Alluxio for Hybrid Cloud | HDFS and AWS S3 demo

Alluxio Community Office Hour *

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.

New features and performance optimization of open source big data storage system Alluxio

Chengdu Meetup *

This technical salon will focus on big data, storage, database and Alluxio application practice, and invite Tencent technical experts and industry technical experts to share the basic principles of Alluxio system, big data system architecture, database application operation and maintenance, AI computer. Themes such as visual technology and landing practice bring rich practical content and experience exchange.

Spark+AI Summit SF 2019

SAIS 2019 *

What’s Spark+AI Summit? It’s the world’s largest conference that is focused on Apache Spark – Alluxio’s older cousin open source project from the same lab (UC Berkeley’s AMPLab – now RISElab).

Open Source Global Tech Leadership Meetup

Global Tech Leadership Conference *

Open source software always plays critical role in software development. From Linux kernel to TensorFlow, it drives a lot of awesome projects which created trend and led direction of technology.
We are pleased to have several experts, Reynold Xin, Dongxu Huang, Qing Han, Bin Fan, Amelia Wong, etc. who will share the technology and stories on their successful open source project.

Tachyon: Past, Present and Future

Bay Area Meetup *

Tachyon is a memory-centric fault-tolerant distributed storage system, which enables reliable file sharing at memory-speed. It originated from AMPLab, UC Berkeley in 2012, the same lab produced Apache Mesos and Apache Spark. Soon later, it became an open source project and is deployed at many companies. Since then, Tachyon has attracted more than 200 contributors from over 50 institutions. In 2015, company Tachyon Nexus was founded to further accelerate the development of Tachyon. In this talk, we will review Tachyon’s new features, deployments, and developments in 2015, and look into 2016.

Production Spark and Tachyon Use Cases

Spark Summit Europe *

During the past several years, Spark has significantly changed the landscape of big data computing. It improves performance of various applications dramatically. However, in certain Spark use cases, the bottleneck is in the I/O stack. In this talk, we will introduce Tachyon, a distributed memory-centric storage system. In addition, we will talk about several production use cases where Tachyon further improves Spark applications’ performance by orders of magnitude.

Deep learning architecture using Tachyon and Spark & Tachyon New Features

Bay Area Meetup *

In the presentation, we will explore several potential industry use cases enabled by the new features. One-click cluster deployment enables users to experiment and prototype with Tachyon on AWS, launching not only Tachyon but also the computation framework and storage system of their choice. Mounting of multiple under storage systems and transparent naming enables more exciting use cases for Tachyon users.

Data Driven #46 (a FirstMark Event)

Data Driven NYC *

Check out our new blog post: “Internet of Things: Are We There Yet? (The 2016 IoT Landscape)”: The Internet of Things is all about data!

Fast big data analytics and machine learning using Alluxio and Spark in Baidu

Strata+Hadoop World San Jose *

A few months ago, Baidu deployed Alluxio to accelerate its big data analytics workload. Bin Fan and Haojun Wang explain why Baidu chose Alluxio, as well as the details of how they achieved a 30x speedup with Alluxio in their production environment with hundreds of machines. Based on the success of the big data analytics engine, Baidu is currently expanding the Alluxio and Spark infrastructure to accelerate other applications, such as machine learning.