Spark + Alluxio Overview | Pair Spark with Alluxio to Modernize Your Data Platform

By bringing Alluxio together with Spark, you can modernize your data platform in a scalable, agile, and cost-effective way.  In this post, we provide an overview of the Spark + Alluxio stack. We explain the architecture, discuss real-world examples, describe deployment models, and showcase performance and cost benchmarking.

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Architecting a Heterogeneous Data Platform Across Clusters, Regions, and Clouds

Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow or PyTorch. Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access. Alluxio enables you to embrace the separation of storage from compute and use Alluxio data orchestration to simplify adoption of the data lake and data mesh paradigms for analytics and AI/ML workloads.

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Accelerating Machine Learning / Deep Learning in the Cloud: Architecture and Benchmark

This whitepaper introduces how to speed up end-to-end  distributed training in the cloud using Alluxio to accelerate data access. With the help of Alluxio, loading data from cloud storage, training and caching data can be done in a transparent and distributed way as a part of the training process. This whitepaper also demonstrates how to set up and benchmark the end-to-end performance of the training process, along with a comparison of other popular approaches.

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Alluxio Use Cases Overview

Alluxio started as a virtual distributed file system, a research project out of the AMPLab at U.C. Berkeley. Alluxio foresaw the need for agility when accessing large data stores separated from compute engines like Hadoop or Spark.
Fast forward several years and over a thousand committers later, and Alluxio has blossomed into the industry’s leading data orchestration platform for analytics and AI/ML. But as with any new type of technology, figuring out the best ways to use it depends on your data environment, computational workloads, issues, and goals. 

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