Big Data technologies are rapidly becoming a key enabler for modern industries. However, the entry costs inherent to "going Big" are considerable, ranging from learning curve, renting/buying infrastructure, etc. A key component of these costs is the time spent on learning about and designing with the many big data frameworks (e.g., Spark, Storm, HadoopMR, etc.) on the market. To reduce said costs while decreasing time-to-market we advocate the usage of Model-Driven Engineering (MDE), i.e., software engineering by means of models and their automated manipulation. This paper outlines a tool architecture to support MDE for big data applications, illustrating with a case-study.