To rate a data store is to compute a value that describes the performance of the data store with a database and a workload. A common performance metric of interest is the highest throughput provided by the data store given a pre-specified service level agreement such as 95% of requests observing a response time faster than 100 milliseconds. This is termed the action rating of the data store. This paper presents a framework consisting of two search techniques with slightly different characteristics to compute the action rating. With both, to expedite the rating process, the framework employs agile data loading techniques and strategies that reduce the duration of conducted experiments. We show these techniques enhance the rating of a data store by one to two orders of magnitude. The rating framework and its optimization techniques are implemented using a social networking benchmark named BG. ∗ This paper is accepted to appear in the Springer Transactions on Large-Scale Data and Kno...