Accelerating Spark Workloads in an Apache Mesos Environment with Alluxio

MesosCon North America 2017 *

Using Alluxio, an open-source memory speed virtual distributed storage system, deployed on Mesos enables connecting any compute framework, such as Apache Spark, to storage systems via a unified namespace. Alluxio enables applications to interact with any data at memory speed. Alluxio can eliminate the pains of ETL and data duplication, and enable new workloads across all data. Adit will discuss the architecture of Mesos, Spark and Alluxio to achieve an optimal architecture for enterprises.

Introduction to Alluxio 2.0 Preview

Alluxio Tech Talk *

Alluxio 2.0 is the most ambitious platform upgrade since the inception of Alluxio with greatly expanded capabilities to empower users to run analytics and AI workloads on private, public or hybrid cloud infrastructures leveraging valuable data wherever it might be stored. This preview release, now available for download, includes many advancements that will allow users to push the limits of their data-workloads in the cloud.

Announcing Alluxio 2.0 Preview – enabling hyper-scale data workloads in the cloud

We are thrilled and excited to announce the availability of Alluxio 2.0 Preview Release Рthe largest open source release with the most new features and improvements since the creation of the project. It is now available for download.
While Alluxio already enabled data locality and data accessibility for many big data workloads in the cloud, there was still innovation needed in key areas.

Achieving Separation of Compute and Storage in a Cloud World

Alluxio Tech Talk *

In this tech talk, we will discuss why leading enterprises are adopting hybrid cloud architectures with compute and storage disaggregated, the new challenges that this new paradigm introduces, and the unified data solution Alluxio provides for hybrid environments.

Accelerating On-Demand Data Analytics with Alluxio

This is an excerpt from the Accelerating On-Demand Data Analytics with Alluxio whitepaper, which includes a detailed implementation guide in addition to this high level overview.
In the Big Data world, it is often the case that only a subset of the total data is relevant for answering the question at hand. As a result, the total cost of ownership for long running clusters for analytics is high while utilization is low, especially when adopting an architecture of co-locating compute and storage.