  The amount of data generated in the modern society is increasing rapidly. New
problems and novel approaches of data capture, storage, analysis and
visualization are responsible for the emergence of the Big Data research field.
Machine Learning algorithms can be used in Big Data to make better and more
accurate inferences. However, because of the challenges Big Data imposes, these
algorithms need to be adapted and optimized to specific applications. One
important decision made by software engineers is the choice of the language
that is used in the implementation of these algorithms. Therefore, this
literature survey identifies and describes domain-specific languages and
frameworks used for Machine Learning in Big Data. By doing this, software
engineers can then make more informed choices and beginners have an overview of
the main languages used in this domain.
