Initializing a Sustainable Data Mesh Transformation
summary
Deutsche Bahn runs a 10‑week pilot that produces a tunnel image data product collecting inspectors’ photos (picked 3–5 of ~50 inspectors) and a central sharing platform; implementation uses a single Kubernetes cluster hosting messaging and web servers to regain deployment speed, which works but reveals infra bottlenecks. Concrete outcomes: need for automated data quality monitoring, standardized observability and lineage/versioning, and automated role‑based access tied to corporate identity. First built artifact is a company‑wide data quality monitoring service as the initial technical governance tool.
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chapters (7)
- 0:00Start
- 3:44Data Mesh for Deutsche Bahn: Why it would be a great fit for a heterogeneous group of companies such as Deutsche Bahn.
- 5:43Building a Data Mesh in a world of centralized data: How to start a revolution?
- 7:42Our lean approach to Data Mesh: Start with a simple data use case to learn, how Data Mesh fits into Deutsche Bahn's data ecosystem.
- 11:04Learning form our first data product implementation: Example Recommendation (1/2)
- 12:26Observing our pilot implementation: The reality of building Deutsche Bahn's first data product-need to build your own infrastructure for autonomy.
- 13:58Learning form our first data product implementation: Example Recommendation (2/2)
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