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.