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Paco Nathan

Paco Nathan
Evil Mad Scientist, derwen.ai

Website | @pacoid

Paco Nathan is the Chief Scientist at Mesosphere in SF, and a “player/coach” who’s led innovative Data teams building large-scale apps for the past decade. He is a recognized expert in distributed systems, machine learning, predictive modeling, and cloud computing. He received his BS Math Sciences and MS Computer Science degrees from Stanford, and has 25+ years experience in the tech industry ranging from Bell Labs to early-stage start-ups.

Paco is an evangelist for the Mesos and Cascading open source projects, and is also an O’Reilly author for “Enterprise Data Workflows with Cascading”.

Sessions

SOLD OUT
Hadoop and Beyond
Room 204
Tutorial Please note: to attend, your registration must include Tutorials on Tuesday.
Florian Leibert (Mesosphere), Paco Nathan (derwen.ai), Benjamin Hindman (Apache Mesos)
Average rating: ****.
(4.40, 5 ratings)
3-Hours: Mesos is a cluster manager that provides efficient resource isolation for distributed frameworks--much like Google's "Borg" for warehouse scale computing. We'll provide hands-on experience in how to build scalable, fault-tolerant data workflows atop Mesos. We'll use Chronos to orchestrate Hadoop jobs and other data prep, then use Marathon to launch a Rails + Redis app to serve results. Read more.
Hadoop and Beyond
GA Ballroom J
Paco Nathan (derwen.ai)
Average rating: ****.
(4.00, 4 ratings)
Google "Omega" research: 80% cluster jobs are batch, 60% cluster resources go to services. Batch is simple, services are hard, mixing workloads is key to building efficient distributed apps. This talk examines case studies of Mesos workloads: ranging from Twitter (100% on prem) to Airbnb (100% cloud). How did they leverage "data center OS" building blocks for orders of magnitude gains at scale? Read more.
Office Hour
Table A
Paco Nathan (derwen.ai)
If you want to build scalable, fault-tolerant data workflows atop Apache Mesos, and master Mesos workloads, ask Paco. He’ll tell you how to use Mesos as an SDK for building distributed frameworks, and how to build enterprise data workflows with Cascading. You might get him to mention PMML, an open standard for migrating predictive models. Read more.