IoT Data Hub
The IoT Data Hub combines machine connectivity, data processing, storage, charts, and dashboards into a single platform. It is based on Apache StreamPipes, which Bytefabrik helped launch. AI pipelines and AI notebooks complement the platform with AI-powered data preparation and customized analyses.
Question and objective
How can existing machine data be turned into usable information without having to re-export and reprocess the data for every analysis? The goal is a seamless process from the source to a reusable analysis.
How to Work at Bytefabrik
| Step | Result |
|---|---|
| Connecting to | An adapter provides a data stream containing defined fields. |
| Pipelines process, filter, calculate, or structure events. | |
| Organize | Saved data records and resources are assigned a traceable reference to the asset. |
| Visualization | Charts answer specific questions; dashboards consolidate them for the team. |
| 's analysis queries and AI notebooks enable in-depth investigations. |
A data stream is, first and foremost, a continuous flow of events. Only once storage has been configured can the events be queried historically. For comprehensive manufacturing analyses, Manufacturing Insights extends the hub with corresponding modules.
Architecture at a Glance
Best Practices for the First Use Case
Start with a machine, a data source, and a testable question—for example, the temperature trend during a known production phase. Monitor a sample event, save the necessary data, and start by creating a chart. Only then should you add additional sources and processing.
Bytefabrik guides you through the selection and implementation process: from the workshop with initial data sources, through configuration, to evaluating the benefits with your team.
What You Should Keep in Mind
The available adapters and processing elements depend on the installed extensions. Therefore, the procedures described here do not replace a review of the specific installation. StreamPipes serves as the platform foundation; Bytefabrik-specific AI functions are described on their own pages.
The Apache StreamPipes documentation provides additional information about the platform foundation.