Advanced Features

This chapter mainly introduces the advanced features of TDengine, such as data subscription, caching, stream computing, edge-cloud collaboration, and data access.

📄️ Data Subscription

To meet the needs of applications to obtain data written to TDengine in real-time, or to process data in the order of event arrival, TDengine provides data subscription and consumption interfaces similar to those of message queue products. In many scenarios, by adopting TDengine's time-series big data platform, there is no need to integrate additional message queue products, thus simplifying application design and reducing maintenance costs.

📄️ Caching

In the big data applications of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), the value of real-time data often far exceeds that of historical data. Enterprises not only need data processing systems to have efficient real-time writing capabilities but also need to quickly obtain the latest status of devices or perform real-time calculations and analyses on the latest data. Whether it's monitoring the status of industrial equipment, tracking vehicle locations in the Internet of Vehicles, or real-time readings of smart meters, current values are indispensable core data in business operations. These data are directly related to production safety, operational efficiency, and user experience.

📄️ Stream Processing

In the processing of time-series data, it is often necessary to clean and preprocess the raw data before using a time-series database for long-term storage. Moreover, it is common to use the original time-series data to generate new time-series data through calculations. In traditional time-series data solutions, it is often necessary to deploy systems like Kafka, Flink, etc., for stream processing. However, the complexity of stream processing systems brings high development and operational costs.

📄️ Edge–Cloud Orchestration

Why Edge-Cloud Collaboration is Needed

🗃️ Data Connectors

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