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
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Videos
Unlock the full performance of your AI/ML infrastructure on Oracle Cloud Infrastructure (OCI).
Join Oracle's Master Principal Cloud Architect Xinghong He and Alluxio's VP of Technology Bin Fan for an in-depth technical session exploring how modern tiered caching, optimized storage integration, and smart deployment choices can deliver sub-millisecond latency and up to 5× faster data access on OCI — at scale.
You'll learn about:
- Architectural insights: How Alluxio’s tiered caching architecture works with OCI Object Storage and BM.DenseIO compute instances to eliminate data access bottlenecks.
- Benchmark-proven results: See real MLPerf Storage 2.0 and Warp benchmark outcomes demonstrating sub-millisecond latency and dramatic throughput gains.
- Deployment strategies: Compare deployment options — dedicated mode for peak performance vs. co-located mode for cost-efficient scale.
- Practical, actionable guidance: Implementation best practices you can apply directly to your AI/ML workloads on OCI.
Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
