Percona Live 2017 Open Source Database Conference

April 24 - 27, 2017

Santa Clara, California

Michael J. Freedman

Michael J. Freedman's picture

Michael J. Freedman

Co-founder/CTO, Timescale - Professor of Computer Science, TimescaleDB


Michael J. Freedman is a Professor in the Computer Science Department at Princeton University, as well as the co-founder and CTO of Timescale, building an open-source database that scales out SQL for time-series data. His work broadly focuses on distributed systems, networking, and security. Freedman developed and operated several self-managing systems -- including CoralCDN, a decentralized content distribution network, and DONAR, a server resolution system that powered the FCC's Consumer Broadband Test -- which reached millions of users daily. Freedman's work on IP geolocation and intelligence led him to co-found Illuminics Systems, which was acquired by Quova (now part of Neustar) in 2006. His work on programmable enterprise networking (Ethane) helped form the basis for the OpenFlow / software-defined networking (SDN) architecture. Freedman is also a technical advisor to Blockstack, building decentralized services leveraging the blockchain. Honors include a Presidential Early Career Award for Scientists and Engineers (PECASE, given by President Obama), Sloan Fellowship, NSF CAREER Award, Office of Naval Research Young Investigator Award, DARPA Computer Science Study Group membership, and multiple award publications. Prior to joining Princeton in 2007, he received his Ph.D. in computer science from NYU's Courant Institute and his S.B. and M.Eng. degrees from MIT.


25 April - 9:50 AM - 10:35 AM at Main Scenario
At Percona we see time series databases as a trend of 2017, hence the idea of having quick 5-minute lightning talks from projects that we think are stellar, followed by a quick panel on time series data stores in general. We discuss the "why a new time series data store" question, why these are ideal solutions, users, as well as where they see the future of this space.
26 April - 2:00 PM - 2:50 PM at Room 204
Today everything is instrumented, generating more and more time-series data streams that need to be monitored and analyzed. When it comes to storing this data, many developers often start with some well-trusted system like PostgreSQL, but when their data hits a certain scale, give up its query power and ecosystem by migrating to some NoSQL or other "modern" time-series architecture.