Optimizing AWS Infrastructure with DevOps Best Practices for Careers360

Industry

EdTech

Expertise

Amazon EKS, Amazon VPC, Amazon ECR, AWS ELB, Amazon S3, Amazon CloudWatch, Amazon CloudFront, Amazon Route53, AWS IAM, Amazon OpenSearch, AWS Lambda, Amazon SSM, Amazon API Gateway, and AWS WAF

Offerings/solutions

Enhanced app scalability via 60+ microservices on Amazon EC2, delivering 13,000 requests/sec support. Leveraged Amazon EKS for a 40% cost cut, optimizing AWS resources for 30% savings.

About the Client

Careers360 is India’s all-in-one education and career hub, offering content spanning Engineering, Law, University, Management, and Study Abroad. It uses predictive tools to provide personalized guidance based on students’ interests, assisting with exam prep and college selection.

Highlights

13,000 requests per second

Optimized Performance

60+

Microservices Transformation

30%

Cost Reduction

The Challenge

Careers360 faced challenges in their existing monolithic application hosted on Amazon EC2 instances. As the number of users increased, application scalability became a major concern. The monolithic applications incurred high costs of 30,000 USD MRR. No monitoring tools were in place, and there was a lack of tracking and monitoring, resulting in limited visibility into the infrastructure.

"Highly professional and qualified team. Excellent service by the team for our Cloud requirements."

Solutions

Utilized eksctl to launch clusters for different environments (dev, stage, prod) by applying YAML config files defining cluster settings and node groups. The 60+ app stack mainly employed the Python Django framework.
Achieved resource allocation for multitenant workloads through Loft, creating virtual clusters and user groups with varied permissions for effective cluster and resource management.
Implemented RBAC for access separation among developers, testers, etc., with defined roles, cluster roles, and permissions, ensuring proper access control.
Conducted secure multi-tenancy benchmarks using Aqua Kube-Bench to identify misconfigurations and security issues, receiving recommendations and compliance reports.
Optimized resource usage with node affinity scheduling, placing pods based on labels or availability zones to enhance performance and reduce conflicts.
Employed Amazon CloudWatch agent for cluster-level insights, Metric Server for autoscaling, and CloudWatch Alerts with SNS integration for monitoring and alerting.
Utilized Prometheus with Grafana for node and pod metrics, providing application and infrastructure health insights.
Integrated runtime security tool (snyk) into CI/CD pipeline for real-time container behavior monitoring.
Leveraged Amazon ECR as a secure image registry with scan on the push for vulnerability detection and mitigation.
Implemented CI/CD via Devtron for automated workload updates.
AWS services used: Amazon EKS, Amazon VPC, Amazon ECR, AWS ELB, Amazon S3, Amazon CloudWatch, Amazon CloudFront, Amazon Route53, AWS IAM, Amazon OpenSearch, AWS Lambda, Amazon SSM, Amazon API Gateway, AWS WAF.
Third-party tools: Prometheus, Grafana, Devtron, Helm.

The Results

Strategic application containerization and resource optimization on AWS, the company achieved enhanced scalability, security, and cost-efficiency while delivering a high-performance application capable of handling substantial customer traffic.

Download the Case Study