Alluxio foresaw the need for agility when accessing data across silos separated from compute engines like Spark, Presto, Tensorflow and PyTorch. Embracing the separation of storage from compute, the Alluxio data orchestration platform simplifies adoption of the data lake and data mesh paradigm for analytics and AI/ML. In this talk, Bin Fan will share observations to help identify ways to use the platform to meet the needs of your data environment and workloads.
越來越多的企業架構已轉向混合雲和多雲環境。雖然這種轉變帶來了更大的靈活性和敏捷性，但也意味著必須將計算與存儲分離，這就對企業跨框架、跨雲和跨存儲系統的數據管理和編排提出了新的挑戰。此分享將讓聽眾深入了解Alluxio數據編排理念在數據中台對存儲和計算的解耦作用，以及數據編排針對存算分離場景提出的創新架構，同時結合來自金融、運營商、互聯網等行業的典型應用場景來展現Alluxio如何為大數據計算帶來真正的加速，以及如何將數據編排技術用於AI模型訓練！
*This is a bilingual presentation.
Alluxio foresaw the need for agility when accessing data across silos separated from compute engines like Spark, Presto, Tensorflow and PyTorch. Embracing the separation of storage from compute, the Alluxio data orchestration platform simplifies adoption of the data lake and data mesh paradigm for analytics and AI/ML. In this talk, Bin Fan will share observations to help identify ways to use the platform to meet the needs of your data environment and workloads.
越來越多的企業架構已轉向混合雲和多雲環境。雖然這種轉變帶來了更大的靈活性和敏捷性，但也意味著必須將計算與存儲分離，這就對企業跨框架、跨雲和跨存儲系統的數據管理和編排提出了新的挑戰。此分享將讓聽眾深入了解Alluxio數據編排理念在數據中台對存儲和計算的解耦作用，以及數據編排針對存算分離場景提出的創新架構，同時結合來自金融、運營商、互聯網等行業的典型應用場景來展現Alluxio如何為大數據計算帶來真正的加速，以及如何將數據編排技術用於AI模型訓練！
*This is a bilingual presentation.
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Videos
Nilesh Agarwal, Co-founder & CTO at Inferless, shares insights on accelerating LLM inference in the cloud using Alluxio, tackling key bottlenecks like slow model weight loading from S3 and lengthy container startup time. Inferless uses Alluxio as a three-tier cache system that dramatically cuts model load time by 10x.
In this talk, Jingwen Ouyang, Senior Product Manager at Alluxio, will share how Alluxio make it easy to share and manage data from any storage to any compute engine in any environment with high performance and low cost for your model training, model inference, and model distribution workload.
Storing data as Parquet files on cloud object storage, such as AWS S3, has become prevalent not only for large-scale data lakes but also as lightweight feature stores for training and inference, or as document stores for Retrieval-Augmented Generation (RAG). However, querying petabyte-to-exabyte-scale data lakes directly from S3 remains notoriously slow, with latencies typically ranging from hundreds of milliseconds to several seconds.
In this webinar, David Zhu, Software Engineering Manager at Alluxio, will present the results of a joint collaboration between Alluxio and a leading SaaS and data infrastructure enterprise that explored leveraging Alluxio as a high-performance caching and acceleration layer atop AWS S3 for ultra-fast querying of Parquet files at PB scale.
David will share:
- How Alluxio delivers sub-millisecond Time-to-First-Byte (TTFB) for Parquet queries, comparable to S3 Express One Zone, without requiring specialized hardware, data format changes, or data migration from your existing data lake.
- The architecture that enables Alluxio’s throughput to scale linearly with cluster size, achieving one million queries per second on a modest 50-node deployment, surpassing S3 Express single-account throughput by 50x without latency degradation.
- Specifics on how Alluxio offloads partial Parquet read operations and reduces overhead, enabling direct, ultra-low-latency point queries in hundreds of microseconds and achieving a 1,000x performance gain over traditional S3 querying methods.
Speaker: David Zhu
David Zhu is a Software Engineer Manager at Alluxio. At Alluxio, David focuses on metadata management and end-to-end performance benchmarking and optimizations. Prior to that, David completed his Ph.D. from UC Berkeley, with a focus on distributed data management systems and operating systems for the data center. David also holds a Bachelor of Software Engineering from the University of Waterloo.