Today’s analytics workloads demand real-time access to expansive amounts of data. This session demonstrates how Alluxio’s data orchestration platform, running on Intel Optane persistent memory, accelerates access to this data and uncovers its valuable business insights faster.
This is an open source community conference focused on the key data engineering challenges and solutions around building cloud-native data and AI platforms using latest technologies such as Alluxio, Apache Spark, Apache Airflow, Presto, Tensorflow, and Kubernetes.
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Join us for this webinar where Alex Ma of Alluxio, an open source data orchestration platform, will discuss how a data orchestration approach offers a solution for connecting traditional on-prem data centers with the cloud, data centers with other data centers, and clouds with other clouds. With Alluxio’s “zero-copy” burst solution, companies can bridge remote data centers with computing frameworks in other locations, enabling them to offload compute and leverage the flexibility, scalability, and power of the cloud for their remote data.
Adit Madan and Parviz Peiravi offer an overview of the Alluxio data orchestration layer that provides a unified data access layer for hybrid and multi cloud deployments, leveraging Intel® Optane™ Persistent Memory for higher performance caching at reduced cost. The data access layer enables distributed compute engines like Presto, TensorFlow, and PyTorch to transparently access data from various storage systems (including S3, HDFS, and Azure) while actively leveraging a multi-tier cache to accelerate data access.
International Data Corporation (IDC) reported that the global datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes by 20251. This trend becomes more and more complicated with the variety and velocity of data growth, and it continuously changes the ways data is collected, stored, processed, and analyzed. New analytics solutions, including machine learning, deep learning, and artificial intelligence (AI), and new architectures and tools are being developed to extract and deliver value from the huge datasphere.
Testing Methodology Decision support workload is a typical workload that models multiple aspects of a decision support system, including queries and data maintenance. We selected 54 queries that represent a typical SQL query behavior in Hadoop for the test. The tests include three different configurations: Without Alluxio, Alluxio on PMem and Alluxio on DRAM. The … Continued
This article describes how Alluxio accelerates the training of deep learning models in a hybrid cloud environment with Intel’s Analytics Zoo open source platform, powered by oneAPI. Details on the new architecture and workflow, as well as Alluxio’s performance benefits and benchmarks results will be discussed.
Learn more about Alluxio and Intel’s joint solution, which allows companies to unify on-premises and cloud data silos into a single, cloud-based data layer, increasing data accessibility and elasticity while virtually eliminating the need for copies—for less complexity, lower costs, and greater speed and agility.