Data Mining in IoT: From Sensors to Insights

In a typical enterprise use case, you always start from something small to evaluate the technology and the solution you would like to implement, a so-called “Proof Of Concept” (POC). This very first step is fundamental to understanding technology’s potential and limits, checking the project's feasibility, and estimating the possible Return on Investment (ROI).

This is exactly what we did in the use-case of a people counting solution for a university. This first project phase aimed to identify how the solution's architecture should look and what kind of data insights are relevant to provide.

How to Connect Azure IoT Hub and CrateDB Cloud to Ingest IoT Sensor Data

This article will describe how to launch a CrateDB cluster on Azure, connect it to Azure IoT Hub, and test it by ingesting simulated sensor data using an Azure IoT Solution Accelerator.

Step #1: Simulating Sensor Data

It’s necessary to first understand the type of data your IoT application will produce in order to accurately mimic this information for testing purposes. Smart factories, for example, will have myriad sensors that collect data in a variety of structures. CrateDB makes it possible to model different data structures in a single table through the use of dynamic objects, which can be queried to an arbitrary depth (this is not a recommended practice for production, but is helpful within the simple confines of this demonstration).