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MapReduce dataflow Quick Tutorial

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Here are the six stages of MapReduce. The MapReduce is critical for your data processing needs. Traditionally, the whole file needs to read once then divided manually, but it is not convenient. With that respect, Hadoop provides the facility to read files (ignoring their size) line-for-line by using offset and key-value. MapReduce dataflow Quick Tutorial 1. Dataflow Diagram 2. MapReduce Stages MapReduce receives input and processes it. Here are the six stages of processing . It is helpful for your interviews and project. MapReduce Stage-1 Take the file as input for processing purposes. Any file will consist of a group of lines. These lines containing key-value pairs of data. The whole file can be read out with this method. MapReduce Stage-2 In the next step, the file will be in "splitting" mode. This mode will divide the file into key, value pair of data. This time key will be offset and data will be a valuable part of the program. Each line will be read individually so there