Time-series data is now everywhere and increasingly used to power core applications. It also creates a number of technical challenges: to ingest high volumes of data; to ask complex, queries for recent and historical time intervals; to perform time-centric analysis and data management. And this data doesn't exist in isolation: entries are often joined against other relational data to ask key business questions.
In this talk, I offer an overview of how we re-engineered TimescaleDB, a new open-source database designed for time series workloads, engineered up as a plugin to PostgreSQL, in order to simplify time-series application development. Unlike most time-series newcomers, TimescaleDB supports full SQL while achieving fast ingest and complex queries. This enables developers to avoid today's polyglot architectures and their corresponding operational and application complexity.
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, and has led to commercial products and deployed systems reaching millions of users daily. Honors include a Presidential Early Career Award (PECASE), SIGCOMM Test of Time Award, Sloan Fellowship, DARPA CSSG membership, and multiple award publications.