A stable system for real-time IT infrastructure monitoring is an absolute necessity for a robust and efficient application system. This document describes how to create such a system using TDengine, Telegraf, and Grafana.
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.
Besides being a time-series database, TDengine provides caching, message queuing, and stream computing functionalities. It is a full stack for time-series data processing, so you it is not necessary to integrate with other big data tools.
TDengine is designed and optimized for IoT. It reduces the computing and storage resources significantly, and it reduce the complexity of development and operation significantly too. Also, it has many other cool features, plus, it is open sourced
General big data platform can handle all types of data, but it faces technology challenges when it handles the huge amount of IoT data. It’s not efficient, not cost effective either. We can utilize the IoT data characteristics to roll out a highly efficient solution