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The amount of data generated in the modern society is increasing rapidly. Newproblems and novel approaches of data capture, storage, analysis andvisualization are responsible for the emergence of the Big Data research field.Machine Learning algorithms can be used in Big Data to make better and moreaccurate inferences. However, because of the challenges Big Data imposes, thesealgorithms need to be adapted and optimized to specific applications. Oneimportant decision made by software engineers is the choice of the languagethat is used in the implementation of these algorithms. Therefore, thisliterature survey identifies and describes domain-specific languages andframeworks used for Machine Learning in Big Data. By doing this, softwareengineers can then make more informed choices and beginners have an overview ofthe main languages used in this domain.

A Survey on Domain-Specific Languages for Machine Learning in Big Data