Saiba como aplicar o paradigma MapReduce para projetar algoritmos escaláveis e tolerantes a falhas para problemas como classificação, pesquisa, análise de gráficos e aprendizado de máquina.

When Hadoop first started gaining attention and early adoption it was inseparable – both technologically and rhetorically – from MapReduce, its then-venerable big data-processing algorithm ...

Introduction This repository contains the implementation of a basic MapReduce framework simulation as part of the Operating Systems course project. The project demonstrates key operating system ...

This paper introduces a new algorithm, which is capable of finding st-connectivity in arbitrary graphs, designed for the MapReduce framework. The paper also defines the data structure, which might be ...

Google introduced the MapReduce algorithm to perform massively parallel processing of very large data sets using clusters of commodity hardware. MapReduce is a core Google technology and key to ...

MapReduce Will be designing and implementing MapReduce algorithms for a variety of common data processing tasks Has a python library called MapReduce.py that implements the MapReduce programming model ...

In recent years the MapReduce framework has become one of the most popular parallel computing platform for processing big data. It is frequently used by companies such as Facebook, IBM, and Google.

MapReduce works well when performing the sort of aggregate operations typical in business intelligence-daily sales totals for instance-as well as operations such as web search that involve searches ...