This article goes through a simple example to illustrate how Structured Data Management available in the latest Alluxio 2.1.0 release to help SQL and structured data workloads.
Category: Developer and Engineering
This article introduces Structured Data Management (Developer Preview) available in the latest Alluxio 2.1.0 release, a new effort to provide further benefits to SQL and structured data workloads using Alluxio.
Kubernetes, Alluxio and the disaggregated analytics stack TL;DR: First the news – Alluxio support for K8s Helm charts now available! K8s is a certified environment for Alluxio. Now the take away- Alluxio brings back data locality for the disaggregated analytics stack in K8s. How? Read on. There’s no arguing the rise of containers in real-world … Continued
In the previous tutorial ”Getting Started with Spark Caching using Alluxio in 5 Minutes”, we demonstrated how to get started with Spark and Alluxio. To share more thoughts and experiments on how Alluxio enhances Spark workloads, this article focuses on how Alluxio helps to optimize the memory utilization of Spark applications. For users who are … Continued
This is a guest blog by Ashwin Sinha with an original blog source. This blog introduces Wormhole— open source Dockerized solution for deploying Presto & Alluxio clusters for blazing fast analytics on file system (we use S3, GCS, OSS). When it comes to analytics, generally people are hands-on in writing SQL queries and love to analyse data which resides in a warehouse (e.g. MySQL database). But as data grows, these … Continued
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.
For today’s blog post I interviewed Bin Fan, Founding Engineer and VP of Open Source at Alluxio. Bin is the PMC maintainer of the Alluxio open source project. Prior to Alluxio, he worked for Google on the next-generation storage infrastructure.
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.
This article describes my lessons from a previous project which moved a data pipeline originally running on a Hadoop cluster managed by my team, to AWS using EMR and S3. The goal was to leverage the elasticity of EMR to offload the operational work, as well as make S3 a data lake where different teams can easily share data across projects.