We will introduce the key new features and enhancements such as: Support for hyper-scale data workloads, Machine learning and deep learning workloads, and Better storage abstraction.
Tag: cloud workloads
Twitter SF is hosting 2019’s half yearly RocksDB Meetup with speakers from Twitter, Facebook and the community on July 11th.
Join us June 24 in Menlo Park for our next meetup! We’ll have 3 valuable talks, a delicious BBQ dinner and amazing summertime-themed raffle prizes! This free event is sponsored by GridGain Systems and Oracle.
[Talk 1] A “how-to” presentation for building a real-time alerting, analytics and reporting system (at scale). With Denis Magda, vice president of the Apache Ignite PMC and director of product management at GridGain Systems. And Viktor Gamov, developer advocate at Confluent.
[Talk 2] Using In-Memory technology for real time analytics. With Andy Rivenes is a Product Manager at Oracle for Database In-Memory.
[Talk 3] Feeding data to the Kubernetes beast: bringing data locality to your containerized big data workloads. With Bin Fan, founding engineer of Alluxio, Inc. and PMC member of Alluxio open source project.
The latest advances in container orchestration by Kubernetes bring cost savings and flexibility to compute workloads in public or hybrid cloud environments. On the other hand, it introduces new challenges such as how to move data to compute efficiently, how to unify data across multiple or remote clouds, how to co-locate data with compute and many more. Alluxio approaches these problems in a new way. It helps elastic compute workloads realize the true benefits of the cloud, while bringing data locality and data accessibility to workloads orchestrated by Kubernetes
In this talk, we discuss how Alluxio can be deployed and used with a Spark data processing pipeline in the cloud. We show how pipeline stages can share data with Alluxio memory for improved performance benefits, and how Alluxio can improves completion times and reduces performance variability for Spark pipelines in the cloud.
Using Alluxio, an open-source memory speed virtual distributed storage system, deployed on Mesos enables connecting any compute framework, such as Apache Spark, to storage systems via a unified namespace. Alluxio enables applications to interact with any data at memory speed. Alluxio can eliminate the pains of ETL and data duplication, and enable new workloads across all data. Adit will discuss the architecture of Mesos, Spark and Alluxio to achieve an optimal architecture for enterprises.
In this webinar, we will discuss:
Why leading enterprises are adopting hybrid cloud architectures with compute and storage disaggregated, The new challenges that this new paradigm introduces, An introduction to Alluxio and the unified data solution it provides for hybrid environments
Alluxio 2.0 is the most ambitious platform upgrade since the inception of Alluxio with greatly expanded capabilities to empower users to run analytics and AI workloads on private, public or hybrid cloud infrastructures leveraging valuable data wherever it might be stored. This preview release, now available for download, includes many advancements that will allow users to push the limits of their data-workloads in the cloud.