Model IoT Data Before You Store It: Device, Measurement, Timestamp

When an IoT data point is stored in a database, it is important to consider more than just the value itself. Before selecting a schema, connector, or query pattern, the origin of the data, what it measures, and when it was recorded should be determined. This article uses Apache IoTDB terminology to illustrate this modeling process.

There are three key elements to consider:

1. Device: The real-world object that generated the measurement. This could be a physical device, measurement apparatus, or collection of sensors. An engineer should be able to identify the source of a signal change, not just the generic message.

2. Timeseries: The physical quantity being measured, such as temperature, voltage, current, speed, vibration, etc. A single device can produce multiple timeseries, each with its own unit, frequency, data type, and purpose.

3. Data point: The actual measurement value recorded at a specific timestamp. This answer the question of "what was observed and when?"

Before defining a schema, a modeling worksheet can be used to answer these questions for each signal. This exposes any gaps in understanding early on. Collection frequency should also be recorded as part of the signal's context, as different signals produce different amounts of temporal detail.

It is important to distinguish between the time the measurement was taken and the time the data is received by the system. This information should be explicitly stated in the schema.

Apache IoTDB supports both tree and table models, but the core concepts remain the same. Before choosing a specific model, the data and the questions that need to be answered should be determined. A pre-ingestion checklist can be used to ensure that every measurement can be traced to a specific device, measured quantity, timestamp, unit, type, and collection frequency. This makes future queries, trend comparisons, and troubleshooting more interpretable.