Blog
We are thrilled to announce the general availability of Alluxio Enterprise for Data Analytics 3.2! With data volumes continuing to grow at exponential rates, data platform teams face challenges in maintaining query performance, managing infrastructure costs, and ensuring scalability. This latest version of Alluxio addresses these challenges head-on with groundbreaking improvements in scalability, performance, and cost-efficiency.
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We’re excited to introduce Rapid Alluxio Deployer (RAD) on AWS, which allows you to experience the performance benefits of Alluxio in less than 30 minutes. RAD is designed with a split-plane architecture, which ensures that your data remains secure within your AWS environment, giving you peace of mind while leveraging Alluxio’s capabilities.
PyTorch is one of the most popular deep learning frameworks in production today. As models become increasingly complex and dataset sizes grow, optimizing model training performance becomes crucial to reduce training times and improve productivity.
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This blog was originally published on the website of NetApp: https://www.netapp.com/blog/modernize-analytics-workloads-netapp-alluxio/
Imagine as an IT leader having the flexibility to choose any services that are available in public cloud and on premises. And imagine being able to scale your storage for your data lakes with control over data locality and protection for your organization. With these goals in mind, NetApp and Alluxio are joining forces to help our customers adapt to new requirements for modernizing data architecture with low-touch operations for analytics, machine learning, and artificial intelligence workflows.
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In the previous blog, we introduced Uber’s Presto use cases and how we collaborated to implement Alluxio local cache to overcome different challenges in accelerating Presto queries. The second part discusses the improvements to the local cache metadata.
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This article shares how Uber and Alluxio collaborated to design and implement Presto local cache to reduce HDFS latency.
This article introduces the design and implementation of metadata storage in Alluxio Master, either on heap and off heap (based on RocksDB).
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Raft is an algorithm for state machine replication as a way to ensure high availability (HA) and fault tolerance. This blog shares how Alluxio has moved to a Zookeeper-less, built-in Raft-based journal system as a HA implementation.
With machine learning (ML) and artificial intelligence (AI) applications becoming more business-critical, organizations are in the race to advance their AI/ML capabilities. To realize the full potential of AI/ML, having the right underlying machine learning platform is a prerequisite.
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This article will discuss a new solution to orchestrating data for end-to-end machine learning pipelines that addresses the above questions. I will outline common challenges and pitfalls, followed by proposing a new technique, data orchestration, to optimize the data pipeline for machine learning.
Today, we are excited to announce the launch of Non-fungible token (NFT) as a new feature in our leading data orchestration platform.
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With the collaboration between Meta (Facebook), Princeton University, and Alluxio, we have developed "Shadow Cache" – a lightweight Alluxio component to track the working set size and infinite cache hit ratio. Shadow cache can keep track of the working set size over the past window dynamically and is implemented by a series of bloom filters. Shadow cache is deployed in Meta (Facebook) Presto and is being leveraged to understand the system bottleneck and help with routing design decisions.
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