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The fault tolerance mechanism employed by MapReduce is achieved through data replication, which can affect scalability by increasing the size of data even further. The need for data replication has been found to be responsible for 90 % of the running time of machine learning tasks in MapReduce [50] and is perhaps the biggest impediment to fast data processing. Another deficiency of MapReduce is that it does not easily allow for iterative processing, making it unsuitable for many machine learning projects. While it is possible to achieve iterative computation in MapReduce, this must be programmed manually through multiple MapReduce jobs requiring careful orchestration of execution [51]. This process is complicated and unable to address any of the previously discussed issues relating to computational resources.

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