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.
This article describes how engineers in the Data Service Center at Tencent PCG leverages Alluxio to optimize the analytics performance by 200% and minimize the operating cost in building Tencent Beacon Growing, a real-time data analytics platform.
Google’s TensorFlow and Facebook’s PyTorch are two Deep Learning frameworks that have been popular with the open source community. Although PyTorch is still a relatively new framework, many developers have successfully adopted it due to its ease of use. By default, PyTorch does not support Deep Learning model training directly in HDFS, which brings challenges … Continued
A collaboration of Alibaba, Alluxio, and Nanjing University in tackling the problems of Deep Learning model training in the cloud. Our goal was to reduce the cost and complexity of data access for Deep Learning training in a hybrid environment, which resulted in over 40% reduction in training time and cost.
Testing Methodology Decision support workload is a typical workload that models multiple aspects of a decision support system, including queries and data maintenance. We selected 54 queries that represent a typical SQL query behavior in Hadoop for the test. The tests include three different configurations: Without Alluxio, Alluxio on PMem and Alluxio on DRAM. The … Continued
This article describes how Alluxio can accelerate the training of deep learning models in a hybrid cloud environment when using Intel’s Analytics Zoo open source platform, powered by oneAPI. Details on the new architecture and workflow, as well as Alluxio’s performance benefits and benchmarks results will be discussed.
Are you using SQL engines, such as Presto, to query existing Hive data warehouse and experiencing challenges including overloaded Hive Metastore with slow and unpredictable access, unoptimized data formats and layouts such as too many small files, or lack of influence over the existing Hive system and other Hive applications?
This article goes through a simple example to illustrate how Structured Data Management available in the latest Alluxio 2.2.0 release to help SQL and structured data workloads.
This article introduces Structured Data Management available in the latest Alluxio 2.2.0 release, a new effort to provide further benefits to SQL and structured data workloads using Alluxio.