In this paper we consider data freshness and overload handling in embedded systems. The requirements on data management and overload handling are derived from an engine control software. Data items need to be up-to-date, and to achieve this data dependencies must be considered, i.e., updating a data item requires other data items are upto- date. We also note that a correct result of a calculation can in some cases be calculated using a subset of the inputs. Hence, data dependencies can be divided into required and not required data items, e.g., only a subset of data items affecting the fuel calculation in an engine control needs to be calculated during a transient overload in order to reduce the number of calculations. Required data items must always be up-to-date, whereas not required data items can be stale. We describe an algorithm that dynamically determines which data items need to be updated taking workload, data freshness, and data relationships into consideration. Performance results show that the algorithm suppresses transient overloads better than (m, k)- and skipover scheduling combined with established algorithms to update data items. The performance results are collected from an implementation of a real-time database on the realtime operating system \muC/OS-II. To investigate whether the system is occasionally overloaded an offline analysis algorithm estimating period times of updates is presented.