Comcast Scales Analytics 80x with AWS S3 Tables
Comcast reduced data access costs by 38% and cut latency by 28% by replacing REST APIs with a GraphQL and Amazon S3 Tables architecture.
Key points
- Comcast scaled its analytics platform to handle 80x higher concurrent request volumes.
- The new architecture reduced overall data access costs by 38 percent.
- Query latency was cut by 28 percent, improving real-time data delivery speeds.
Comcast has significantly optimized its internal data infrastructure by replacing a traditional REST API setup with a new architecture built on GraphQL and Amazon S3 Tables. This shift allows the company to handle eighty times more concurrent requests while simultaneously reducing operational costs by thirty-eight percent.
The Reliability Analytics and Data Science team at Comcast previously struggled with a system that required two weeks to add a single new data field. The new design addresses this bottleneck by providing a unified schema, which drastically shortens development cycles and improves query speed for teams using real-time analytics.
Legacy API limitations drove the redesign
The original system served ten internal clients consuming over 350 terabytes of data. Each endpoint returned a fixed set of fifteen fields, even when users only needed five or seven. This forced clients to download unnecessary data, increasing network transfer costs and slowing down client-side processing.
As traffic grew to peak levels of 32,000 requests per five-minute window, the architecture began to degrade. Engineers spent significant time manually tuning separate database copies for each use case, diverting resources away from building new features and toward maintaining a fragile infrastructure.
GraphQL enables precise data retrieval
By adopting GraphQL, Comcast allows clients to request only the specific fields they require. This eliminates the waste associated with over-fetching data and reduces the payload size sent over the network. The result is a more efficient use of compute resources and faster response times for end users.
The new setup uses Amazon API Gateway and AWS Lambda to manage requests dynamically. This flexibility means that adding new fields to the data schema no longer requires creating entirely new API variants, which previously took two weeks to deploy across multiple systems.
S3 Tables simplify data storage
Amazon S3 Tables provides a simplified way to store and access large datasets without the overhead of traditional relational databases. This change supports the simplified extract, transform, and load pipeline, reducing the operational burden on the engineering team and allowing them to focus on innovation.
The combination of these technologies has cut query latency by twenty-eight percent, meeting the strict performance targets of keeping p95 latency below 800 milliseconds. For Comcast, this represents a move from a rigid, costly system to a scalable platform that supports their goal of delivering a more reliable broadband experience.
