Spark is a widely adopted open source framework that provides a unified interface for analytics and machine learning workloads. Alluxio, originating from the UC Berkeley AMPLab – the same lab as Spark, is an open source data orchestration platform that empowers compute frameworks like Spark by providing stateful caching to enable efficient data sharing between multiple jobs and improving resilience against job failures as well as bringing data together from many different sources, be it remote HDFS or cloud object stores.
Alluxio partnered with IBM to deliver a Spark-based solution to provide fast data analytics. With the integration of IBM Spectrum Conductor, an advanced workload and resource management platform that maximizes hardware utilization to speed results and cut infrastructure costs, Alluxio and IBM delivered a solution that powers leading telecom company’s applications to support 320 million subscribers. In this online meetup, we will present the benefits of the fast analytics stack of Spark on Alluxio and IBM and dive into a leading telecom’s use case of leveraging Spark and Alluxio to process massive amounts of mobile data.
In this online meetup, you will learn about:
- Why the leading companies are moving towards a decoupled compute and storage architecture, and the associated challenges and requirements.
- Why Spark and Alluxio together can solve the challenges and fulfill the requirements
- How leading telecom leverages Spark with Alluxio for fast data processing at scale on top of object store and HDFS
Spark is a widely adopted open source framework that provides a unified interface for analytics and machine learning workloads. Alluxio, originating from the UC Berkeley AMPLab – the same lab as Spark, is an open source data orchestration platform that empowers compute frameworks like Spark by providing stateful caching to enable efficient data sharing between multiple jobs and improving resilience against job failures as well as bringing data together from many different sources, be it remote HDFS or cloud object stores.
Alluxio partnered with IBM to deliver a Spark-based solution to provide fast data analytics. With the integration of IBM Spectrum Conductor, an advanced workload and resource management platform that maximizes hardware utilization to speed results and cut infrastructure costs, Alluxio and IBM delivered a solution that powers leading telecom company’s applications to support 320 million subscribers. In this online meetup, we will present the benefits of the fast analytics stack of Spark on Alluxio and IBM and dive into a leading telecom’s use case of leveraging Spark and Alluxio to process massive amounts of mobile data.
In this online meetup, you will learn about:
- Why the leading companies are moving towards a decoupled compute and storage architecture, and the associated challenges and requirements.
- Why Spark and Alluxio together can solve the challenges and fulfill the requirements
- How leading telecom leverages Spark with Alluxio for fast data processing at scale on top of object store and HDFS
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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
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.
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.