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How does Hadoop help us manage Big Data?


Apart from the problem of storage another problem with Big Data is processing. When data is huge the time taken for processing it will also be very high. Additionally since the processing power has not increased much in the large 100 years, at least on par with the increase in storage capacity, the speed of processing doesn’t look good as compared to the huge amounts of data available. Thus, we have huge amounts of data but the processing speed is very less comparatively. Hence, the only solution to processing huge data in less time is having more processors or machines.


And to bridge the gap between the amount of data that requires processing and processing speed Hadoop has been introduced. Thus, Hadoop is considered the best solution especially to reduce the time taken for processing Big Data. How is Hadoop going to overcome the Big Data challenges like huge volumes and large processing time? The core concepts that make up Hadoop are HDFS and MapReduce. These concepts behind the above two techniques have been introduced to the world in the form of white papers by Google and they were called GFS or Google file system and MapReduce. Later these concepts were further transformed by Yahoo as HDFS and MapReduce.


Here HDFS or Hadoop Distributed File System is a technique to store huge amount of data and MapReduce is a technique to process this huge data stored in HDFS in less time. And Hadoop is an open source frame work given by Apache software foundation. Basically it is being overseen by Apache software foundation. This is a technique for storing and processing huge datasets by using commodity hardware. HDFS is a technique for storing huge amount of data with a cluster of commodity hardware and MapReduce is a technique for processing that data stored on HDFS.