Products
Tech Talk: Build a hybrid data lake and burst processing to Google Cloud Dataproc with Alluxio
May 28, 2020
ALLUXIO TECH TALK
As the amount of data analyzed and stored continues to grow exponentially, fixed on-premises infrastructure like Apache Hadoop data lakes becomes costly. Add to that the need to support newer and popular frameworks on an already busy data lake, it is not uncommon to see Hadoop-based data lakes running at beyond 100% utilization and hybrid processing split between physical and cloud infrastructure. As a result, companies are looking to leverage the flexibility and cost savings of the cloud.
Join us for this tech talk where we will show you how Alluxio can help burst your private computing environment to Google Cloud, minimizing costs and I/O overhead. Alluxio coupled with Google’s open source data and analytics processing engine, Dataproc, enables zero-copy burst for faster query performance in the cloud so you can take advantage of resources that are not local to your data, without the need for managing the copying or syncing of that data.
We’ll also show a demo on how to get up and running with Alluxio and Dataproc, including how to:
- Setup your hybrid environment between your private datacenter and Google Cloud Platform
- Burst a Spark based machine learning algorithm to Dataproc while accessing on-prem data
- Scale analytic workloads directly on data on-prem without copying and synchronizing the data into the cloud
ALLUXIO TECH TALK
As the amount of data analyzed and stored continues to grow exponentially, fixed on-premises infrastructure like Apache Hadoop data lakes becomes costly. Add to that the need to support newer and popular frameworks on an already busy data lake, it is not uncommon to see Hadoop-based data lakes running at beyond 100% utilization and hybrid processing split between physical and cloud infrastructure. As a result, companies are looking to leverage the flexibility and cost savings of the cloud.
Join us for this tech talk where we will show you how Alluxio can help burst your private computing environment to Google Cloud, minimizing costs and I/O overhead. Alluxio coupled with Google’s open source data and analytics processing engine, Dataproc, enables zero-copy burst for faster query performance in the cloud so you can take advantage of resources that are not local to your data, without the need for managing the copying or syncing of that data.
We’ll also show a demo on how to get up and running with Alluxio and Dataproc, including how to:
- Setup your hybrid environment between your private datacenter and Google Cloud Platform
- Burst a Spark based machine learning algorithm to Dataproc while accessing on-prem data
- Scale analytic workloads directly on data on-prem without copying and synchronizing the data into the cloud
Videos:
Presentation Slides:
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Videos
Bridging Speed and Scale: AWS S3 Data Caching for Low-Latency, Semantically-Rich AI Workloads

Amazon S3 and other cloud object stores have become the de facto storage system for organizations large and small. And it’s no wonder why. Cloud object stores deliver unprecedented flexibility with unlimited capacity that scales on demand and ensures data durability out-of-the-box at unbeatable prices.
Yet as workloads shift toward real-time AI, inference, feature stores, and agentic memory systems, S3’s latency and limited semantics begin to show their limits. In this webinar, you’ll learn how to augment — rather than replace — S3 with a tiered architecture that restores sub-millisecond performance, richer semantics, and high throughput — all while preserving S3’s advantages of low-cost capacity, durability, and operational simplicity.
We’ll walk through:
- The key challenges posed by latency-sensitive, semantically rich workloads (e.g. feature stores, RAG pipelines, write-ahead logs)
- Why “just upgrading storage” isn’t sufficient — the bottlenecks in metadata, object access latency, and write semantics
- How Alluxio transparently layers on top of S3 to provide ultra-low latency caching, append semantics, and zero data migration with both FSx-style POSIX access and S3 API access
- Real-world results: achieving sub-ms TTFB, 90%+ GPU utilization in ML training, 80X faster feature store query response times, and dramatic cost savings from reduced S3 operations
- Trade-offs, deployment patterns, and best practices for integrating this tiered approach in your AI/analytics stack
October 28, 2025
AI/ML Infra Meetup | AI at scale Architecting Scalable, Deployable and Resilient Infrastructure

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.
September 30, 2025
AI/ML Infra Meetup | Alluxio + S3 A Tiered Architecture for Latency-Critical, Semantically-Rich Workloads

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.
September 30, 2025