Data sovereignty
If production data, integration logic and AI functions are to remain under our own control in the long term, we need open technology, controllable operating models and clear interfaces instead of lock-ins.
To the IIoT platformOpen, operable, expandable
Data sovereignty is not created by a single product feature, but by the interplay of an open platform core, controlled operation and clearly defined expansion points.
These properties help to reduce dependencies and keep industrial data platforms manageable in the long term.
With Apache StreamPipes, we rely on an open platform core that Bytefabrik initiated and continues to develop significantly to this day. Data flows, extension points and the operating model remain transparent as a result.
The platform, storage and integration logic can be operated in your own data center or in controlled cloud environments.
Custom adapters, data models, pipelines and front ends can be extended in a controlled manner via APIs, client libraries and SDKs.
Roles, authorizations and documented data flows help to implement compliance, audits and internal guidelines in a technically clean way.
Open source for industrial data streams
Bytefabrik initiated Apache StreamPipes, continues to develop the open core to this day and supplements it for productive industrial environments. As a result, data models, integration logic and extensions remain transparent and under your own control.
Foundation
Apache StreamPipes provides connectors, pipelines and analysis modules for industrial data teams. Because the platform is open and actively co-developed by Bytefabrik, architecture decisions, data flows and extension points remain traceable.
Deployment
The platform can be operated on-premise or in controlled cloud environments and connected to existing security, network and governance requirements. This means that responsibilities, data storage and system boundaries can be controlled internally.
Models generate logic, not raw data copies
For many companies, data sovereignty is crucial when it comes to the use of AI. That's why our AI modules are designed in such a way that language models do not work directly with real data, but primarily support the generation of executable logic.
The execution of data access and processing remains in your platform. Models mainly help to generate code or configurations for open interfaces and client libraries.
Language models do not generate direct access to production data for us. They support the creation of analysis code, which is then executed in your environment via open interfaces and client libraries.
Even productively operated AI modules do not access real data via the language model. Data processing runs in your platform, while models primarily support the generation and maintenance of execution logic.
The AI functions are built on the same open data and integration basis. This means that governance, access rights and operating limits can also be kept consistent for AI-supported applications.
Technical control instead of platform dependency
Open source and on-premise operation help to keep integration logic, data models and analysis functions traceable in the long term. This makes audits easier, reduces dependencies on individual providers and simplifies the gradual expansion of the platform.
This is particularly relevant in production environments with sensitive process, quality or product data: Data access, execution paths and AI support can be bundled in an architecture that fits the company's security and governance requirements.
Demo
Arrange a demo to get to know the platform, analysis and AI functions based on your questions.