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

Virtual Product School

Thursday, January 2710:00 AM PT

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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Speaker: Adit Madan

Sr. Product Manager, Alluxio

Adit Madan is a Sr. Product Manager at Alluxio. Adit is experienced in multiple roles and is also a core maintainer and Project Management Committee (PMC) member of the Alluxio Open Source project. Prior to Alluxio, he was a Research Engineer at Hewlett-Packard Laboratories. Adit has extensive experience in distributed systems and large-scale data analytics. Adit holds an MS from Carnegie Mellon University and a BS from the Indian Institute of Technology – Delhi.

Alluxio is...

...a data orchestration layer for compute in any cloud. It unifies data silos on-premise and across any cloud to give you data locality, accessibility, and elasticity.

Whether it’s accelerating big data frameworks on the public cloud, running big data workloads in hybrid cloud environments, or enabling big data on object stores or multiple clouds, Alluxio reduces the complexities associated with orchestrating data for today’s big data and AI/ML workloads.