Scatter-updates represent a reoccurring algorithmic pattern in many scientific applications. Their scalable execution on modern systems is difficult due to performance limitations introduced by their irregular memory access pattern that prohibits an efficient use of the memory subsystem. Further performance degradation is caused by techniques that are required in order to eliminate potential data races and come at the cost of overhead. Taking a closer look at algorithmic properties, access patterns and common support techniques reveals that a one-size-fits-all solution does not exist and solutions are needed that can adapt to individual properties of the algorithm while maintaining programming transparency. In this work we propose a solution framework that supports a broad set of techniques, provides the required access pattern analytics to allow dynamic decision making and shows what language extensions are needed to maintain programming transparency. A reference implementation in OmpSs, a task-based parallel programming model, shows programmability and scalability of this solution.