Products
Building a high-performance platform on AWS to support real-time gaming services using Presto, Alluxio, and S3
December 13, 2020
Electronic Arts (EA) is a leading company in the gaming industry, providing over a thousand games to serve billions of users worldwide. The EA Data & AI Department builds hundreds of platforms to manage petabytes of data generated by games and users every day. These platforms consist of a wide range of data analytics, from real-time data ingestion to ETL pipelines. Formatted data produced by our department is widely adopted by executives, producers, product managers, game engineers, and designers for marketing and monetization, game design, customer engagement, player retention, and end-user experience.
Near real-time information for EA’s online services is critical for making business decisions, such as campaigns and troubleshooting. These services include, but are not limited to, real-time data visualization, dashboarding, and conversational analytics. Highly time-sensitive applications such as BI software, dashboards and AI tools heavily rely on these services. To support these use cases, we studied an innovative platform with Presto as the computing engine and Alluxio as a data orchestration layer between Presto and S3 storage. We evaluated this platform with real industrial examples of data visualization, dashboarding, and a conversational chatbot. Our preliminary results show that Presto with Alluxio outperforms S3 significantly in all cases, with a 6x performance gain when handling a large number of small files.
Electronic Arts (EA) is a leading company in the gaming industry, providing over a thousand games to serve billions of users worldwide. The EA Data & AI Department builds hundreds of platforms to manage petabytes of data generated by games and users every day. These platforms consist of a wide range of data analytics, from real-time data ingestion to ETL pipelines. Formatted data produced by our department is widely adopted by executives, producers, product managers, game engineers, and designers for marketing and monetization, game design, customer engagement, player retention, and end-user experience.
Near real-time information for EA’s online services is critical for making business decisions, such as campaigns and troubleshooting. These services include, but are not limited to, real-time data visualization, dashboarding, and conversational analytics. Highly time-sensitive applications such as BI software, dashboards and AI tools heavily rely on these services. To support these use cases, we studied an innovative platform with Presto as the computing engine and Alluxio as a data orchestration layer between Presto and S3 storage. We evaluated this platform with real industrial examples of data visualization, dashboarding, and a conversational chatbot. Our preliminary results show that Presto with Alluxio outperforms S3 significantly in all cases, with a 6x performance gain when handling a large number of small files.
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Videos
Bridging Speed and Scale: AWS S3 Data Caching for Low-Latency, Semantically-Rich AI Workloads

Amazon S3 and other cloud object stores have become the de facto storage system for organizations large and small. And it’s no wonder why. Cloud object stores deliver unprecedented flexibility with unlimited capacity that scales on demand and ensures data durability out-of-the-box at unbeatable prices.
Yet as workloads shift toward real-time AI, inference, feature stores, and agentic memory systems, S3’s latency and limited semantics begin to show their limits. In this webinar, you’ll learn how to augment — rather than replace — S3 with a tiered architecture that restores sub-millisecond performance, richer semantics, and high throughput — all while preserving S3’s advantages of low-cost capacity, durability, and operational simplicity.
We’ll walk through:
- The key challenges posed by latency-sensitive, semantically rich workloads (e.g. feature stores, RAG pipelines, write-ahead logs)
- Why “just upgrading storage” isn’t sufficient — the bottlenecks in metadata, object access latency, and write semantics
- How Alluxio transparently layers on top of S3 to provide ultra-low latency caching, append semantics, and zero data migration with both FSx-style POSIX access and S3 API access
- Real-world results: achieving sub-ms TTFB, 90%+ GPU utilization in ML training, 80X faster feature store query response times, and dramatic cost savings from reduced S3 operations
- Trade-offs, deployment patterns, and best practices for integrating this tiered approach in your AI/analytics stack
October 28, 2025
AI/ML Infra Meetup | AI at scale Architecting Scalable, Deployable and Resilient Infrastructure

Pratik Mishra delivered insights on architecting scalable, deployable, and resilient AI infrastructure at scale. His discussion on fault tolerance, checkpoint optimization, and the democratization of AI compute through AMD's open ecosystem resonated strongly with the challenges teams face in production ML deployments.
September 30, 2025
AI/ML Infra Meetup | Alluxio + S3 A Tiered Architecture for Latency-Critical, Semantically-Rich Workloads

In this talk, Bin Fan, VP of Technology at Alluxio, presents on building tiered architectures that bring sub-millisecond latency to S3-based workloads. The comparison showing Alluxio's 45x performance improvement over S3 Standard and 5x over S3 Express One Zone demonstrated the critical role the performance & caching layer plays in modern AI infrastructure.
September 30, 2025