Streaming & Real-Time Data
Instead of daily batch runs, relevant metrics are available within seconds.
Streaming platforms such as Kafka or Confluent process events from production, logistics or the web in real time instead of daily or hourly cycles. This enables immediate reaction to faults, bottlenecks or customer actions, while batch processing stays in place where real time adds no value.
- Faster response to faults and bottlenecks.
- More current metrics for operational decisions.
- Early warning instead of after-the-fact analysis.
reaction time
higher data frequency
to production use
unplanned downtime
Calculated from the time between event and response before and after introducing streaming.
- 01
Use case selection
We identify scenarios where real time genuinely adds value.
- 02
Architecture design
We plan event sources, brokers and processing logic.
- 03
Pipeline implementation
We implement streaming pipelines with error handling.
- 04
Consumer integration
We connect dashboards, alerts and downstream systems.
- 05
Operations & scaling
We monitor throughput, latency and scaling needs.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Machine events
Sensor and status events directly from equipment.
Order events
Real-time orders from shop or ERP systems.
Logistics data
Tracking and shipment data from the supply chain.
Application logs
Event streams from web and backend applications.
Sensor data
Measurement series from IoT devices and gateways.
Fault alerts
Alarm and fault messages from control systems.
Production management
Immediate visibility into faults and bottlenecks.
Logistics
Current shipment status instead of daily reports.
IT operations
Early warnings instead of reactive troubleshooting.
Executive management
Current metrics instead of outdated daily reports.
Faster response to faults and bottlenecks.
More current metrics for operational decisions.
Early warning instead of after-the-fact analysis.
Better planning through continuous status updates.
A basis for real-time automation and alerting.
With a solid data foundation this use case gets faster, cheaper and far more stable.
An existing data platform simplifies connecting streams.
Central storage allows streaming and batch to coexist.
Existing monitoring catches throughput issues early.
Standardized schemas make onboarding new event sources easier.
Lakehouse architecture
Streams land directly in the lakehouse's bronze layer.
Predictive maintenance
Real-time data improves anomaly detection.
Data quality & observability
Streaming metrics feed directly into quality monitoring.
Lakehouse Architecture
A central lakehouse replaces the patchwork of data warehouse, data lake and Excel exports.
Data Integration from ERP & MES
Automatically merge ERP and MES data instead of exporting and reconciling manually.
Data Modeling & Semantic Layer
A shared semantic layer ensures 'revenue' means the same thing everywhere.