Implementing AI-enabled Analytics

With traditional industries increasingly moving toward Industry 4.0, the need for better analytics is more apparent than ever. Only through modern, advanced analytics is it possible to implement predictive maintenance and data-driven decision making. Furthermore, the recent advances in AI technology have sparked interest among industry leaders who are looking for ways to make their operations more efficient.

However, traditional data historians are not equipped to handle the requirements of modern analytics, much less AI. They typically lack the ability to centralize data from multiple sites, preventing analytics tools from obtaining a global view of company operations. More importantly, they are closed systems that are difficult to integrate with cutting-edge analytics tools.

TDengine is a next generation data historian that can resolve these issues for industrial data. It is an open system that can seamlessly integrate with the latest AI tools, finally bringing the power of AI to traditional industries. At the same time, TDengine is familiar to existing users, supporting standard SQL like a relational database. TDengine provides powerful and easy data analytics capabilities as follows:

  1. Efficient aggregation between multiple data collection points: TDengine introduces the innovative concept of the supertable, which is a template for a type of data collection point based on the characteristics of time series data. By specifying filtering conditions when querying a supertable, you can efficiently aggregate data collection points of the same type, making it easier to organize and find data and eliminating the need for costly JOIN operations. In addition, TDengine allows you to add up to 128 labels to each data collection point, which you can delete and update later. TDengine provides a powerful way to slice data into cubes for multidimensional analysis.
  2. Separation of storage and compute: TDengine supports the separation of storage and compute resources. The system can start one or more query nodes as needed to increase computing resources, speed up complex queries, and reduce latency. For cloud platforms, the compute node can be a container, which can be started or stopped quickly. The separation of storage and compute takes full advantage of the elasticity of cloud platforms.
  3. Unified analysis of historical and real-time data: TDengine automatically partitions data according to time interval. As much as 10 years of data can be stored in a database or table, and there is no concept of archived data in the TDengine system. This remains true even when tiered storage is used: the only difference between querying the latest data and data from 10 years ago is the start and end times in your SQL statement.
  4. Extended functions for time series data analytics: TDengine extends standard SQL to meet the needs of of time series data analytics, providing essential functions such as cumulative summation, time weighted average, and moving average out of the box. Through time windows and interpolation, the timestamps of data from different data collection points can be aligned at fixed time intervals to facilitate further analysis. To learn more, see SQL Manual.
  5. Real-time data analytics: TDengine provides time-driven and event-driven stream processing. Not only can stream processing be performed on the data stream generated by a single data collection point, but also on the aggregated data streams of multiple collection points. Support for User Defined Functions (UDF) enables stream processing to easily provide preprocessing, transformation, and other complex compute functions. For more information about stream processing, see the documentation.
  6. Python support: TDengine provides a Python connector and supports pandas and data frames, so that data analysts who love Python can easily use various Python libraries to do their time series data analytics.
  7. Other convenient means of data access and analysis: TDengine provides a command line interface (CLI) with which you can run ad hoc queries and import or export data. TDengine also provides R and Matlab connectors and seamless integration with Grafana and Google Data Studio.
  8. Easy integration with AI analytics tools: TDengine is an open system that provides standard interfaces, making it easy to integrate and test the latest AI analytics software with your data.

In typical Industrial IoT scenarios, TDengine can be used as a time-series data warehouse; it is no longer necessary to import time series data into a special data warehouse or data lake for processing and analysis. And its open ecosystem with seamless integration with various AI tools brings industrial data analytics into the modern age. All in all, TDengine greatly reduces the cost of industrial data processing while opening the door to advanced, AI-enabled analytics.