Many organizations are leveraging EMR to run big data analytics on public cloud. However, reading and writing data to S3 directly can result in slow and inconsistent performance. Alluxio is a data orchestration layer for the cloud, and in this use case it caches data for S3, ensuring high and predictable performance as well as reduced network traffic.
Slides from our latest talks
This talk will overview two projects at Electronic Arts (EA) that address the mismatch by data orchestration: One project automatically generates configurations for all components in a large monitoring system, which reduces the daily average number of alerts from ~1000 to ~20. The other project introduces Alluxio for caching and unifying address space across ETL and analytics workloads, which substantially simplifies architecture, improves performance, and reduces ops overheads.
Alluxio 2.0 release was the biggest update since the birth of the project “Tachyon” from UC Berkley’s AmpLab. Gathering feedback from our Open Source Community and enterprise users, Alluxio 2.0 expands the system in three major directions including improving the operability of the system, having more advanced data management, as well as re-architecting the system to be able to scale to 1 billion + file. The system is now cloud native on AWS, Google Cloud, and allow users to enable native deployment with K8s. The new advanced data management enables data migration and replication from diff storage systems.
This webinar will describe the concept and internal mechanism using the stack of Spark+Alluxio in Kubernetes to enhance data locality even when the storage service is outside or remote.
Join us for this tech talk where we’ll 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.
This talk will guide the audience on how Alluxio can greatly simplify the data preparation phase in with remote and possibly multiple data sources. We will share the lessons and benchmark from Bill Zhao an engineer led in Apple when building a Machine Learning platform using Tensorflow, NFS, DC/OS and Alluxio.
Users deploy Alluxio in a wide range of use cases from analytics to AI platforms, for Alluxio’s unified access to data and transparent caching for acceleration. However, many frameworks are SQL engines, like Presto, Apache Spark SQL, or Apache Hive, and consume data structured as tables of rows and columns.
This office hour describes the concept and dataflow with respect to using the stack of Spark/Alluxio in Kubernetes with enhanced data locality even the storage service is outside or remote.
This talk describes a stack of open-source projects to serve high-concurrent and low-latency SQL queries using Presto with Alluxio on big data in the cloud. Deploying Alluxio as a data orchestration layer to access cloud storage object storage (e.g., AWS S3), this architecture greatly enhances the data locality of Presto with distributed and cross-query caching, thus avoids reading the same data repeatedly from the cloud storage.