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The topic involving the processing of large amounts of data is widely discussed subject currently, about its challenges and applicability. This work proposes a programming tool for development and an execution environment for applications with large amounts of data. The use of the tool aims to achieve better performance of applications in this scenario, exploring the use of physical resources such as multiple lines of execution in multi-core processors and distributed programming, which uses multiple computers interconnected by a communication network, so that they operate jointly in the same application, dividing such processing among such machines. The proposed tool consists of the use of programming blocks, so that these blocks are composed of tasks, and the blocks are executed using the producer consumer model, following an execution flow. The use of the tool allows the division of tasks between the machines to be transparent to the user. With the tool, several functionalities can be used, such as cycles in the execution flow or task advancing using the strategy of speculative processing. The results were compared with two other frameworks, Hadoop and Spark. These results indicate that the use of the tool provides an increase in the performance of the applications, mostly when executed in homogeneous clusters.

Ferramenta de programação e processamento para execução de aplicações com grandes quantidades de dados em ambientes distribuídos.