Blog
Alluxio's strong Q2 featured Enterprise AI 3.7 launch with sub-millisecond latency (45× faster than S3 Standard), 50%+ customer growth including Salesforce and Geely, and MLPerf Storage v2.0 results showing 99%+ GPU utilization, positioning the company as a leader in maximizing AI infrastructure ROI.
In this blog, Greg Lindstrom, Vice President of ML Trading at Blackout Power Trading, an electricity trading firm in North American power markets, shares how they leverage Alluxio to power their offline feature store. This approach delivers multi-join query performance in the double-digit millisecond range, while maintaining the cost and durability benefits of Amazon S3 for persistent storage. As a result, they achieved a 22 to 37x reduction in large-join query latency for training and a 37 to 83x reduction in large-join query latency for inference.
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TL;DR: First the news - Alluxio support for K8s Helm charts now available! K8s is a certified environment for Alluxio. Now the take away- Alluxio brings back data locality for the disaggregated analytics stack in K8s. How? Read on.
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We are delighted by the success of the inaugural Data Orchestration Summit on Nov. 7, 2019! Organized by Alluxio, this one-day event was sold out with nearly 400 attendees! Data engineers, cloud engineers, data scientists joined the talks of 24 industry leaders from all over the globe to share their experiences building cloud-native data and AI platforms. All session recordings and slides are now available.
This tutorial guides users to set up a stack of Presto, Alluxio and Hive Metastore on your local server, and it demonstrates how to use Alluxio as the caching layer for Presto queries.
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For today’s blog post I interviewed Bin Fan, Founding Engineer and VP of Open Source at Alluxio. Bin is the PMC maintainer of the Alluxio open source project. Prior to Alluxio, he worked for Google on the next-generation storage infrastructure.
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This tutorial describes steps to set up an EMR cluster with Alluxio as a distributed caching layer for Hive, and run sample queries to access data in S3 through Alluxio.
This article describes my lessons from a previous project which moved a data pipeline originally running on a Hadoop cluster managed by my team, to AWS using EMR and S3. The goal was to leverage the elasticity of EMR to offload the operational work, as well as make S3 a data lake where different teams can easily share data across projects.
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This article describes how JD built this interactive OLAP platform combining two open-source technologies: Presto and Alluxio.
In this article, you will learn how to incorporate Alluxio to implement a unified distributed file system service as well as how to add extensions on top of Alluxio including customized authentication schemes and UDF (user-defined functions) on Alluxio files.
Alluxio is a new layer on top of under storage systems that can not only improve raw I/O performance but also enables applications flexible options to read, write and manage files. This article focuses on describing different ways to write files to Alluxio, realizing the tradeoffs in performance, consistency, and also the level of fault tolerance compared to HDFS.
Monitoring metrics is highly important to operate distributed systems in production. Alluxio collects metrics using the Codahale Metrics Library on I/O throughput, RPC throughput, and resource usage. Alluxio metrics are shown in its webUI, but are also available through a REST endpoint or exportable to several third-party sinks in a time-series manner (see docs).