This tutorial guides users to set up a stack of Presto, Alluxio and Hive Metastore on your local server, and it demonstrates how to use Alluxio as the caching layer for Presto queries.
This tutorial describes steps to set up an EMR cluster with Alluxio as a distributed caching layer for Hive, and run sample queries to access data in S3 through Alluxio.
Learn more about Bazaarvoice’s use case leveraging Apache Spark, Hive, and Alluxio on S3. Along with how to set up Hive with Alluxio so that Hive jobs can seamlessly read from/write to S3.
EMR has become a widely used service to run big data analytics in the public cloud. But issues around slow/inconsistent EMR performance due to S3 data lakes creates challenges for organizations.
Alluxio is a data orchestration layer for the cloud that increases performance of analytic workloads running on AWS EMR using S3 as the storage.
Join us for this webinar where we will show you how to set up EMR Spark and Hive with Alluxio so jobs can seamlessly read from and write to your S3 data lake. You’ll see the performance gains with Alluxio in your EMR/S3 stack.
The ever increasing challenge to process and extract value from exploding data with AI and analytics workloads makes a memory centric architecture with disaggregated storage and compute more attractive. This decoupled architecture enables users to innovate faster and scale on-demand. Enterprises are also increasingly looking towards object stores to power their big data & machine learning workloads in a cost-effective way. However, object stores don’t provide big data compatible APIs as well as the required performance.
In this webinar, the Intel and Alluxio teams will present a proposed reference architecture using Alluxio as the in-memory accelerator for object stores to enable modern analytical workloads such as Spark, Presto, Tensorflow, and Hive. We will also present a technical overview of Alluxio.
Learn more about the practice of Alluxio in AVA deep learning platform, Ctrip big data platform, and Sogou.