Sharing a large amount of data between many mobile devices can lead to scalability problems. One of these scalability problems is that the data becomes too large to store on mobile devices and that many updates are sent to each device. In this thesis, Couchbase is evaluated as a tool to solve this problem where the data has a geographical position. The scalability problem is solved by partitioning the data with the help of Couchbase channels and Google’s tile-based mapping system. Synchronising and storing only data of interest for each user has been in focus. The result showed that it was effective to use a Couchbase solution together with Google’s tile-based mapping system to reduce the amount of data that was required to be stored for each user. It was shown to be more effective to store objects encoded as base64 data instead of their binary data representation for the data set used in this study. The reason for this is because Couchbase stores Binary Large Objects (BLOBs) as separate files and the BLOBs in the data set had much smaller file size than what the disk sector size was. A test to find how the synchronisation time was affected by the number of channels was conducted. It showed that the synchronisation time increased linearly with an increasing number of channels when the objects were stored in separate files. When the objects were encoded as base64 data, the number of channels used had a minor effect on the synchronisation time. The conclusion is that the approach presented in this study has been effective. However, the results are data dependent and therefore it is recommended to rerun similar tests in order to decide the number of channels to use when partitioning the data.