Effective data analytics development is an important capability for modern enterprises. However, managing the data pipelines feeding these data analytics applications gives rise to new challenges, which are especially felt in the automotive domain. This thesis investigates challenges for data pipeline management and suitable data analysis approaches for automotive event data in a case study with the Daimler Truck AG. A literature review identifies challenges for data pipeline management in general and in the automotive context. These challenges are compared to challenges identified through semistructured interviews with data pipeline stakeholders at Daimler Truck AG. Approaches for handling the identified challenges are also discussed. Furthermore, a prototype of a concrete data analysis on automotive event data is designed and developed. The prototype’s performance is experimentally evaluated on an existing data set of automotive event data. This evaluation indicates a need for improvement of the approach. Based on the initial prototype, alternative analysis approaches are investigated and scope for future work is outlined.