How Comcast scaled analytics 80x while cutting costs 38% with GraphQL and Amazon S3 Tables

This article describes how Comcast's Reliability Analytics and Data Science team redesigned their analytics infrastructure using GraphQL and Amazon S3 Tables to handle massive scale improvements.

• Replaced REST endpoints with unified GraphQL API, reducing average fields per query from 15 to 5
• Adopted Amazon S3 Tables with Apache Iceberg for ACID properties and automated optimization
• Implemented device-identifier partitioning and data sorting to reduce scanned data by 35-40%
• Achieved 80x higher concurrent request volumes (32,000 requests per 5-minute window)
• Reduced query latency by 28% (820ms to 590ms median response time)
• Cut infrastructure costs by 38% through eliminating duplicate data copies
• Accelerated feature development: adding new fields now takes 2 days instead of 2 weeks
• Unified data access across real-time GraphQL queries and batch analytics via Athena and EMR

The architecture decouples the API layer from storage, enabling clients to request exactly what they need while pushing filtering and optimization into S3 Tables, freeing engineers to focus on features instead of infrastructure maintenance.