When MongoDB and InfluxDB could not meet the high-performance and stringent requirements for an AIoT Livestock Monitoring app, TDengine was the carefully considered choice.
In Lyric data system, mosquitto is used as MQTT broker to collect the data from smart massage device, TDengine is used as a time-series database to process the collected data, and Grafana is used as the visualization.
From the perspective of big data monitoring, TDengine demonstrates dominant competency in terms of O&M costs, writing/reading performance, and technical support.
TDengine demonstrates excellent performance, 1: The writing speed can reach 12,000 to 13,000 records per second; 2: Storage spaces required for storing location data are only 1/7 of the former solution; 3: Querying the data from a single device by one-day interval will respond within 0.1 seconds.
TDengine serves as a solution for monitoring in the HeyGears 3D pringting system. The problems we’ve encountered in Hadoop system can be easily solved with its simple SQL-like syntax.
TDengine currently performs as a highly efficient time-series database in POIZON. It’s very easy to manage the cluster. In addition, it lowers the operation and maintenance costs a lot.
Baffled by numerous pain points, RisingStar IoT Platform chose TDengine, a high-performance time-series database, to upgrade their data processing solution.
Through comparative research on the solutions provided by several prevailing time-series databases such as IoTDB, Druid, ClickHouse, and TDengine, SF Technology finally decided to choose TDengine as a solution. Since using TDengine, impressive improvements have been achieved in the terms of stability and writing/query performance.
A stable real-time monitoring system is an absolute necessity for a robust and efficient application system. For huge monitoring data volumes, the bottleneck of the whole system’s performance is usually in the data persistence layer.
In the era of the Internet of Things (IoT), the real-time data generated by connected vehicles enhance user experiences in car rental and fleet management business, drive innovative business model like usage-based insurance, and build the foundation of autonomous driving and vehicle-to-everything (V2X) paradigm. Time series data processing platforms like TDengine are purposely built for time series data and much more cost-efficient in structured IoT data processing.