Today’s conventional wisdom states that network latency across the two ends of a hybrid cloud prevents you from running analytic workloads in the cloud with the data on-prem. As a result, most companies copy their data into a cloud environment and maintain that duplicate data. All of this means that it is challenging to make both on-prem HDFS data accessible with the desired application performance.
In this talk, we will show you how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud.
In this Office Hour, we will go over:
- A strategy to embrace the hybrid cloud, including an architecture for running ephemeral compute clusters using on-prem HDFS.
- An example of running on-demand Presto, Spark, and Hive with Alluxio in the public cloud.
- An analysis of experiments with TPC-DS to demonstrate the benefits of the given architecture.
ALLUXIO COMMUNITY OFFICE HOUR
Today’s conventional wisdom states that network latency across the two ends of a hybrid cloud prevents you from running analytic workloads in the cloud with the data on-prem. As a result, most companies copy their data into a cloud environment and maintain that duplicate data. All of this means that it is challenging to make both on-prem HDFS data accessible with the desired application performance.
In this talk, we will show you how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud.
In this Office Hour, we will go over:
- A strategy to embrace the hybrid cloud, including an architecture for running ephemeral compute clusters using on-prem HDFS.
- An example of running on-demand Presto, Spark, and Hive with Alluxio in the public cloud.
- An analysis of experiments with TPC-DS to demonstrate the benefits of the given architecture.
Video:
Slides:
ALLUXIO COMMUNITY OFFICE HOUR
Today’s conventional wisdom states that network latency across the two ends of a hybrid cloud prevents you from running analytic workloads in the cloud with the data on-prem. As a result, most companies copy their data into a cloud environment and maintain that duplicate data. All of this means that it is challenging to make both on-prem HDFS data accessible with the desired application performance.
In this talk, we will show you how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud.
In this Office Hour, we will go over:
- A strategy to embrace the hybrid cloud, including an architecture for running ephemeral compute clusters using on-prem HDFS.
- An example of running on-demand Presto, Spark, and Hive with Alluxio in the public cloud.
- An analysis of experiments with TPC-DS to demonstrate the benefits of the given architecture.
Video:
Slides:
Videos:
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Videos
In the rapidly evolving landscape of AI and machine learning, Platform and Data Infrastructure Teams face critical challenges in building and managing large-scale AI platforms. Performance bottlenecks, scalability of the platform, and scarcity of GPUs pose significant challenges in supporting large-scale model training and serving.
In this talk, we introduce how Alluxio helps Platform and Data Infrastructure teams deliver faster, more scalable platforms to ML Engineering teams developing and training AI models. Alluxio’s highly-distributed cache accelerates AI workloads by eliminating data loading bottlenecks and maximizing GPU utilization. Customers report up to 4x faster training performance with high-speed access to petabytes of data spread across billions of files regardless of persistent storage type or proximity to GPU clusters. Alluxio’s architecture lowers data infrastructure costs, increases GPU utilization, and enables workload portability for navigating GPU scarcity challenges.
In this talk, Zhe Zhang (NVIDIA, ex-Anyscale) introduced Ray and its applications in the LLM and multi-modal AI era. He shared his perspective on ML infrastructure, noting that it presents more unstructured challenges, and recommended using Ray and Alluxio as solutions for increasingly data-intensive multi-modal AI workloads.
As large-scale machine learning becomes increasingly GPU-centric, modern high-performance hardware like NVMe storage and RDMA networks (InfiniBand or specialized NICs) are becoming more widespread. To fully leverage these resources, it’s crucial to build a balanced architecture that avoids GPU underutilization. In this talk, we will explore various strategies to address this challenge by effectively utilizing these advanced hardware components. Specifically, we will present experimental results from building a Kubernetes-native distributed caching layer, utilizing NVMe storage and high-speed RDMA networks to optimize data access for PyTorch training.