Squarepanda got Significant Reduction in Time-to-Market With AWS Cloud

Headquartered in the US, with offices in Mumbai and Beijing, SquarePanda was founded by Andy Butler, the CEO, who initially started this to help his dyslexic daughter to learn. SquarePanda is an award-winning, edtech literacy platform and research-based phonics learning system for pre-readers and early readers worldwide.

Equipped with this AI-based platform that assesses and engages learners, SquarePanda aims to empower all children to reach their full potential. Square Panda’s adaptive platform helps children globally, across skill sets and socio-economic backgrounds, to equip themselves with reading, writing, and communicative skills. The learning experience is joyful and based on innovative teaching practices.

The applications were already hosted on AWS in the US. However, with the new setup in India, SquarePanda wanted to expand its operations in India and China and implement the hosting of applications on AWS in these new geographical locations. The client wanted to address the business need for a faster go-to-market facility and deliver an enhanced customer experience that would be brought about with this implementation.

It was looking for a partner who could provide them with an AWS solution for their application and ensure scalability, high availability, and security. Rapyder Cloud Solutions, an Advanced AWS Consulting partner with extensive experience and competency in successful cloud migration and implementation services, was chosen and partnered with.

This led to the Customer looking for a solution that would improve its delivery and support of IT while lowering the total cost of ownership.

To effectively address the business need of the Client, Rapyder’s team of Cloud Architects thoroughly studied the former’s requirement for implementation. A customized solution around AWS services was implemented while applying Cloud Deployment Architecture best practices and business continuity principles.
Rapyder implemented the following proposed solution and architecture approach:

• In the new AWS infrastructure, all the servers were launched in private subnets for ease of administration and control, with most components deployed in multi-AZ configuration to avoid single-point failure.
• AWS-managed load balancer was used to balance the traffic across two availability zones. The balancer was configured to route all site traffic, including dynamic pages.
• AWS-managed NAT Gateway was used for any outbound traffic from servers in private subnets.
• Monitoring and alerts were set up on each server to monitor metrics like CPU, memory, and disk.
• Cloudtrail was enabled for all regions. CI/CD pipelines were set up for each application component, where AWS CodePipeline and CodeDeploy were used.