In collaboration with Knorr-Bremse, the global market leader for braking systems and other systems for rail and commercial vehicles, we are developing an Azure-based solution for the use of condition data from train operations. This will enable the development as well as operation of a wide range of use cases, from descriptive and diagnostic data analysis to predictive maintenance.

Sustainable Mobility

Almost 200 years have passed since the invention of the railroad - and yet today, more than ever before, rail is the transport method of the future, whether national or international, freight or passenger. However, in order to meet the growing trend of green mobility, the highly limited capacity of the rail network must be used optimally. Digitization makes it possible to evaluate existing processes on the basis of data and thus reduce downtimes. It is precisely this exploitation of operational data that can be realized in the context of the joint project between Knorr-Bremse and grandcentrix.

In doing so, potential savings are uncovered and the basis for future developments is created.
Connected Assets
Data series per train and day

State-based monitoring,
user-centric visualizations,
advanced analytics

To reliably enable applications such as condition monitoring and predictive maintenance, a resilient infrastructure is necessary. That's why our experts relied on Terraform when setting up the Azure environment. The implementation as Infrastructure as Code leads to a reproducible and fast rollout of all components, which is not a matter of course for complex systems. In addition, operation is also simplified, as faults on multiple structures become correctable and systematic versioning of the components is possible.
The Data Science use cases are developed by Knorr-Bremse and grandcentrix directly in the cloud on the existing database. This allows the step from prototype to productive pipeline to be achieved in the shortest possible time.


The first step in any new connectivity project is a systematic investigation of data quality, in which grandcentrix provides significant support. In addition, system engineers are now enabled to visualize the raw data with various tools. This way, a first - non-code-based - approximation of the data is ensured. In addition to train operations, this is of particular interest for data from test stands to determine what happened and for what reason.


To ensure ideal scaling of performance, grandcentrix provides the solutions technological foundation on technologies such as Databricks and Apache Spark, which are ready for very large data volumes. Knorr-Bremse is thus optimally equipped for the Big Data future.

Technical Highlights

Environment for developing and continuously running Data Science applications.
Terraform modules developed in-house, e.g. enabling rapid deployment of parametrizable pipelines.
Data Lakehouse
The implemented data architecture combines the advantages of data lake and data warehouse data structures.
Apache Spark
Enables efficient and parallel processing of large amounts of data.

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