Not only do we have our usual top notch speakers talking about their first hand experiences across the subjects of real world deep learning, data and data systems engineering and building scalable engineering culture, but all proceeds are going to the outstanding charity, TechFugees. This one day event will bring new perspective across these three critical areas of modern day software engineering all the while helping refugees gain access to the knowledge economy.
Julien le Dem
We are witnessing a new revolution in data, the age of automation of decisions. In this presentation, Cloudera cofounder and CTO Amr Awadallah will explain the historic importance of this wave, the common patterns with which it manifests itself in organizations today, then conclude by talking about the foundational capabilities required to enable it.
This talk describes the process of improving the quality of business metrics reporting at Pinterest. This process consisted of specifying core metrics, understanding the end-to-end architecture, executing a cross-functional improvement program, and creating a novel reporting tool. The talk extracts five tips for successfully improving metrics quality from this process: know your stakeholders; define core metrics; prioritize quality; fund test implementation, and measure progress. The talk focuses on the innovation that led to a new kind of metrics quality measurement report, which PInterest has been using to track our progress throughout the year.
Technical overview of challenges and trade offs in the design and use of protobuffers within Google: This talk will discuss code size, and CPU efficiency and how different languages and platforms lead to different designs. I touch upon using Arena's new upcoming features that we work on to release and compare with competitors like Cap'n Proto, flatbuffers and thrift.
A project to evaluate alternative database technologies as a partial replacement for Hive. The motivation for the project was that Hive is slow and inefficient and it was felt that we could improve the productivity of our analysts with a technology with better response time while also saving money on hardware. We describe the evaluation process and the technology that was picked, Presto. We also describe some of the practical work that was done in order to deploy Presto on a 200-node production cluster, including frameworks for monitoring, testing, upgrading, failover, and end-user training
Criteo Palo Alto
325 Lytton Street, Suite 200
Palo Alto CS 94301