The schema flexibility of database management systems is often seen as an advantage, because it makes it easy to store all kinds of different data. Schema-less database systems (such as JSON or graph databases) allow structurally different data to be stored in the same database. It also allows storing different variants of data or data evolving over time. However, their use is much more complicated compared to relational data. In this paper, we show the impact of heterogeneity on two data processing steps: query execution and evolution operations including their composition and data transformation. From this, we will derive three proposals: (i) storing data with a partial schema management, (ii) vertical partitioning, and (iii) the usage of multi-model databases. In all cases, regular and irregular parts are distinguished in order to mitigate these effects of heterogeneity.