JD.com is one of the largest e-commerce corporations. In big data platform of JD.com, there are tens of thousands of nodes and tens of petabytes off-line data which require millions of spark and MapReduce jobs to process everyday. As the main query engine, thousands of machines work as Presto nodes and Presto plays an import role in the field of In-place analysis and BI tools. Meanwhile, Alluxio is deployed to improve the performance of Presto. The practice of Presto & Alluxio in JD.com benefits a lot of engineers and analysts.
JD.com is one of the largest e-commerce corporations. In big data platform of JD.com, there are tens of thousands of nodes and tens of petabytes off-line data which require millions of spark and MapReduce jobs to process everyday. As the main query engine, thousands of machines work as Presto nodes and Presto plays an import role in the field of In-place analysis and BI tools. Meanwhile, Alluxio is deployed to improve the performance of Presto. The practice of Presto & Alluxio in JD.com benefits a lot of engineers and analysts.
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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.
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.
In this talk, Greg Lindstrom shared how Blackout Power Trading achieved double-digit millisecond offline feature store performance using Alluxio, a game-changer for real-time power trading where every millisecond counts. The 60x latency reduction for inference queries was particularly impressive.