Abstract
Information technologies such as the internet, and social networks, produce vast amounts of data exponentially (known as Big Data) and use conventional information systems. Big Data is characterized by volume, a high rate of generation, and variety. Systems integration and data querying systems must be adapted to cope with the emergence of Big Data. The authors' interest is with the impact Big Data has on the decision-making environment, most particularly, the data querying phase. Their contribution is the development of a parallel and distributed platform, named high level query language for big data analytics (HLQL-BDA), created to query vast amounts of data in a computer cluster based on the MapReduce paradigm. The query language in HLQL-BDA is implemented by means of interactive query language based on a functional model. The researchers' experiment shows the scalability of HLQL-BDA when they increase the number of nodes and the size of data.
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