MapReduce is a tool that is used by most of the big organizations to analyze quickly the huge amounts of data their customers generate via online activities. This enables them to understand and serve their customers better and efficiently. Therefore, MapReduce is a programming framework, which was developed by Google to simplify data processing across massive data sets. In fact, MapReduce is the engine of Hadoop.
If you are familiar with clustered scale-out data processing, you will easily understand the MapReduce concept. If you are new to Hadoop’s MapReduce jobs, by the end of this article, you will have grasped the tips to help you catch up quickly.
How does it work?
The term MapReduce comes from the two independent tasks that Hadoop programs execute. The first task is the Map job that picks a set of data and converts it to another set of data in which the individual elements are broken down to tuples. Tuples are key/value pairs. The second task or Reduce job uses as input the output from the map and combines the data tuples into a smaller set of tuples. The Map job comes first followed by the Reduce job.
This makes the MapReduce the most appropriate tool when you want to perform numerous operations in a business sales operation, especially that involving Internet search in massive data sets.
MapReduce, Big Data and IT budgets
Currently, there are multiple opportunities for the MapReduce programming to change the environment in which big data has an impact on an IT budget. To begin with, it will play a major role in accommodating the exponentially increasing amounts of data like storage, processing and storage. Secondly, it will reduce the cost of models imposed by the It vendors.
The enormous amount of data is usually as a result of increasing mobility trends, data access and consumption and ecosystem capabilities. For instance, mobile devices, mobile events, sharing and sensory integration are becoming part of our daily activities.
Hadoop – MapReduce Market Forecast 2015-2020
It is estimated that by 2020, the Hadoop MapReduce market will have grown at a compound annual growth rate of 58%. This will enable it to attain $2.2 billion mark. The secret behind it is that it is taking the Big Data market very fast such that the small players also have a chance at the playground. Without any doubt, MapReduce is very cost effective and scalable when it comes to handling Big Data in comparison to other packages, which dominate the market industry.
The most fascinating thing about MapReduce is that it scales linearly with any addition of compute nodes. Since it stores the data locally on each node, the network bandwidth will be eliminated as a bottleneck. In other words, the data will be accessed from shared storage. Therefore, with MapReduce if you have enough computers you will be able to process large amounts of data within a very short time. It is also important to note that MapReduce has applications, which can be run on other networks rather than Google and Yahoo.