Alluxio is a leading data orchestration platform that offers a compute agnostic, storage agnostic, and cloud agnostic solution for big data and machine learning applications. Aunalytics is a data platform company delivering Insights-as-a-Service to answer enterprise and mid-sized companies’ most important IT and business questions.
This whitepaper details how to evaluate Alluxio’s data orchestration platform as a distributed cache for Apache Spark in a public cloud or on-premises. We discuss best practices and benchmarking results with a combination of standard industry benchmarking suites, such as TPC-DS and HiBench, on cloud storage.
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.
We’re pleased to announce the general availability of Alluxio Data Orchestration Hub, your single pane of glass to orchestrate data for analytics and AI. The data ecosystem is complex with the separation of storage and compute across data centers and cloud providers. With this release we’ve made great strides towards simplifying data access and management across multiple environments.
In this talk, we describe the architecture to migrate analytics workloads incrementally to any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) directly on on-prem data without copying the data to cloud storage.
In this talk, we will describe how we have solved an issue with large S3 API costs incurred by Presto under several usage concurrency levels by implementing Alluxio as a data orchestration layer between S3 and Presto. Also, we will show the results of an experiment with estimating the per-query S3 API costs using the TPC-DS dataset.
Join us for this tech talk where we will show you how Alluxio can help burst your private computing environment to Google Cloud, minimizing costs and I/O overhead. Alluxio coupled with Google’s open source data and analytics processing engine, Dataproc, enables zero-copy burst for faster query performance in the cloud so you can take advantage of resources that are not local to your data, without the need for managing the copying or syncing of that data.
A collaboration of Alibaba, Alluxio, and Nanjing University in tackling the problems of Deep Learning model training in the cloud. Our goal was to reduce the cost and complexity of data access for Deep Learning training in a hybrid environment, which resulted in over 40% reduction in training time and cost.
This article presents the collaborative work of Alibaba, Alluxio, and Nanjing University in tackling the problem of Artificial Intelligence and Deep Learning model training in the cloud. We adopted a hybrid solution with a data orchestration layer that connects private data centers to cloud platforms in a containerized environment. Various performance bottlenecks are analyzed with detailed optimizations of each component in the architecture.