On-Demand Videos
Real-time OLAP databases are optimized for speed and often rely on tightly coupled storage-compute architectures using disks or SSDs. Decoupled architectures, which use cloud object storage, introduce an unavoidable tradeoff: cost efficiency at the expense of performance. This makes them unsuitable for databases that need to provide low-latency, real-time analytics, especially the new wave of LLM-powered dashboards, retrieval-augmented generation (RAG), and vector-embedding searches that thrive only when fresh data is milliseconds away. Can we achieve both cost efficiency and performance?
In this talk, we’ll explore the engineering challenges of extending Apache Pinot—a real-time OLAP system—onto cloud object storage while still maintaining sub-second P99 latencies.
We’ll dive into how we built an abstraction in Apache Pinot to make it agnostic to the location of data. We’ll explain how we can query data directly from the cloud (without needing to download the entire dataset, as with lazy-loading) while achieving sub-second latencies. We’ll cover the data fetch and optimization strategies we implemented, such as pipelining fetch and compute, prefetching, selective block fetches, index pinning, and more. We'll also share our latest work about integration with open table formats like iceberg, and how we will continue to achieve fast analytics directly on parquet files by implementing all the same techniques that apply to tiered storage.
The data lake is a fantastic, low-cost place to put data at rest for offline analytics, but we've built it under the terms of a terrible bargain: all that cheap storage at scale was a great thing, but we gave up schema management and transactions along the way. Apache Iceberg has emerged as king of the Open Table Formats to fix this very problem.
Built on the foundation of Parquet files, Iceberg adds a simple yet flexible metadata layer and integration with standard data catalogs to provide robust schema support and ACID transactions to the once ungoverned data lake. In this talk, we'll build Iceberg up from the basics, see how the read and write path work, and explore how it supports streaming data sources like Apache Kafka™. Then we'll see how Confluent's Tableflow brings Kafka together with open table formats like Iceberg and Delta Lake to make operational data in Kafka topics instantly visible to the data lake without the usual ETL—unifying the operational/analytical divide that has been with us for decades.
Storing data as Parquet files on S3 is increasingly used not just as a data lake but also as a lightweight feature store for ML training/inference or a document store for RAG. However, querying petabyte- to exabyte-scale data lakes directly from cloud object storage remains notoriously slow (e.g., latencies ranging from hundreds of milliseconds to several seconds on AWS S3).
In this talk, we show how architecture co-design, system-level optimizations, and workload-aware engineering can deliver over 1000× performance improvements for these workloads—without changing file formats, rewriting data paths, or provisioning expensive hardware.
We introduce a high-performance, low-latency S3 proxy layer powered by Alluxio, deployed atop hyperscale data lakes. This proxy delivers sub-millisecond Time-to-First-Byte (TTFB)—on par with Amazon S3 Express—while preserving compatibility with standard S3 APIs. In real-world benchmarks, a 50-node Alluxio cluster sustains over 1 million S3 queries per second, offering 50× the throughput of S3 Express for a single account, with no compromise in latency.
Beyond accelerating access to Parquet files byte-to-byte, we also offload partial Parquet processing from query engines via a pluggable interface into Alluxio. This eliminates the need for costly index scans and file parsing, enabling point queries with 0.3 microseconds latency and up to 3,000 QPS per instance (measured using a single-thread)—a 100× improvement over traditional query paths.
.png)
Watch this video to gain insights onhow Uber manages its Generative AI Gateway, which powers all generative AI applications across the company.
Join us to learn about the latest release of Alluxio Enterprise AI. In this webinar, we’ll provide an overview of the new features and capabilities of Alluxio Enterprise AI, built to accelerate AI workloads and maximize GPU utilization.
Key highlights include:
- New caching mode accelerates AI checkpoints
- Advanced cache eviction policies provide fine-grained control
- Python SDK integrations enhance AI framework compatibility
- A demo of Alluxio accelerating AI training workloads in AWS
Ready to optimize your AI infra strategy? Watch this on-demand video, where Bin Fan, VP of Technology at Alluxio, will guide you through how to balance cost & performance for GPU/CPU workloads.
LLM inference can be huge, particularly, with long contexts. In this on-demand video, Junchen Jiang, Assistant Professor at University of Chicago, presents a 10x solution for long contexts inference: an easy-to-deploy stack over multiple vLLM engines with tailored KV-cache backend.
You won't want to miss this talk presented by Robert Nishihara, Co-Founder of Anyscale, which is packed with insights on using Ray to conquer the last-mile challenges in AI deployment.
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.
In this talk, Sandeep Manchem discussed big data and AI, covering typical platform architecture and data challenges. We had engaging discussions about ensuring data safety and compliance in Big Data and AI applications.
TorchTitan is a proof-of-concept for Large-scale LLM training using native PyTorch. It is a repo that showcases PyTorch's latest distributed training features in a clean, minimal codebase.
In this talk, Tianyu will share TorchTitan’s design and optimizations for the Llama 3.1 family of LLMs, spanning 8 billion to 405 billion parameters, and showcase its performance, composability, and scalability.
AI training workloads running on compute engines like PyTorch, TensorFlow, and Ray require consistent, high-throughput access to training data to maintain high GPU utilization. However, with the decoupling of compute and storage and with today’s hybrid and multi-cloud landscape, AI Platform and Data Infrastructure teams are struggling to cost-effectively deliver the high-performance data access needed for AI workloads at scale.
Join Tom Luckenbach, Alluxio Solutions Engineering Manager, to learn how Alluxio enables high-speed, cost-effective data access for AI training workloads in hybrid and multi-cloud architectures, while eliminating the need to manage data copies across regions and clouds.
What Tom will share:
- AI data access challenges in cross-region, cross-cloud architectures.
- The architecture and integration of Alluxio with frameworks like PyTorch, TensorFlow, and Ray using POSIX, REST, or Python APIs across AWS, GCP and Azure.
- A live demo of an AI training workload accessing cross-cloud datasets leveraging Alluxio's distributed cache, unified namespace, and policy-driven data management.
- MLPerf and FIO benchmark results and cost-savings analysis.
As machine learning and deep learning models grow in complexity, AI platform engineers and ML engineers face significant challenges with slow data loading and GPU utilization, often leading to costly investments in high-performance computing (HPC) storage. However, this approach can result in overspending without addressing the core issues of data bottlenecks and infrastructure complexity.
A better approach is adding a data caching layer between compute and storage, like Alluxio, which offers a cost-effective alternative through its innovative data caching strategy. In this webinar, Jingwen will explore how Alluxio's caching solutions optimize AI workloads for performance, user experience and cost-effectiveness.
What you will learn:
- The I/O bottlenecks that slow down data loading in model training
- How Alluxio's data caching strategy optimizes I/O performance for training and GPU utilization, and significantly reduces cloud API costs
- The architecture and key capabilities of Alluxio
- Using Rapid Alluxio Deployer to install Alluxio and run benchmarks in AWS in just 30 minutes