Accelerate Analytics and ML in the Hybrid Cloud Era

Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.

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Alluxio Architecture and Scaling Performance

In this talk, I will introduce the high-level architecture of the current system, and present the various components of Alluxio. Also, I will discuss some of the main challenges of large scale Alluxio deployments, and the lessons we learned from those environments. This talk will detail some of the major scalability improvements added in the past several months, and how users can benefit from the changes.

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Introducing the Hub for Data Orchestration

We introduce Data Orchestration Hub, a management service that makes it easy to build an analytics or machine learning platform on data sources across regions to unify data lakes. Easy to use wizards connect compute engines, such as Presto or Spark, to data sources across data centers or from a public cloud to a private data center. In this session, you will witness the use of “The Hub” to connect a compute cluster in the cloud with data sources on-premises using Alluxio. This new service allows you to build a hybrid cloud on your own, without the expertise needed to manage or configure Alluxio.

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The Future of Computing is Distributed

Distributed applications are not new. The first distributed applications were developed over 50 years ago with the arrival of computer networks, such as ARPANET. Since then, developers have leveraged distributed systems to scale out applications and services, including large-scale simulations, web serving, and big data processing. However, until recently, distributed applications have been the exception, rather than the norm. However, this is changing quickly.

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