AWS + Alluxio: Data Orchestration for Analytics & AI in the cloud

Many organizations have taken advantage of the scalability and cost-savings of cloud computing as well as cloud storage services to meet their data-powered workload demands. In addition, as data is increasingly siloed and lives everywhere, there’s a need for data orchestration to bring the needed data closer to compute. With Alluxio’s data orchestration platform, bring back data locality for your compute with in-memory & tiered data access.

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Accelerating Analytical Workloads for Public & Hybrid Clouds

New York Meetup *

In this meetup, Dipti and HY will present a new approach to hybrid analytical workloads using Alluxio, an open source data orchestration layer, which sits between compute and storage layer. Applications like Apache Spark or TensorFlow can then seamlessly access multiple disparate data sources with consistent performance using data locality and abstraction that the data orchestration tier brings.

Community Office Hour: Running Spark & Alluxio in Kubernetes

The data orchestration layer bridging the gap between data locality with improved performance and data accessibility for analytics workloads in Kubernetes, and enables portability across storage providers.
An overview of Alluxio and the cloud use case with Spark in Kubernetes. Learn how to set up Alluxio and Spark to run in Kubernetes.

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Building Fast SQL Analytics with Presto, Alluxio, and S3

Alluxio Community Office Hour *

Learn how to set up Presto with Alluxio such that Presto jobs can seamlessly read from and write to S3.
Compare the performance between Presto on S3 with Presto and Alluxio on S3.

Hybrid Environments for Data Analytics is a Possibility

As the data ecosystem becomes massively complex and more and more disaggregated, data analysts and end users have trouble adapting and working with hybrid environments. The proliferation of compute applications along with storage mediums leads to a hybrid model that we are just not accustomed to.
With this disaggregated system data engineers now come across a multitude of problems that they must overcome in order to get meaningful insights.