How mapreduce works
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When Hadoop word comes to mind instantly, one more word also comes side by side in mind which is big data. Big data means a very large amount of data.…
advantages and disadvantages of decision tree
Various types of data compression in MapReduce When Hadoop word comes to mind instantly, one more word also comes side by side in mind which is big data. Big data means a very large amount of data. When we need to play with a large amount of data there will always be an issue of scarcity of space. So, how can we or Hadoop as architecture can handle such a critical issue? Hadoop has provided very nice and important to rescue us from this issue. The resolution is data compression. We can do data compression using different Hadoop libraries on our huge dataset. If you are still not clear about what are the benefits of data compression in Hadoop let me show you. As we will compress the dataset size required to store data will decrease drastically. On the other end as we all know we need to transfer data among the Hadoop clusters from one machine to another. So, as a result of data compression data size will decrease, and eventually, the speed at which data will be transferred over ...
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