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The unprecedented growth of data volumes has caused traditional approaches tocomputing to be re-evaluated. This has started a transition towards the use ofvery large-scale clusters of commodity hardware and has given rise to thedevelopment of many new languages and paradigms for data processing andanalysis. In this paper, we propose a compiler technology-based alternative tothe development of many different Big Data application infrastructures. Key tothis approach is the development of a single intermediate representation thatenables the integration of compiler optimization and query optimization, andthe re-use of many traditional compiler techniques for parallelization such asdata distribution and loop scheduling. We show how the single intermediate canact as a generic intermediate for Big Data languages by mapping SQL andMapReduce onto this intermediate.

Providing A Compiler Technology-Based Alternative For Big Data Application Infrastructures