Past, Present and Future of Alluxio [Chinese]

Nanjing Big Data Meetup *

The Alluxio project has greatly improved system performance, Scalability and user experience, and added a series of new features, including scalable tiered storage, transparent UFS data reading and writing, unified namespaces, and more. Easy to use with Alluxio. At the same time, the Alluxio ecosystem has expanded to support different storage systems and computing frameworks. Alluxio now supports a variety of storage systems, including Amazon S3, Google Cloud Storage, Gluster, Ceph, HDFS, NFS and OpenStack Swift, as well as big data processing frameworks such as Spark, MapReduce, Flink and more. These integrations allow Alluxio to manage and help with more and more complex data.

Past, Present and Future of Alluxio [Chinese]

Shanghai Meetup *

The Alluxio project has greatly improved system performance, Scalability and user experience, and added a series of new features, including scalable tiered storage, transparent UFS data reading and writing, unified namespaces, and more. Easy to use with Alluxio. At the same time, the Alluxio ecosystem has expanded to support different storage systems and computing frameworks. Alluxio now supports a variety of storage systems, including Amazon S3, Google Cloud Storage, Gluster, Ceph, HDFS, NFS and OpenStack Swift, as well as big data processing frameworks such as Spark, MapReduce, Flink and more. These integrations allow Alluxio to manage and help with more and more complex data.

Alluxio (Formerly Tachyon): The Journey thus Far and the Road Ahead

Vault Linux Storage and Filesystems Conference *

Haoyuan Li explores Alluxio’s goal of making its product accessible to an even wider set of users, through a focus on security, new language bindings, and further increased stability. Haoyuan also covers some new APIs Alluxio is working on to allow applications to access data more efficiently and manage data across different under storage systems.

Best Practices for Using Alluxio with Spark

Strata Data Conference New York 2017 *

Haoyuan Li and Cheng Chang explain how Alluxio makes Spark more effective in both on-premises and public cloud deployments and share production deployments of Alluxio and Spark working together. Along the way, they discuss best practices for using Alluxio with Spark, including with RDDs and DataFrames.

Interactive Big Data Analytics with the Presto + Alluxio stack for the Cloud

Alluxio Tech Talk *

In this tech talk, we will introduce the Starburst Presto, Alluxio, and Cloud object store stack for building a highly-concurrent and low-latency analytics platform. This stack provides a strong solution to run fast SQL across multiple storage systems including HDFS, S3 and others in public cloud, hybrid cloud and multi cloud environments.

Achieving Separation of Compute and Storage in a Cloud World

Alluxio Tech Talk *

In this tech talk, we will discuss why leading enterprises are adopting hybrid cloud architectures with compute and storage disaggregated, the new challenges that this new paradigm introduces, and the unified data solution Alluxio provides for hybrid environments.

Testing Distributed Systems in the Big Data Ecosystem at 1000+ node Scale

Testing distributed systems at scale is typically a costly yet necessary process. At Alluxio we take testing very seriously as organizations across the world rely on our technology, therefore, a problem we want to solve is how to test at scale without breaking the bank. In this blog we are going to show how the maintainers of the Alluxio open source project build and test our system at scale cost-effectively using public cloud infrastructure. We test with the most popular frameworks, such as Spark and Hive, and pervasive storage systems, such as HDFS and S3. Using Amazon AWS EC2, we are able to test 1000+ worker clusters, at a cost of about $16 per hour.

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Testing Distributed Systems at 1000+ node Scale for the Cost of a Large Pizza, and yes, on AWS!

Testing distributed systems at scale is typically a costly yet necessary process. At Alluxio we take testing very seriously as organizations across the world rely on our technology, therefore, a problem we want to solve is how to test at scale without breaking the bank. In this blog we are going to show how the maintainers of the Alluxio open source project build and test our system at scale cost-effectively using public cloud infrastructure. We test with the most popular frameworks, such as Spark and Hive, and pervasive storage systems, such as HDFS and S3. Using Amazon AWS EC2, we are able to test 1000+ worker clusters, at a cost of about $16 per hour.