Data modeling in Cassandra databases follows a query-driven approach where each table is created to satisfy a query, leading to repeated data as the Cassandra model is not normalized by design. Consequently, developers bear the responsibility to maintain the data integrity at the application level, as opposed to when the model is normalized. This is done by embedding in the client application the appropriate statements to perform data changes, which is error prone. Cassandra data modeling methodologies have emerged to cope with this problem by proposing the use of a conceptual model to generate the logical model, solving the data modeling problem but not the data integrity one. In this thesis we address the problem of the integrity of these data by proposing a method that, given a data change at either the conceptual or the logical level, determines the executable statements that should be issued to preserve the data integrity. Additionally, as this integrity may also be lost as a consequence of creating new data structures in the logical model, we complement our method to preserve data integrity in these scenarios. Furthermore, we address the creation of data structures at the conceptual level to represent a normalized version of newly created data structures in the logical model.