This talk will go over a generic example of stateful coordination service moving from Zookeeper to Raft.
Alluxio meetups, conferences, events and more
The latest Alluxio meetups, webinars, conferences and more
Alluxio, as a data orchestration layer provides the physical data independence, for Presto to interact with the data more efficiently. In addition to caching for IO acceleration, Alluxio also provides a catalog service to abstract the metadata in the Hive Metastore, and transformations to expose the data in compute-optimized way. In this talk, we describe some of the challenges of using Presto with Hive, and introduce Alluxio data orchestration for solving those challenges.
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.
Building distributed systems is no small feat. Software testing is just one of many critical practices that engineers who build these systems need to utilize to ensure the quality and usability of their software. For distributed systems, scaling out testing frameworks to ensure that enterprises who run our in highly distributed environments is a complicated (and expensive task!)
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.
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.
In the on-prem days, one key performance optimization for Apache Hadoop or Apache Spark workloads is to run tasks on nodes with local HDFS data. However, while adoption of the Cloud & Kubernetes makes scaling compute workloads exceptionally easy, HDFS is often not an option. Effectively accessing data from cloud-native storage services like AWS S3 or even on-premises HDFS becomes harder as data locality is lost.
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.
The rise of compute intensive workloads and the adoption of the cloud has driven organizations to adopt a decoupled architecture for modern workloads – one in which compute scales independently from storage. While this enables scaling elasticity, it introduces new problems – how do you co-locate data with compute, how do you unify data across multiple remote clouds, how do you keep storage and I/O service costs down and many more.
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.