All cars support reading diagnostic data from their control units via the On-Board Diagnostics II protocol. For companies with large vehicle fleets it may be valuable to analyze this diagnostic data, but large vehicle fleets produce large amounts of data. In this thesis we investigated whether the time series database TimescaleDB is suitable for storing such data. In order to investigate this we tested and evaluated its insertion speed, query execution time and compression ratio. The results show that TimescaleDB is able to insert over 200 000 rows of data per second. They also show that the compression algorithm can speed up query execution by up to 134.5 times and reach a compression ratio of 9.1. Considering these results we conclude that TimescaleDB is a suitable choice for storing diagnostic data, but not necessarily the most suitable.