Kubernetes is widely used across enterprises to orchestrate computation. And while Kubernetes helps improve flexibility and portability for computation in public/hybrid cloud environments across infrastructure providers, running data-intensive workloads can be challenging.
When it comes to efficiently moving data closer to Spark or Presto frameworks, co-locating data with these frameworks and accessing data from multiple or remote clouds is hard to do. That’s where Alluxio, an open source data orchestration platform, can help.
Alluxio enables data locality with your Spark and Presto workloads for faster performance and better data accessibility in Kubernetes. It also provides portability across storage providers.
In this on demand webinar we’ll give a quick overview of Alluxio and the use cases it powers for Spark/Presto in Kubernetes. We’ll show you how to set up Alluxio and Spark/Presto to run in Kubernetes as well.
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Speaker: Adit Madan
Adit Madan is a core engineer at Alluxio. His experience is in distributed systems, storage systems, and large-scale data analytics. He has a M.S. from Carnegie Mellon University, and a B.S. from IIT.
Distributed Systems Engineer at Alluxio
Speaker: Dipti Borkar
Dipti Borkar is the VP of Product & Marketing at Alluxio with over 15 years experience in data and database technology across relational and non-relational. Prior to Alluxio, Dipti was VP of Product Marketing at Kinetica and Couchbase. Dipti holds a M.S. in Computer Science from the UC San Diego, and an MBA from the Haas School of Business at UC Berkeley.
VP, Product and Marketing
...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.