How to increase IoT data platform performance for edge devices?

#IoT #Storage #Time-Series #Data #Embedded #systems
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Abstract:

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 store, analyze and take quick actions on the massive amounts of time-series data for real-time applications for oil and gas, manufacturing, health, weather, seismic, security, and other industries.

TDengine is developed specifically to address this need and can run on Edge devices with 16MB of memory. It supports ARM32, ARM64  Lots of developers in China even play TDengine on Raspberry. Compared with InfluxDB, TimeScaleDB and other TSDBs, this is our big competitive advantage.

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

 


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  • Date: 20 Apr 2022
  • Time: 12:00 PM to 01:00 PM
  • All times are (UTC-07:00) Pacific Time (US & Canada)
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  Speakers

Jeff Tao Jeff Tao of TDengine

Biography:

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.

Address:United States




Agenda

12:00 - 12:05 PM    CTSoc Introduction

12:05 - 12:45 PM   Main Speaker

Topics to be covered:

  1. The standard way to process Big Data

  2. Characteristics of time-series data

  3. Innovative design of TDengine

  4. Why open source TDengine?

  5. Use Cases

12:45 -   1:00 PM   Q&A