Using “zero-copy” hybrid bursting with Spark to solve capacity problems
Want to leverage your existing investments in Hadoop with your data on-premise and still benefit from the elasticity of the cloud?
Like other Hadoop users, you most likely experience very large and busy Hadoop clusters, particularly when it comes to compute capacity. Bursting HDFS data to the cloud can bring challenges – network latency impacts performance, copying data via DistCP means maintaining duplicate data, and you may have to make application changes to accomodate the use of S3.
“Zero-copy” hybrid bursting with Alluxio keeps your data on-prem and syncs data to compute in the cloud so you can expand compute capacity, particularly for ephemeral Spark jobs.
In this tech talk, we’ll discuss:
- Approaches to burst data to the cloud
- How Alluxio can enable “zero-copy” bursting of Spark workloads to cloud data services like EMR and Dataproc
- How DBS Bank uses Alluxio to solve for limited on-prem compute capacity by zero-copy bursting Spark workloads to AWS EMR
Using “zero-copy” hybrid bursting with Spark to solve capacity problems
Want to leverage your existing investments in Hadoop with your data on-premise and still benefit from the elasticity of the cloud?
Like other Hadoop users, you most likely experience very large and busy Hadoop clusters, particularly when it comes to compute capacity. Bursting HDFS data to the cloud can bring challenges – network latency impacts performance, copying data via DistCP means maintaining duplicate data, and you may have to make application changes to accomodate the use of S3.
“Zero-copy” hybrid bursting with Alluxio keeps your data on-prem and syncs data to compute in the cloud so you can expand compute capacity, particularly for ephemeral Spark jobs.
In this tech talk, we’ll discuss:
- Approaches to burst data to the cloud
- How Alluxio can enable “zero-copy” bursting of Spark workloads to cloud data services like EMR and Dataproc
- How DBS Bank uses Alluxio to solve for limited on-prem compute capacity by zero-copy bursting Spark workloads to AWS EMR
Using “zero-copy” hybrid bursting with Spark to solve capacity problems
Want to leverage your existing investments in Hadoop with your data on-premise and still benefit from the elasticity of the cloud?
Like other Hadoop users, you most likely experience very large and busy Hadoop clusters, particularly when it comes to compute capacity. Bursting HDFS data to the cloud can bring challenges – network latency impacts performance, copying data via DistCP means maintaining duplicate data, and you may have to make application changes to accomodate the use of S3.
“Zero-copy” hybrid bursting with Alluxio keeps your data on-prem and syncs data to compute in the cloud so you can expand compute capacity, particularly for ephemeral Spark jobs.
In this tech talk, we’ll discuss:
- Approaches to burst data to the cloud
- How Alluxio can enable “zero-copy” bursting of Spark workloads to cloud data services like EMR and Dataproc
- How DBS Bank uses Alluxio to solve for limited on-prem compute capacity by zero-copy bursting Spark workloads to AWS EMR
Videos:
Presentation Slides:
Complete the form below to access the full overview:
.png)
Videos
Pratik Mishra delivered insights on architecting scalable, deployable, and resilient AI infrastructure at scale. His discussion on fault tolerance, checkpoint optimization, and the democratization of AI compute through AMD's open ecosystem resonated strongly with the challenges teams face in production ML deployments.
In this talk, Bin Fan, VP of Technology at Alluxio, presents on building tiered architectures that bring sub-millisecond latency to S3-based workloads. The comparison showing Alluxio's 45x performance improvement over S3 Standard and 5x over S3 Express One Zone demonstrated the critical role the performance & caching layer plays in modern AI infrastructure.
In this talk, Greg Lindstrom shared how Blackout Power Trading achieved double-digit millisecond offline feature store performance using Alluxio, a game-changer for real-time power trading where every millisecond counts. The 60x latency reduction for inference queries was particularly impressive.