Session will present data caching strategies for data analytics and AI; attend demonstration of its open source data platform in booth #29
SAN MATEO, CA – June 19, 2023 - Alluxio, the developer of the open source data platform that simplifies the management of large-scale analytics and AI/ML applications, today announced its participation at Data + AI Summit, taking place June 26 – 29, 2023 at Moscone Center, San Francisco, CA and virtually. Alluxio will also showcase its open source data platform in booth #29 during the event.
Alluxio Session at Data + AI Summit
Wednesday, June 28 at 3:30 PM PDT – “Data Caching Strategies For Data Analytics And AI,” by Beinan Wang, Senior Staff Software Engineer, Alluxio; and Chunxu Tang, Research Scientist, Alluxio
The increasing popularity of data analytics and artificial intelligence (AI) has led to a dramatic increase in the volume of data being used in these fields, creating a growing need for an enhanced computational capability. Cache plays a crucial role as an accelerator for data and AI computations, but it is important to note that these domains have different data access patterns, requiring different cache strategies. In this session, attendees will benefit from observations about data access patterns in the analytical SQL and AI training domains based on practical experience with large-scale systems. Beinan and Chunxu will discuss the evaluation results of various caching strategies for analytical SQL and AI and provide caching recommendations for different use cases. Over the years, they have learned some best practices from big internet companies about the following aspects of their journey:
- Traffic pattern for analytical SQL and cache strategy recommendation
- Traffic pattern for AI training and how to measure the cache efficiency for different AI training process
- Cache capacity planning based on real-time metrics of the working set
- Adaptive caching admission and eviction for uncertain traffic patterns
The Alluxio Exhibit
Alluxio is a Bronze Sponsor of Data + AI Summit. In booth #29, Alluxio will be offering free architecture assessments and cloud savings estimates. To book your assessment, register here and a Solutions Engineer will reach out to you to confirm a time slot. Alluxio will also be giving product demos and use case overviews at booth #29. Stop by to play some games during break time for a chance to win a drone!
Data + AI Summit registration is open here. Get a discounted full conference pass with this code: SPCUSrodttusd.
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About Alluxio
Alluxio is a leading provider of accelerated data access platforms for AI workloads. Alluxio’s distributed caching layer accelerates AI and data-intensive workloads by enabling high-speed data access across diverse storage systems. By creating a global namespace, Alluxio unifies data from multiple sources—on-premises and in the cloud—into a single, logical view, eliminating the need for data duplication or complex data movement.
Designed for scalability and performance, Alluxio brings data closer to compute frameworks like TensorFlow, PyTorch, and Spark, significantly reducing I/O bottlenecks and latency. Its intelligent caching, data locality optimization, and seamless integration with modern data platforms make it a powerful solution for teams building and scaling AI pipelines across hybrid and multi-cloud environments. Backed by leading investors, Alluxio powers technology, internet, financial services, and telecom companies, including 9 out of the top 10 internet companies globally. To learn more, visit www.alluxio.io.
Media Contact:
Amelia Wong
amelia@alluxio.com
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News & Press
SAN MATEO, Calif. — JUNE 3, 2026 — Alluxio, the developer of a leading large-scale caching solution for AI, today announced a solution designed to help organizations maximize GPU utilization and improve the efficiency of AI workloads on Oracle Cloud Infrastructure (OCI). By combining Alluxio’s data acceleration capabilities with OCI’s high-performance AI infrastructure, organizations can reduce data bottlenecks and keep GPUs continuously fed with data for training and inference.
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