As the number of sensors and devices in the Internet of Things (IoT) increases by a CAGR of ~25%, i.e., doubles every 3 years, a critical question that arises is how to ingest, store and analyze the massive amounts of time-series data generated and transmitted. As a core developer and founder of TDengine, Jeff has a very deep understanding of what it takes.
Topics to be covered:
The standard way to process Big Data
Characteristics of time-series data
Innovative design of TDengine
Why open source TDengine?
Use Cases
Speaker Bio:
Jeff Tao (Founder and Core Developer of TDengine)
Jeff has a stellar background as a technologist and serial entrepreneur. Early in his career he conducted research and development on mobile Internet at Motorola, 3Com and other companies. This was followed by founding two successful startups. In his latest venture, in May 2017 he founded TDengine, which focuses on technologies to store, query and compute time series data in real time in a scalable and efficient way. TDengine outperforms its competitors at least 10 times faster in terms of data ingestion rate and query speed. TDengine has applications in Internet of Things, IT Infrastructure Monitoring, Finance and other industries to lower the overall operation cost by 80%. TDengine was open sourced in July 2019, and for a while was #1 in GitHub’s global trend rankings.
