SCALABLE ARCHITECTURE FOR SHOPIFY WEBHOOKS AT HIGH VOLUME FROM DEDICATED SERVERS TO CLOUDFLARE WORKERS

Statement of the Problem: The exponential growth of e-commerce platforms like Shopify generates massive webhook traffic, with enterprise-scale applications handling millions of events daily from tens of thousands of stores.Traditional server-based architectures, utilizing Node.js,RabbitMQ, and Docker Swarm, face significant challenges including network saturation, manual scaling requirements, single points of failure, and high operational costs.These limitations hinder reliability and performance under high loads, such as during flash sales, necessitating innovative approaches to ensure robust, cost-effective webhook processing.This study aims to propose and validate a serverless architecture using Cloudflare Workers and Queues for processing high-volume Shopify webhooks, comparing its performance, reliability, and cost against traditional dedicated server infrastructure.The article provides practical implementation guidance to address scalability bottlenecks and reduce operational overhead in e-commerce integrations.The research introduces a production-proven serverless architecture leveraging Cloudflare's edge computing for Shopify webhook processing, a domain underexplored in existing literature.Unlike prior studies focusing on general cloudnative systems, this work offers specific insights into webhook handling at scale, demonstrating measurable advantages in latency, concurrency, and cost efficiency over traditional and major cloud-based solutions.The proposed Cloudflare Workers architecture achieves sub-50ms global latency and handles over 1,000 requests per second, with infinite concurrency and zero cold starts.It reduces monthly infrastructure costs by 93% (~$350 savings) compared to Vda a perspektivy 9(52) 2025