Paco Nathan is Chief Scientist for Mesosphere in San Francisco. He is a data scientist and a “player/coach” who's led innovative Data teams building large-scale apps for the past decade, and is also a recognized expert in Hadoop, R, cloud computing, distributed systems, machine learning, predictive analytics, and natural language processing. Paco works as an technology evangelist for both the Cascading and Mesos open source projects, and is the author of the O'Reilly book “Enterprise Data Workflows with Cascading”. He received his BS Math Sciences and MS Computer Science degrees from Stanford University, and has 25+ years technology industry experience ranging from Bell Labs to early-stage start-ups.
Cascading provides an abstraction layer API for integrating Apache Hadoop with other data frameworks -- as middleware for Big Data. It leverages functional programming to help make large-scale apps faster to build and test, and less complex to maintain. Mesos is an open source cluster scheduler API, akin to the "Borg" technology used at Google, which improves cluster utilization and multi-tenancy for high ROI apps. Twitter and other firms leverage these projects for their revenue apps at scale. This talk will introduce both technologies, show sample apps, and review use cases in industry.
When & Where
Data Science for Social Good fellowship
The Eric and Wendy Schmidt Data Science for Social Good fellowship at the University of Chicago's Computation Institute is a summer program for aspiring data scientists to work on big data projects with social impact.