A potential problem for persisting large volume of streaming logs with conventional relational databases is that loading large volume of data logs produced at high rates is not fast enough due to the strong consistency model and high cost of indexing. As a possible alternative, state-of-the-art NoSQL data stores that sacrifice transactional consistency to achieve higher performance and scalability can be utilized. In this paper, we describe the challenges in large scale persisting and analysis of numerical streaming logs. We propose to develop a benchmark comparing relational databases with state-of-the-art NoSQL data stores to persist and analyze numerical logs. The benchmark will investigate to what degree a state-of-the-art NoSQL data store can achieve high performance persisting and large-scale analysis of data logs. The benchmark will serve as basis for investigating query processing and indexing of large-scale numerical logs.