What’s New in Alluxio 2.7: Enhanced Scalability, Stability and Major Improvements in AI/ML Training Efficiency

With this release, Alluxio has strengthened its position as a de-facto data unification and acceleration solution in data analytics and machine learning pipelines. The solution is optimized to support Spark, Presto, Tensorflow, and PyTorch, and is available on multiple cloud platforms such as AWS, GCP, and Azure Cloud, and also on Kubernetes in private data centers or public clouds.

Advancing GPU Analytics with RAPIDS Accelerator for Spark and Alluxio

RAPIDS is a set of open source libraries enabling GPU aware scheduling and memory representation for analytics and AI. Spark 3.0 uses RAPIDS for GPU computing to accelerate various jobs including SQL and DataFrame. With compute acceleration from massive parallelism on GPUs, there is a need for accelerating data access and this is what Alluxio enables for compute in any cloud. In this talk, you will learn how to use Alluxio and Spark with RAPIDS Accelerator on NVIDIA GPUs without any application changes.

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Accelerate Analytics and ML in the Hybrid Cloud Era

Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.

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