Hybrid cloud technology is becoming increasingly popular as it merges private and public cloud to bring the best of two worlds together. However, due to the heterogeneous cloud installation, facilitating a hybrid cloud setup is not simple. In this thesis, Apache Mesos is used to abstract resources in an attempt to build a hybrid cloud on multiple cloud platforms, private and public. Viable setups for increasing the availability of the hybrid cloud are evaluated, as well as the feasibility and suitability of data segmentation. Additionally an automated cloud bursting solution is outlined and implementation has been done in an attempt to dynamically scale the hybrid cloud solution to temporarily expand the resource pool available in the hybrid cloud platform using spot price instances to maximize economical efficiency. The thesis presents functional and viable solutions with respect to availability, segmentation and automated cloud bursting for a hybrid cloud platform. However, further work remains to be done to further improve and confirm the outlined solution, in particular a performance analysis of the proposed solutions.