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.
Learn more about Alluxio, a virtual unified file system and data orchestration layer for big data and machine learning workloads in the cloud.
As the amount of data being collected and analyzed by Enterprises continues to grow unabated, more attention is being placed on managing the cost of storing the data relative to performance. Hadoop provides a scalable and fast way of storing and analyzing data, however, the cost of storing data in Hadoop is typically higher compared to alternative technologies like Object Stores.
The cloud is rapidly becoming ubiquitous, with continued adoption focused on the flexibility and cost benefits of a utility infrastructure model. Enterprises are increasingly taking a “data first” view of infra- structure, which demands a new way of thinking in a world in which data is stored and accessed from multiple locations and providers. Performance and interoperability challenges, however, can present obstacles to cloud adoption and complicate data management. Techniques such as the use of data silos, ETL processes and multiple data copies, which are commonly employed to accommodate cloud limitations, often tend to offset the expected benefits of cloud infrastructure. Alluxio offers a new way to enhance the benefits of cloud infra- structure without the performance limitations or interoperability challenges resulting from accessing disparate data sources in multiple, often remote, locations.
Alluxio is an open source data orchestration platform for large-scale analytics and AI/ML applications. It provides a unified namespace for accessing data distributed across private data centers and clouds, and also provides advanced caching to address issues with data locality, performance, and data egress costs. Alluxio provides the data accessibility, locality, and elasticity needed to reduce complexity and improve the performance for analytics and AI/ML workloads.
Alluxio was created because we saw a need for innovation at the data layer rising from the growing complexity of connecting multiple compute frameworks to an ever-expanding mix of storage systems and formats. Our approach uses a memory-centric architecture that abstracts files and objects in underlying persistent storage systems and provides a shared data access layer for compute applications.
Alluxio is not a persistent storage system. Instead, Alluxio serves as a data access layer, residing between any persistent storage system (such as Amazon S3, Microsoft Azure Object Store, Apache HDFS or OpenStack Swift) and computation frameworks (such as Apache Spark, Presto or Hadoop MapReduce). This whitepaper provides a technical overview of the Alluxio architecture and describes the data flow for common read and write scenarios.
Recently, Qunar deployed Alluxio with Spark in production and found that Alluxio enables Spark streaming jobs to run 15x to 300x faster. In their case study, they described how Alluxio improved their system architecture, and mentioned that some existing Spark jobs would slow down or would never finish because they would run out of memory. After using Alluxio, those jobs were able to finish, because the data could be stored in Alluxio, instead of within Spark.
In this blog, we show by saving RDDs in Alluxio, Alluxio can keep larger data sets in-memory for faster Spark applications, as well as enable sharing of RDDs across separate Spark applications.
The world is entering the data revolution era. Along with the latest advancements of the Internet, Artificial Intelligence (AI), mobile devices, autonomous driving, and Internet of Things (IoT), the amount of data we are generating, collecting, storing, managing, and analyzing is growing exponentially. To store and process these data has exposed tremendous challenges and opportunities. … Continued
In the age of growing datasets and increased computing power, deep learning has become a popular technique for AI. Deep learning models continue to improve their performance across a variety of domains, with access to more and more data, and the processing power to train larger neural networks. This rise of deep learning advances the state-of-the-art for AI, but also exposes some challenges for the access to data and storage systems. In this article, we further describe the storage challenges for deep learning workloads and how Alluxio can help to solve them.