Nowadays, although MapReduce is applied to the parallel processing on big data, it has some limitations: for instance, lack of generic but efficient and richly functional primitive parallel methods, incapability of entering multiple input parameters on the entry of parallel methods, and inefficiency in the way of handling iterative algorithms. Spark and Stratosphere are developed to deal with (partly) the shortcoming of MapReduce. The goal of this thesis is to evaluate Spark and Stratosphere both from the point of view of theoretical programming model and practical execution on specified application algorithms. In the introductory section of comparative programming models, we mainly explore and compare the features of Spark and Stratosphere that overcome the limitation of MapReduce. After the comparison in theoretical programming model, we further evaluate their practical performance by running three different classes of applications and assessing usage of computing resources and execution time. It is concluded that Spark has promising features for iterative algorithms in theory but it may not achieve the expected performance improvement to run iterative applications if the amount of memory used for cached operations is close to the actual available memory in the cluster environment. In that case, the reason for the poor results in performance is because larger amount of memory participates in the caching operation and in turn, only a small amount memory is available for computing operations of actual algorithms. Stratosphere shows favorable characteristics as a general parallel computing framework, but it has no support for iterative algorithms and spends more computing resources than Spark for the same amount of work. In another aspect, applications based on Stratosphere can achieve benefits by manually setting compiler hints when developing the code, whereas Spark has no corresponding functionality.