The extraction of cosmological parameters from big galaxy surveys is a very complex task. It is indispensable for the creation of synthetic universes to learn how to deal with massive data. The volume needed to be sampled makes it very challenging to implement galaxy formation simulations. Therefore, one solution possible is populating halos from huge dark matter-only simulations with empirical recipes. In this work, I will present the galaxy mocks created for the Euclid consortium. I will detail the different recipes used in the process of assigning properties to galaxies, crucial for testing the robustness of theoretical models. Emphasis will be placed on the big data frameworks that are necessary to generate mocks and data sets of this large magnitude.