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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Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
