About Customer:
Customer is a India-based hands-on learning and skill-verification platform for electronics engineering students and professionals – offering HDL coding environments, structured problem sets and real-world debugging challenges that connect theory-heavy education with industry-ready design skills.
Industry:
EdTech/Electronics Skill Platform
Offering:
Cloud Migration
Business Challenges:
- Constrained Hosting – Firebase/Cloudflare couldn’t support validator nodes, RPC endpoints & multi-API workloads
- Manual, Inconsistent Releases – No automated CI/CD across microservices; deployments were slow and error-prone
- Fragile Data Layer – No durable, low-latency database/caching layer for growing storage-intensive workflows
- Limited Security & Visibility – No layered security, threat detection, or audit trail built in from day one
- Unpredictable Scaling – Traffic growth needed manual intervention; no auto-scaling for peak learner usage
Solution Implemented:
- Multi-AZ Amazon EKS — 5 managed node groups (VLSI/MATLAB/Embedded/API/Frontend) with Cluster & Pod Autoscaling
- Automated CI/CD — 6 GitHub-integrated pipelines via CodeBuild/CodeDeploy with Blue/Green & auto-rollback
- Managed Data & Caching — RDS PostgreSQL Multi-AZ (migrated via AWS DMS) + ElastiCache Redis + Amazon S3
- Layered Security Stack — IAM, Cognito, WAF, GuardDuty, Inspector, KMS, Secrets Manager + CloudTrail/Config audit
- Auto-Scaling Architecture — Cluster/HPA autoscaling, ALB traffic routing, and SQS/SNS event-driven messaging
Services Used:
- Amazon Bedrock
- Lambda
- OpenSearch
- ECS / ECR
- SQS
- DynamoDB
- API Gateway
Business Benefits:
- 99% Application Availability – Multi-AZ EKS keeps validator nodes and learning tools online through zone failures.
- 80% Less Manual Deployment – Automated CI/CD replaces manual scripts with self-healing, GitHub-triggered releases.
- 30% Infrastructure Cost Optimized – Right-sized, managed data services cut cloud spend without sacrificing performance.
- 90% Lower Unauthorized Access Risk – Network isolation and least-privilege IAM shrink the platform’s attack surface.
- 70% Scalability Improvement – Kubernetes autoscaling absorbs learner traffic spikes without manual intervention.
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Frequently Asked Questions
Firebase and Cloudflare couldn't support customer’ blockchain validator nodes, RPC endpoints, multiple concurrent APIs, and storage-intensive developer tooling - limiting the platform's ability to scale reliably or prepare for future AI/ML workloads.
A multi-AZ Amazon EKS cluster with five managed node groups (VLSI, MATLAB, Embedded, API, and Frontend workers), backed by RDS PostgreSQL Multi-AZ, ElastiCache Redis, and Amazon S3.
Through Blue/Green deployments with automatic rollback, Multi-AZ failover on RDS, and Cluster/Horizontal Pod Autoscaling — allowing traffic to shift safely without disrupting learners.
Rapyder built six GitHub-integrated pipelines using AWS CodeBuild and CodeDeploy, reducing manual deployment effort by 80% and cutting release cycles significantly.
A layered security stack - AWS IAM, Cognito, WAF, GuardDuty, Inspector, KMS, and Secrets Manager - combined with CloudTrail and AWS Config for continuous audit visibility, reducing unauthorized access risk by 90%.
