Abstract: 

Today, everything is online – meters, cars, elevators, assembly lines, and even bicycles are connected to the Internet. And all of these items are emitting a relentless stream of metrics and events. With the advent of IoT and the cloud, the volume of time-series data has begun growing exponentially in an unprecedented way. The massive size of time-series data sets is a major challenge for general database management systems like relational and NoSQL databases. Purpose-built time-series databases, on the other hand, are optimized to handle the special characteristics of time-series data. This means that time-series databases are much more efficient in terms of ingestion rate, query latency, and data compression. In addition, time-series databases include special analytic functions and data management features so that you can develop applications more easily.

Bio: 

Jeff Tao is the founder and CEO of TDengine. He has a background as a technologist and serial entrepreneur, having previously conducted research and development on mobile Internet at Motorola and 3Com and established two successful tech startups. Foreseeing the explosive growth of time-series data generated by machines and sensors now taking place, he founded TDengine in May 2017 to develop a high-performance time-series database purpose-built for modern IoT and IIoT businesses.