Simplify and Accelerate Your Geo-Distributed Analytics Platform at Scale

Today, many organizations are running a multitude of data-driven applications and data platforms that span multiple geographic regions and across heterogeneous environments – public, private, hybrid, or multi-cloud. Further, the trend of separating compute resources from storage resources makes it easier to scale compute and storage independently, allowing organizations to keep up with new trends in data analytics and AI. In response, more organizations are modernizing their data platforms to meet their needs.

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

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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.

What’s New in Alluxio 2.7: Enhanced Scalability, Stability and Major Improvements in AI/ML Training Efficiency

With this release, Alluxio has strengthened its position as a de-facto data unification and acceleration solution in data analytics and machine learning pipelines. The solution is optimized to support Spark, Presto, Tensorflow, and PyTorch, and is available on multiple cloud platforms such as AWS, GCP, and Azure Cloud, and also on Kubernetes in private data centers or public clouds.