Abstract
The introduction of MapReduce as a tool for Big Data Analytics, combined with the new requirements of emerging application scenarios such as the Web 2.0 and scientific computing, has motivated the development of data processing languages which are more flexible and widely applicable than SQL. Based on the Big Data context, we discuss the points in which SQL is considered too restrictive. Furthermore, we provide a qualitative evaluation of how recent query languages overcome these restrictions. Having established the desired characteristics of a query language, we provide an abstract description of the compilation into the MapReduce programming model, which, up to minor variations, is essentially the same in all approaches. Given the requirements of query processing, we introduce simple generalizations of the model, which allow the reuse of well-established query evaluation techniques, and discuss strategies to generate optimized MapReduce plans.
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