In the last few years, the growing significance of data-intensive computing has been closely tied to the emergence and popularity of new programming paradigms for this class of applications, including Map-Reduce, and new high-level languages for data-intensive computing. The ultimate goal of these efforts in data-intensive computing has been to achieve parallelism with as little effort as possible, while supporting high efficiency and scalability. While these are also the goals that the parallel language/compiler community has tried meeting for the past several decades, the development of languages and programming systems for data-intensive computing has largely been in isolation to the developments in general parallel programming. Such independent developments in the two areas, i.e., data-intensive computing and high productivity languages lead to the following questions: I) Are HPC languages suitable for expressing data-intensive computations? and if so, II.a) What are the issues in using them for effective parallel programming? or, if not, II.b) What characteristics of data-intensive computations force the need for separate language support?. This paper takes a case study to address these questions. Particularly, we study the suitability of Chapel for expressing data-intensive computations. We also examine compilation techniques required for directly invoking a data-intensive middleware from Chapel's compilation system. The data-intensive middleware we use in this effort is FREERIDE that has been developed at Ohio State. We show how certain transformations can enable efficient invocation of the FREERIDE functions from the Chapel compiler. Our experiments show that after certain optimizations, the performance of the version of Chapel compiler that invokes FREERIDE functions is quite comparable to the performance of hand-written data-intensive applications.