Building distributed systems is no small feat. Software testing is just one of many critical practices that engineers who build these systems need to utilize to ensure the quality and usability of their software. For distributed systems, scaling out testing frameworks to ensure that enterprises who run our in highly distributed environments is a complicated (and expensive task!)
In this session, we share common design patterns AWS customers are applying as part of their Data and AI journey.
Vitaliy and Dipti dive into how DBS Bank built a modern big data analytics stack, leveraging an object store as persistent storage even for data-intensive workloads, and how it uses Alluxio to orchestrate data locality and data access for Spark workloads.
This online meetup shows why and how we solve some challenging technical issues, improve the speed, and reduce the costs of our AWS EMR Hadoop & Presto -Backend with Alluxio to an awesome level.
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.
Alluxio is a proud sponsor and exhibitor at the AWS Summit in New York. If you weren’t able to attend, here are the highlights
This article aims to provide a different approach to help connect and make distributed files systems like HDFS or cloud storage systems look like a local file system to data processing frameworks: the Alluxio POSIX API. To explain the approach better, we used the TensorFlow + Alluxio + AWS S3 stack as an example.
This whitepaper details how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud. We will show an example of what it might look like to run on-demand Starburst Presto, Spark, and Hive with Alluxio in the public cloud using on-prem HDFS.
The paper also includes a real world case study on a leading hedge fund based in New York City, who deployed large clusters of Google Compute Engine VMs with Spark and Alluxio using on-prem HDFS as the underlying storage tier.
The AWS EMR service has made it easy for enterprises to bring up a full-featured analytical stack in the cloud that elastically scales based on demand.
The EMR service along with S3 provides a robust yet flexible platform in the cloud with the click of a few buttons, compared to the highly complex and rigid deployment approach required for on-premise Hadoop Data platforms. However, because data on AWS is typically stored in S3, an object store, you lose some of the key benefits of compute frameworks like Apache Spark and Presto that were designed for distributed file systems like HDFS.
In this white paper, we’ll share some of the challenges that arise because of the impedance mismatch between HDFS and S3, the expectations of analytics workloads of the object store, and how Alluxio with EMR addresses them.