solution struggled to provide reliability and scalability, particularly during sudden traffic surges.
Managed and provisioned AWS VPC and EKS using Terraform to enable a scalable infrastructure that ensures smooth performance during peak travel times.
Designed a custom monitoring solution with Prometheus, Alertmanager, and Grafana for the EKS cluster to proactively address potential performance issues.
Leveraged Jenkins for automating deployments in EKS and utilized helm charts for consistent deployments and configuration management.
Implemented AWS Elastic Load Balancer (ELB) to ensure high availability of microservices, minimizing latency and improving load times.
Installed Karpenter as an autoscaling solution for Kubernetes to optimize resource utilization, thus reducing overall platform operational costs.
Implemented AWS RDS for a scalable managed database solution to ensure consistent performance during high-demand periods.
Achieved complete platform modernization in under 6 weeks.
Ensured consistent performance during traffic surges.
Improved time to market by 10x.
Reduced overall operational costs by 50%.
Established centralized visibility with a custom monitoring solution.
Enhanced security, compliance, and observability across the platform.
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