The latest Alluxio meetups, webinars, conferences and more
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.
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.
This presentation focuses on how Alluxio helps the big data analytics stack to be cloud-native. The trending Cloud object storage systems provide more cost-effective and scalable storage solutions but also different semantics and performance implications compared to HDFS. Applications like Spark or Presto will not benefit from the node-level locality or cross-job caching when retrieving data from the cloud object storage. Deploying Alluxio to access cloud solves these problems because data will be retrieved and cached in Alluxio instead of the underlying cloud or object storage repeatedly.
Cloud object storage systems provide different semantics and performance implications compared to HDFS. Applications like Presto cannot benefit from the node-level locality or cross-job caching when reading from the cloud. Deploying Alluxio with Presto to access cloud solves these problems because data will be retrieved and cached in Alluxio instead of the underlying cloud or object storage repeatedly. Bin will present the architecture to combine Presto with Alluxio with use cases from major internet companies like JD.com and NetEase.com, and their lessons learned to operate this architecture at scale.
The future is the era of data, and the abstraction of efficient management, storage, and access to data is undoubtedly the cornerstone of this era. Open source distributed virtual data system Alluxio is dedicated to providing simple and efficient data abstraction, convenient data sharing and high-speed I/O for big data, machine learning, and artificial intelligence, while keeping applications and data persistent and providing rich Storage system selection. After several years of development, Alluxio was developed from a prototype of a research project involving only a few Ph.D. students and researchers in the AMPLab at the University of California, Berkeley, to more than 800 code contributors (Alluxio 1.8 release data), and deployed in Tencent. Baidu, JD, Two-Sigma, Barclays Bank and other hundreds of Chinese and foreign industry leaders in the production environment, become an important part of the data platform and data infrastructure.
A10 Big Data Application Summit
The future is the era of data, and the abstraction of efficient management, storage, and access to data is undoubtedly the cornerstone of this era. Open source distributed virtual data system Alluxio is dedicated to providing simple and efficient data abstraction, convenient data sharing and high-speed I/O for big data, machine learning, and artificial intelligence, while keeping applications and data persistent and providing rich Storage system selection.
After several years of development, Alluxio was developed from a prototype of a research project involving only a few Ph.D. students and researchers in the AMPLab at the University of California, Berkeley, to more than 800 code contributors (Alluxio 1.8 release data), and deployed in Tencent. Baidu, JD, Two-Sigma, Barclays Bank and other hundreds of Chinese and foreign industry leaders in the production environment, become an important part of the data platform and data infrastructure.
Speeding Up Machine Learning in the Cloud with Alluxio
Alluxio has run in JD.com’s production environment on 100 nodes for six months. Mao Baolong, Yiran Wu, and Yupeng Fu explain how JD.com uses Alluxio to provide support for ad hoc and real-time stream computing, using Alluxio-compatible HDFS URLs and Alluxio as a pluggable optimization component. To give just one example, one framework, JDPresto, has seen a 10x performance improvement on average. This work has also extended Alluxio and enhanced the syncing between Alluxio and HDFS for consistency.