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The world around us is changing. Traditional approaches of doing business are giving way to modern methods. Most of the transactions that take place today are over the internet and ecommerce has slowly risen to prominence.
It has become a known fact that big data has been hogging the limelight in many sectors in the recent times.
Developers and Programmers are still continue to explore various approaches to leverage the distributed computation benefits of MapReduce and the almost limitless storage capabilities of HDFS in intuitive manner that can be exploited by R.
Simple” often sense as “elegant” when it comes to those remarkable architectural drawings for that new Silicon Valley mansion we have planned for when the money starts rolling in after we implement Hadoop.
HDFS is one of the two main components of the Hadoop framework; the other is the computational paradigm known as MapReduce.
In HDFS, the Data block size needs to be large enough to warrant the resources dedicated to an individual unit of data processing On the other hand.
As we already know that in Hadoop, files are composed of individual records, which are ultimately processed one-by-one by mapper tasks.
From the beginning of the Hadoop’s history, MapReduce has been the complete game changer in town when it comes to deal with data processing.