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Alluxio 2.5 focuses on improving interface support to broaden the set of data driven applications which can benefit from data orchestration. The POSIX and S3 client interfaces have greatly improved in performance and functionality as a result of the widespread usage and demand from AI/ML workloads and system administration needs. Alluxio is rapidly evolving to meet the needs of enterprises that are deploying it as a key component of their AI/ML stacks.

Data processing is increasingly making use of NVIDIA computing for massive parallelism. Advancements in accelerated compute mean that access to storage must also be quicker, whether in analytics, artificial intelligence (AI), or machine learning (ML) pipelines.

This post outlines a solution for building a hybrid data lake with Alluxio to leverage analytics and AI on Amazon Web Services (AWS) alongside a multi-petabyte on-premises data lake. Alluxio’s solution is called “zero-copy” hybrid cloud, indicating a cloud migration approach without first copying data to Amazon Simple Storage Service (Amazon S3).

How T3Go's high-performance data lake using Apache Hudi and Alluxio shortened the time for data ingestion into the lake by up to a factor of 2. Data analysts using Presto, Hudi, and Alluxio in conjunction to query data on the lake saw queries speed up by 10 times faster.

We’re pleased to announce the general availability of Alluxio Data Orchestration Hub, your single pane of glass to orchestrate data for analytics and AI. The data ecosystem is complex with the separation of storage and compute across data centers and cloud providers. With this release we’ve made great strides towards simplifying data access and management across multiple environments.

Unlike HDFS which provides one-copy update semantics or AWS S3 which provides eventual consistency, data consistency in Alluxio is a bit more complicated and depends on the configuration. In short, when clients are only reading and writing through Alluxio, the Alluxio file system provides strong consistency. However, when clients are writing data across both Alluxio and under storage, the consistency may depend on the write type and under storage type.

This article described how engineers at datasapiens brought down S3 API costs by 200x by implementing Alluxio as a data orchestration layer between S3 and Presto.

As the third largest e-commerce site in China, Vipshop processes large amounts of data collected daily to generate targeted advertisements for its consumers. In this article, Gang Deng from Vipshop describes how to meet SLAs by improving struggling Spark jobs on HDFS by up to 30x, and optimize hot data access with Alluxio to create a reliable and stable computation pipeline for e-commerce targeted advertising.

In this blog, Derek Tan, Executive Director of Infra & Simulation at WeRide, describes how engineers leverage Alluxio as a hybrid cloud data gateway for applications on-premises to access public cloud storage like AWS S3.

Alluxio 2.3.0 focuses on streamlining the user experience in hybrid cloud deployments where Alluxio is deployed with compute in the cloud to access data on-prem. Features such as environment validation tools and concurrent metadata synchronization greatly improve Alluxio’s functionality. Integrations with AWS EMR, Google Dataproc, K8s, and AWS Glue make Alluxio easy to use in a variety of cloud environments. In this article, we will share some of the highlights of the release. For more, please visit our release notes page.