Product
IoT Data Hub
An open and expandable data platform for companies that want to make machine data quickly usable and later expand it in a controlled manner for analysis, applications and AI.
Contact usTypical triggers
When companies need the IoT Data Hub
The IoT Data Hub is useful when machine data needs to be quickly usable without having to set up several separate data islands for dashboards, analysis, applications and AI.
Typical triggers are distributed machine data, high integration effort between OT and IT or the desire to quickly create initial visibility with just a few data sources.
The platform forms a common foundation for MDE, dashboards, analysis functions, proprietary applications and integrated AI functions.
Connectivity
Record and connect machine data
The platform connects controllers, sensors, gateways and store floor systems via specific industrial protocols and reusable integration patterns.
- Concrete protocols such as Siemens S7, OPC UA, MQTT, Modbus
- Connection of PLCs, sensors, historian systems and specialist sources
- Reusable templates for machine, line and location rollouts
Data management
Model, manage and provide data
Raw data is transferred into a consistent data model and structured with asset management and semantic description for dashboards, analysis and AI.
- Asset management for locations, areas, labels and machine types
- Consistent structuring and provision of industrial data via a common data model
- Semantic metadata model for data streams, measured values, measurement units, data types and semantics
Live data analysis and historical data analysis
On the same platform basis, current data streams, history and AI functions can be shared without additional data storage.
Evaluate operational signals, rules and current statuses
Pipelines, rules and events process ongoing machine data directly and make current statuses usable for the store floor, control center and technical monitoring.
- Streaming pipelines for ongoing machine data and events
- Charts and dashboards for current key figures and statuses
- Basis for alarms, triggers and operational reactions
Combining trends, history and AI-supported evaluations
Auf derselben Datenbasis lassen sich historische Daten, Trends und Fachkontexte mit Dashboards und AI Notebooks für tiefergehende Auswertungen verbinden, während AI Pipelines laufende Datenströme und betreibbare Analyselogik ergänzen.
- Evaluate time series, history and context data together
- AI Notebooks für Exploration, Modellierung und gespeicherte Daten
- AI Pipelines für laufende Datenströme und betreibbare Analyselogik
Application layer
Build your own applications and modules
Dashboards, analysis applications, workflows or your own extensions can be operated on the same database without having to introduce a second platform.
- APIs and SDKs for company-specific applications
- Roles, rights and governance for productive environments
- Open expandability for analytics, dashboard and AI modules
Architectural building blocks
The platform combines data connection, data model, usage layer and operational aspects in a coherent stack.
Data connection
Siemens S7, OPC UA, MQTT, REST, BeckhoffSpecific industry protocols and standard interfaces for controllers, gateways, historian systems and specialist sources.
Connector templatesReusable configurations for machine types, lines and cross-location rollouts.
Edge component in the OT networkData acquisition and pre-processing close to the system with controlled communication to the central or cloud instance.
Processing and persistence
Streaming and event processingLive data for rules, monitoring, pipelines and further processing.
Time series and history managementBasis for trends, comparisons, dashboards and historical analyses.
Pipelines and pre-processingTransformation, enrichment and transfer of data for operational and analytical workflows.
Data model and use
Asset management and data structureLocations, areas, labels, machine types and signals in a consistent structure for professionally usable industrial data.
Semantic metadata modelDescribe and manage data streams, measured values, measurement units, data types and semantics in a comprehensible manner.
APIs, dashboards and data explorationProvision for engineering, production, quality and own applications on the same platform basis.
Operation and expansion
On-prem, cloud and hybridAdaptable to existing infrastructure specifications and distributed deployment scenarios.
Roles, rights, governanceControl of productive use across teams, locations, clients and responsibilities.
Open source basis and expandabilityExpansion with own modules, services, dashboards, analysis functions and AI components.
Operating models and deployment
The IoT Data Hub can be operated as an integrated solution for a quick start or as a distributed architecture with governance and security requirements.
Data acquisition and pre-processing directly in the OT network
An edge component can be operated close to machines, controllers or cells, collect data locally and synchronize it with the central or cloud instance in a controlled manner.
- Suitable for networks with limited connectivity or segmentation
- Pre-processing, buffering and secure transfer to central instances
- Clean separation between OT-related operations and central governance
Integrated MDE and analysis platform for a quick start
For small and medium-sized companies, the IoT Data Hub can be used as a comparatively simple, integrated solution for machine data acquisition, dashboards and initial analyses.
- Less integration effort thanks to a common platform
- Quick start with connectivity, charts and dashboards
- Gradual expansion in the direction of analysis and AI possible
Governance, security and distributed deployment for large organizations
For larger companies, the platform supports cross-site architectures with roles, responsibilities, distributed instances and controlled provision of data and applications.
- Governance across locations, divisions and teams
- Security and operating models for centralized, hybrid and distributed scenarios
- Common technical basis for local and central data rooms
Quick start, later expansion if required
The IoT Data Hub can be started with just a few data sources. Additional locations, applications or AI functions can be added later on the same basis.
01Start
Connect initial data sources quickly
At the beginning, the relevant machines, control systems or sensor sources are connected so that the first live data can be displayed without a long lead time.
02Visibility
Structuring data and creating initial views
Assets, signals and specialist contexts are then structured and initial dashboards, charts or analytical views are provided on the same database.
03Benefit
Use initial analyses and applications productively
The first analyses, rules or application-specific modules can be used productively and evaluated in operation after just a short time.
04Expansion
Expand step by step if required
If additional lines, locations, specialist applications or AI modules are added, the platform can be expanded in a controlled manner on the same basis.
AI notebooks and AI pipelines
Integrated AI functions in the IoT Data Hub
AI Notebooks and AI Pipelines are part of the IoT Data Hub and use the same data, integration and operating basis as connectivity, dashboards and applications. This means there are no separate data islands between platform operation, analysis and AI.
AI functions on the same basis
The AI functions are based directly on the existing data model, the connectors and the operating mechanisms of the IoT Data Hub.
AI Notebooks
AI Notebooks sind als integrierte Arbeitsumgebung Teil des IoT Data Hub und helfen dabei, gespeicherte Maschinendaten per Sprache und erklärbarem Python-Code sicher auszuwerten und als Visualisierungen, Tabellen oder weiterführende Analysen aufzubereiten.
AI Pipelines
AI Pipelines sind in die Plattform integriert und definieren Analysen, Überwachungen und Datenharmonisierungen auf laufenden Maschinendaten. Die erzeugte Logik wird auf derselben Daten- und Betriebsbasis ausführbar und betreibbar.
Demo
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Arrange a demo to get to know the platform, analysis and AI functions based on your questions.