While running analytics workloads using EMR Spark on S3 is a common deployment today, many organizations face issues in performance and consistency. EMR can be bottlenecked when reading large amounts of data from S3, and sharing data across multiple stages of a pipeline can be difficult as S3 is eventually consistent for read-your-own-write scenarios.
A simple solution is to run Spark on Alluxio as a distributed cache for S3. Alluxio stores data in memory close to Spark, providing high performance, in addition to providing data accessibility and abstraction for deployments in both public and hybrid clouds.
In this webinar you'll learn how to:
Increase performance by setting up Alluxio so Spark can seamlessly read from and write to S3
Use Alluxio as the input/output for Spark applications
Save and load Spark RDDs and Dataframes with Alluxio
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 at Alluxio
...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.