Architecting a Heterogeneous Data Platform Across Clusters, Regions, and Clouds
January 27, 2022
By 
Adit Madan

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.

Join Alluxio’s Sr. Product Mgr., Adit Madan, to learn:

  • Key challenges with architecting a successful heterogeneous data platform
  • How data orchestration can overcome data access challenges in a distributed, heterogeneous environment
  • How to identify ways to use Alluxio to meet the needs of your own data environment and workload requirements

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.

Join Alluxio’s Sr. Product Mgr., Adit Madan, to learn:

  • Key challenges with architecting a successful heterogeneous data platform
  • How data orchestration can overcome data access challenges in a distributed, heterogeneous environment
  • How to identify ways to use Alluxio to meet the needs of your own data environment and workload requirements

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Architecting a Heterogeneous Data Platform Across Clusters, Regions, and Clouds from Alluxio, Inc.

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