On Demand Video

Modernizing Your Data Platform for Analytics and AI in the Hybrid Cloud Era


With data lakes expanding from on-prem to the cloud as well as increasing use of new object data stores, data platform teams are challenged with providing consistent, high-throughput access to distributed data sources for analytics and AI/ML applications. In today’s hybrid cloud and multi-cloud era, data-intensive applications such as Presto, Spark, Hive, and Tensorflow are suffering more sluggish response times and increased complexity with the growing separation of data and compute.

Join Alluxio’s distributed systems experts as they explore today’s data access challenges and open source data orchestration solutions for modernizing your data platform.

In this tech talk, you’ll learn:

  • How data access and throughput challenges are hindering large-scale analytics and AI/ML applications
  • How a data orchestration layer can simplify distributed data access and improve performance
  • Real-world production use cases and example journeys for architecting a modern data platform


Bin Fan is the founding engineer and VP of Open Source at Alluxio. Prior to Alluxio, he worked for Google to build the next-generation storage infrastructure. Bin received his Ph.D. in Computer Science from Carnegie Mellon University on the design and implementation of distributed systems.

Adit Madan has extensive experience in distributed systems, storage systems, and large-scale data analytics. Adit holds an MS from Carnegie Mellon University and a BS from the Indian Institute of Technology – Delhi. Adit is a product manager at Alluxio and is also a core maintainer and Project Management Committee (PMC) member of the Alluxio Open Source project.

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