Enterprises are increasingly looking towards object stores to power their big data & machine learning workloads in a cost-effective way. The combination of SwiftStack and Alluxio together, enables users to seamlessly move towards a disaggregated architecture.
Alluxio can help data scientists and data engineers interact with different storage systems in a hybrid cloud environment. Using Alluxio as a data access layer for Big Data and Machine Learning applications, data processing pipelines can improve efficiency without explicit data ETL steps and the resulting data duplication across storage systems.
Wenbo Zhao (Two Sigma) and Bin Fan (Alluxio) will be presenting on how Two Sigma uses Alluxio to make data-intensive compute independent of the storage beneath.
Tags: apache spark, case study, compute storage separation, hybrid cloud, meetup, presto
Using intermediate APIs means developers can learn just one framework and still access features offered by different technologies. It means writing job logic only once and being able to test it easily on a new underlying service with no effort. Not only is modularity a win for users but it means creators of execution frameworks and storage systems can focus on performance and capability without having to worry about API maintenance.
The goal is to make Alluxio accessible to an even wider set of users through a focus on security, new language bindings, and further increased stability. In addition, the team is working on new APIs to allow applications to access data more efficiently and manage data across different under storage systems.
The goal is to make Alluxio accessible to an even wider set of users through a focus on security, new language bindings, and further increased stability. In addition, the team is working on new APIs to allow applications to access data more efficiently and manage data across different under storage systems.
Alluxio is the first memory-speed virtual distributed storage system in the world. It unifies the interface between the various computing frameworks and under storages. Data access can be several magnitude faster because of Alluxio’s memory-centric architecture. In addition, Alluxio’s tiered storage, unified namespace, flexible file API, web UI, and command-line tools increase the usability in different application scenarios.
Qunar has been running Alluxio in production for over a year. Lei Xu explores how stream processing on Alluxio has led to a 16x performance improvement on average and 300x improvement at service peak time on workloads at Qunar.
Enterprises typically store large amounts of data in existing storage systems, which are often separate from big data analytics systems. Therefore, importing petabytes of data into a big data analytics system takes a long time with large overheads and high costs. Even worse, transferring large amounts of data results in data silos and unnecessary duplication, which creates serious data management problems.
In this talk, we briefly introduce Alluxio, present several ways how Alluxio can help Spark be more effective, show benchmark results with Spark RDDs and DataFrames, and describe production deployments both Alluxio and Spark working together. In the meantime, we will provide live demos for some of the use cases.