The Java Stream API aims at increasing developer productivity thanks to an easy-to-read declarative syntax to express computations. It also simplifies parallel computing, providing a high-level abstraction on top of common parallelization aspects. Unfortunately, there is a lack of benchmarks specifically targeting stream-based applications. Such a lack of benchmarks makes it difficult for researchers and developers of the Java class library to optimize the Stream API. Moreover, in the absence of dedicated benchmarks, it is difficult to analyze the performance of streams to suggest developers how to write efficient code using the API. In this work we present JEDI, a benchmark suite that targets the Stream API. JEDI is automatically generated by converting SQL benchmarks into Java benchmarks. Our code generator supports targets different implementations (both stream-based and imperative) for the same query. The ultimate goal of our benchmark suite -- and the main contribution of this work -- is to analyze the performance of the different implementations to spot inefficient code structures and better alternatives, suggesting best practices to Java developers. Among the multiple implementations we generate, we focus on different parallelization strategies and explain the most efficient parallelization strategies based on characteristics of the processed data. Finally, the code generation producing imperative code defines of a baseline that can guide researchers and Java implementers to optimize the Stream API.