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Ed-Tech
AWS Lambda, Amazon VPC, Amazon EKS, Amazon S3, Amazon Elastic Container Registry
Improved Operational Efficiency and Cost Reduction with gp3 Storage and Advanced Autoscaling
Rocket Learning is a pioneering non-profit organization catalyzing Early Childhood Development (ECD) across India. It incubates communities of practice for Anganwadi (publicly funded daycare centers) workers and parents, both in person and digitally, by leveraging accessible technology and engaging content. The organization also supports policy and structural reform. Rocket Learning’s solution is scalable, effective, and cost-efficient at just $1.50 per child per year.
Cost Reduction
Enhanced routing and system performance
Implemented Karpenter
The customer encountered challenges with underperforming gp2 storage volumes, inefficient traffic management with Classic Load Balancers (CLB), and suboptimal resource scaling with their Cluster Autoscaler. They sought improvements through transitioning to gp3 storage, adopting Application Load Balancers (ALB), and implementing Karpenter for dynamic resource scaling. These enhancements necessitated automation and optimization using DevOps practices and Infrastructure as Code (IAC) tools such as Terraform.
• Transitioned from gp2 to gp3 storage volumes to improve performance and reduce costs. This migration was managed using Terraform to ensure consistency and reliability across environments.
• Replaced Classic Load Balancers (CLB) with Application Load Balancers (ALB) to enhance traffic management, improve load balancing capabilities, and enable better routing features.
• Implemented Karpenter to replace the existing Cluster Autoscaler, providing more dynamic and efficient resource allocation, which resulted in optimized scaling and cost efficiency.
• Utilized Terraform for automating the deployment and management of AWS infrastructure. Modular Terraform code simplified resource provisioning and minimized deployment errors.
• Developed Continuous Integration/Continuous Deployment (CI/CD) pipelines using AWS CodeBuild and AWS CodePipeline to automate the deployment process. This enabled automated and seamless deployments for all applications.
• Set up Prometheus and Grafana for comprehensive monitoring of system performance and metrics. This enhanced the ability to proactively manage and optimize the infrastructure.
• Integrated Amazon GuardDuty for continuous security monitoring and threat detection. Employed AWS CloudTrail for compliance, auditing, and detailed logging of all API activities within the AWS account.
• AWS Services Utilized: Amazon EKS, Amazon S3, AWS CodeBuild, AWS CodePipeline, Amazon VPC, Amazon RDS, Amazon ECR, Amazon ALB, Amazon GuardDuty, AWS CloudTrail, and Amazon Karpenter.
Enhanced performance and cost efficiency achieved through gp3 storage migration, ALB adoption for improved traffic management, Karpenter autoscaling for dynamic resource allocation, and EKS cluster deployment with managed nodes.
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