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Unified Data Access In Virtual Reality

In a recent blog, we discussed the ideation, design and new features in Alluxio 2.0 preview. Today we are thrilled to announce another new revolutionary project that the Alluxio engineering team has been hard at work on for the past year – the Alluxio Virtual Reality (VR) client.

Founder Blog | Alluxio Chapter 2.0

In the early 2000s, big data was born, and technology companies were racing to create the next-gen compute frameworks or storage systems geared towards the requirements brought about by big data. By the time I was a first year Ph.D. student at UC Berkeley’s AMPLab in 2011, numerous advances in big data related technologies such as Apache Spark was emerging. Through working on Apache Spark and getting exposed to cutting-edge technologies it became clear that sharing data among data driven applications with different compute frameworks and moving data across storage systems would become the bottleneck for any organization that wants to extract value from their data. To solve these challenges, I created Alluxio (formerly Tachyon), which for the lack of a defined category I called it a virtualized distributed file system in my original thesis.

Getting Started with Spark Caching using Alluxio in 5 Minutes

Apache Spark has brought significant innovation to Big Data computing, but its results are even more extraordinary when paired with Alluxio. Alluxio, provides Spark with a reliable data sharing layer, enabling Spark to excel at performing application logic while Alluxio handles storage. Bazaarvoice uses the combination of Spark and Alluxio to provide a real time big data platform that has the ability to not only handle the intake of 1.5 billion page views during peak events like Black Friday, but also provide real time analytics against it (read more). At this scale, the gain in speed is an enabler for new workloads. We’ve established a clean and simple way to integrate Alluxio and Spark.

Announcing Alluxio 2.0 Preview – enabling hyper-scale data workloads in the cloud

We are thrilled and excited to announce the availability of Alluxio 2.0 Preview Release – the largest open source release with the most new features and improvements since the creation of the project. It is now available for download.
While Alluxio already enabled data locality and data accessibility for many big data workloads in the cloud, there was still innovation needed in key areas.

Top 5 Performance Tuning Tips for Presto caching using Alluxio

Presto is an open source distributed SQL engine widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Alluxio is an open-source distributed file system that provides a unified data access layer at in-memory speed. The combination of Presto and Alluxio is getting more popular in many companies like JD, NetEase to leverage Alluxio as distributed caching tier on top of slow or remote storage for the hot data to query, avoiding reading data repeatedly from the cloud. In general, Presto doesn’t include a distributed caching tier and Alluxio enables caching of files and objects that the Presto query engine needs.

One Click to Benchmark Spark + Alluxio + S3 Stack with TPC-DS queries on AWS

The Alluxio sandbox is the easiest way to test drive the popular data analytics stack of Spark, Alluxio, and S3 deployed in a multi-node cluster in a public cloud environment. The sandbox cluster is fully configured and ready for users to run applications ranging from the hello-world example to the TPC-DS benchmark suite. Don’t take our word for it; kick off the benchmark yourself to see the performance benefits of running Spark jobs that interface through Alluxio on S3 compared to running Spark jobs directly on S3. It is extremely easy to request and launch a sandbox cluster as a playground for 24 hours at no cost to you.

Alluxio Developer Tip: Why am I seeing the error “User yarn is not configured for any impersonation. impersonationUser: foo?”

Impersonation is simply the ability for one user to act on behalf of another user. For example, say user ‘yarn’ has the credentials to connect to a service, but user ‘foo’ does not. Therefore, user ‘foo’ would never be able to access the service. However, user ‘yarn’ can access the service and impersonate (act on behalf of) user ‘foo’, allowing access to user ‘foo’. Therefore, impersonation enables one user to access a service on behalf of another user.
The impersonation feature defines how users can act on behalf of other users. Therefore, it is important to know who the users are.