ING Bank is a multinational financial services company headquartered in Amsterdam with over $1 trillion in assets. As a leading bank, we place a great emphasis on cybersecurity. One aspect of this is the Security incident and event management (SIEM), which is the process of identifying, monitoring, recording and analyzing security events or incidents within a real-time IT environment. SIEM requires our data platform to have high and consistent performance, so we use open source technologies Presto and Alluxio for fast SQL analytics in the cloud.
In this online presentation, we are going to present how ING is leveraging Presto (interactive query), Alluxio (data orchestration & acceleration), S3 (massive storage), and DC/OS (container orchestration) to build and operate our modern Security Analytics & Machine Learning platform. We will share the challenges we encountered and how we solved them. Today we run this platform in several different data centers, and we have reduced our 10+ minutes queries to under 10 seconds!
ING Bank is a multinational financial services company headquartered in Amsterdam with over $1 trillion in assets. As a leading bank, we place a great emphasis on cybersecurity. One aspect of this is the Security incident and event management (SIEM), which is the process of identifying, monitoring, recording and analyzing security events or incidents within a real-time IT environment. SIEM requires our data platform to have high and consistent performance, so we use open source technologies Presto and Alluxio for fast SQL analytics in the cloud.
In this online presentation, we are going to present how ING is leveraging Presto (interactive query), Alluxio (data orchestration & acceleration), S3 (massive storage), and DC/OS (container orchestration) to build and operate our modern Security Analytics & Machine Learning platform. We will share the challenges we encountered and how we solved them. Today we run this platform in several different data centers, and we have reduced our 10+ minutes queries to under 10 seconds!
Video:
Presentation slides:
ING Bank is a multinational financial services company headquartered in Amsterdam with over $1 trillion in assets. As a leading bank, we place a great emphasis on cybersecurity. One aspect of this is the Security incident and event management (SIEM), which is the process of identifying, monitoring, recording and analyzing security events or incidents within a real-time IT environment. SIEM requires our data platform to have high and consistent performance, so we use open source technologies Presto and Alluxio for fast SQL analytics in the cloud.
In this online presentation, we are going to present how ING is leveraging Presto (interactive query), Alluxio (data orchestration & acceleration), S3 (massive storage), and DC/OS (container orchestration) to build and operate our modern Security Analytics & Machine Learning platform. We will share the challenges we encountered and how we solved them. Today we run this platform in several different data centers, and we have reduced our 10+ minutes queries to under 10 seconds!
Video:
Presentation slides:
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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.