Manufacturing Insights
Turnkey analytics for your production: plant analysis, fault analysis, machine status, process analysis, and product lifecycle data expand the IoT Data Hub.
Explore Manufacturing Insights →
IoT Data Hub
Put machine data to work. Get results faster with AI.
A single, scalable software solution for machine data acquisition, processing, and visualization. AI pipelines and AI notebooks reduce development effort.
- From the lead developers of Apache StreamPipes
- Edge and On-Premise
- Dashboards and analytics included
Less integration effort. Achieve more with data.
The IoT Data Hub has everything you need to collect, organize, and make machine data available for analysis. This integrated architecture enables a seamless user experience and a consistent security model. This saves time and effort during setup and reduces the complexity of your application landscape.
- 01
Every new line becomes an integration project
Machines and systems provide data in various formats. Connections and mappings are constantly being reestablished.
- 02
Too much time goes into integrating separate software solutions
Data collection, storage, visualization, and analysis are often handled using separate tools. This reduces flexibility and increases maintenance efforts.
- 03
Every custom analysis starts with coding
Loading, cleaning, calculating, and visualizing data: It often takes a long time to answer a business question.
- 04
OT requirements and IT infrastructure create complexity.
Segmented networks, data sovereignty, and existing infrastructure call for flexible operating models rather than a purely cloud-based solution.
Connect → Process → Analyze
Connect, process, and analyze in one application
Every step stays in one platform. Use data directly and tackle new tasks faster with the integrated AI capabilities.
- 01
Connect
Connect controllers, sensors, gateways, and existing systems via industrial protocols with a single mouse click.
S7 · OPC UA · MQTT · Modbus · REST
- 02
Process
Use pipelines to standardize real-time data and assign it to machines and locations. AI pipelines help you create the necessary processing logic more quickly.
Pipelines · AI Pipelines · Machine Context
- 03
Analyze
Track metrics in dashboards, analyze trends with charts, or create custom analyses with AI notebooks: The choice is yours!
Dashboards · Charts · AI Notebooks
Machine data collection in minutes.
We support the standard protocols and interfaces for machines and control systems.
OPC UA
Connect structured machine data.
MQTT
Receive data streams from connected devices.
Siemens S7
Use signals from existing controllers.
Modbus
Capture data from industrial devices.
IO-Link
Connect sensor data through IO-Link masters.
REST
Connect existing systems through APIs.
From data source to analysis in one environment
Connect a machine, process its signals using pipelines, and use the data in dashboards, charts, or AI notebooks. You don't need to set up a separate analytics system for analysis.
How does your machine data get from connection to analysis?
Connectivity
Record and connect machine data
With the adapter library, you can integrate data in minutes—including preprocessing and user-friendly browsing.
Siemens S7 · OPC UA · MQTT · Modbus · REST
View Connectors
Product in Use
One machine model for many applications.
Assign signals to a machine and link data sources and applications to that asset. This way, you can find all your resources in one place and keep track of them.
- Organize machines and data sources by technical category
- Organization by Locations, Facilities, and Processes
- Comprehensive rights management for assets and resources
Open source done right.
Behind Bytefabrik are the founders and lead developers of Apache StreamPipes, a world-leading open-source solution for IoT data management with over 60,000 downloads. The IoT Data Hub complements the open platform with additional features and excellent support. Manufacturing Insights extends the platform with preconfigured production analytics.
IoT Data Hub
One platform from connectivity to AI-assisted analysis
Apache StreamPipes, including pipelines, dashboards, and charts, supplemented by AI pipelines, AI notebooks, and commercial support.
Compare editions and featuresOperating models and deployment
The IoT Data Hub can be operated locally at a facility, as a shared on-site platform, or as a distributed architecture with centralized governance.
Data Collection Directly on the OT Network
An edge component can be operated close to machines, control systems, or cells, collect data locally, and synchronize it with a central instance in a controlled manner.
- Suitable for networks with limited connectivity or segmentation
- Pre-processing, buffering and secure transfer to central instances
- Clear separation between operations close to the OT and the central IT server for office access
One platform for lines and production areas
On a local server at the customer's site, the IoT Data Hub collects all signals and makes them available for analysis.
- Less integration effort thanks to a common platform
- Reusable Templates for Lines and Machine Types
- A Cost-Effective Entry Point Through Reduced Complexity
Global scalability
For larger companies, the platform supports the extensive features required for a secure global rollout.
- Governance across locations, divisions and teams
- Security and operating models for centralized, hybrid and distributed scenarios
- Comprehensive permissions system and audit logs for enterprise requirements
Frequently Asked Questions Before Getting Started
Will the IoT Data Hub fit into our environment?
Does production data have to leave the factory?
No. The IoT Data Hub can be operated locally and close to the edge. Centralized or cloud-based instances can be added where infrastructure and governance permit.
Do existing systems need to be replaced?
No. The IoT Data Hub can integrate controllers, gateways, historians, and business applications. Pipelines, storage, dashboards, and analytics are also available directly in the platform.
When should you choose the IoT Data Hub over Apache StreamPipes?
Apache StreamPipes provides an open platform with connectivity, pipelines, Data Explorer, and live dashboards. The IoT Data Hub adds AI pipelines and AI notebooks for faster data preparation and customized analytics, as well as commercial support. The product comparison shows the feature sets of the different editions.
What does "open source" mean at Bytefabrik?
We initiated Apache StreamPipes. This open-source project is the technological foundation of the IoT Data Hub. Bytefabrik builds additional capabilities on it and supports companies in production use.
From workshop to pilot in a few days to weeks
We start with a workshop and connect the first data sources straight away. Within a few days to weeks, we onboard a production line in the IoT Data Hub and implement a suitable use case to demonstrate its value to your team. The scope and availability of data determine the timeline.
- 01Workshop
Connect the first data sources in a workshop
In a joint workshop, we select a production line and a suitable use case. We connect the first data sources directly to the IoT Data Hub.
- 02Setup
Connect the production line and configure the system
We connect the required machines and systems, configure the IoT Data Hub, and set up data collection, storage, and processing for the pilot.
- 03Implementation
Prepare the data and implement the use case
We structure the signals, map them to machines and processes, and implement the selected use case, such as a dashboard, automated data preparation, or custom analysis.
- 04Pilot review
Evaluate the value with your team
Your team tries out the use case with data from the production line. Together, we assess its value against the agreed objectives and discuss the next steps.
30-minute product demo
See how AI shortens the path to analysis
In 30 minutes, we'll show you how machine data is fed into the platform, how AI pipelines support data processing, and how dashboards and AI notebooks turn that data into actionable insights. No preparation needed.