Reducing server resources by 85% in a logistics scenario
By moving to TDengine, Cross Express Group was able to reduce the complexity of their system, reduce number of servers from 21 to 3, and reduce daily storage increments from 352G to 4G.
Migrating from MongoDB to TDengine significantly reduces TCO
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.
Building a data collection platform for smart devices
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.
Improving portfolio management system performance with a time-series database
Yuanbo Yi (Hithink RoyalFlush)
From the perspective of big data monitoring, TDengine demonstrates dominant competency in terms of O&M costs, writing/reading performance, and technical support.
China Mobile IoT reduces storage resources by over 80% for connected car data
Chao Xue (China Mobile IoT)
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.
Simplified, efficient monitoring and storage of industrial equipment metrics
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.
Enabling millisecond-level response time for a massive time-series data set
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.
Migrating from OpenTSDB for a simplified IoT platform
Zhongyuan Ai (RisingStar IoT)
Baffled by numerous pain points, RisingStar IoT Platform chose TDengine, a high-performance time-series database, to upgrade their data processing solution.
SF Technology upgrades big data monitoring platform
Fei Yin (SF Technology)
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.