Alluxio video presentations

Tech Talk: Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with Disaggregated Compute and Storage

Enterprises are increasingly looking towards object stores to power their big data & machine learning workloads in a cost-effective way. The combination of SwiftStack and Alluxio together, enables users to seamlessly move towards a disaggregated architecture. Swiftstack provides a massively parallel cloud object storage and multi-cloud data management system. Alluxio is a data orchestration layer, which sits between compute frameworks and storage systems and enables big data workloads to be deployed directly on SwiftStack. Alluxio provides data locality, accessibility and elasticity via its core innovations. With the Alluxio and Swiftstack solution, Spark, Presto, Tensorflow and Hive and other compute workloads can benefit from 10X performance improvement and dramatically lower costs. In this tech talk, we will provide a brief overview of the Alluxio and SwiftStack solution as well as the key use cases it enables.

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Tech Talk: Achieving Separation of Compute and Storage in a Cloud World

The rise of compute intensive workloads and the adoption of the cloud has driven organizations to adopt a decoupled architecture for modern workloads – one in which compute scales independently from storage. While this enables scaling elasticity, it introduces new problems – how do you co-locate data with compute, how do you unify data across multiple remote clouds, how do you keep storage and I/O service costs down and many more.  

Enter Alluxio, a virtual unified file system, which sits between compute and storage that allows you to realize the benefits of a  hybrid cloud architecture with the same performance and lower costs. 

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Modern Software Architectures and Data Pipelines Panel

Scala by the Bay 2016 – Throughout our four-year history, Scala and Scale By the Bay is leading the way on evangelizing and understanding modern software architectures. We have the best set of them here, including Akka, Kafka, Spark, Finagle, Lagom, and so on. How do they come together in a SMACK / MIND Stack? What are the best practices to follow and pitfalls to avoid? This panels of experienced practitioners will discuss and illuminate it all.

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