Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore
Francois Orsini

Francois Orsini
Chief Technology Officer, MZ

Website

Francois Orsini is the chief technology officer for MZ’s Satori business unit. Previously, he served as vice president of platform engineering and chief architect, bringing his expertise in building server-side architecture and implementation for a next-gen social and server platform; was a database architect and evangelist at Sun Microsystems; and worked in OLTP database systems, middleware, and real-time infrastructure development at companies like Oracle, Sybase, and Cloudscape. Francois has extensive experience working with database and infrastructure development, honing his expertise in distributed data management systems, scalability, security, resource management, HA cluster solutions, soft real-time and connectivity services. He also collaborated with Visa International and Visa USA to implement the first Visa Cash Virtual ATM for the internet and founded a VC-backed startup called Unikala in 1999. Francois holds a bachelor’s degree in civil engineering and computer sciences from the Paris Institute of Technology.

Sessions

4:00pm4:30pm Tuesday, December 5, 2017
Smart cities and urban automation
Location: 323 Level: Intermediate
One of the key application domains leveraging live data is smart cities, but success depends on the availability of generic platforms that support high throughput and ultralow latency. Arun Kejariwa and Francois Orsini offer an overview of Satori's live data platform and walk you through a country-scale case study of its implementation. Read more.
11:15am11:55am Wednesday, December 6, 2017
Machine Learning
Location: 323
Anomalies occur frequently in live data for a multitude of reasons, so detection and filtering of anomalies is of paramount importance for robust decision making. Dhruv Choudhary, Arun Kejariwal, and Francois Orsini explore the design and architecture of MZ's Satori platform and share techniques for anomaly detection on live data. Read more.