How to choose a MapReduce Design Pattern?

Design pattern are infect making it easier for developers as these are tools problem solving tools which can be reused and specifies ways to solve a problem. Hence the developer need not spend too much of time in analysis a similar problem. They can use patterns to arrive at the solution sooner than before. There is also transferability involved in pattern where the knowledge or information can be transferred to other or successors who will be handling similar kind of data. Design pattern have time and again proved its mettle and over the years it has become a large source of learning especially in the field of data science.

MapReduce design pattern is same like a general design pattern expert for the type of computation that is involved. A problem is taken and the solution of that problem is arrived by fitting the solution in the map and reduce to arrive at the pattern which will help us in reaching the solution. They provide a general and regulatory in nature kind of framework for your problem and hence you can reuse and form similar pattern for similar solutions. Also, a developer who can created the MapReduce code can any time pass on the knowledge to a junior or a new person handling it in the later period of time. MapReduce has become so popular because of the general framework it provides and also the fast adoption and acceptance rate within the industry which is very important for any processing framework. It also provides a common languages for teams to work together and hence the process is unified and there is no need for a new software to process the data across the teams.

MapReduce is predominantly used in the context of Hadoop and is very important to know what kind of data and what is the data requirement that is needed for it to form patterns. Just like any system which needs an input to process the output, MapReduce also need a certain data level requirements like inter-clustered operated nodes connectivity to start processing the data and give us the right kind of framework to solve the problem. The input to a MapReduce is a set of data files which are stored across the Hadoop distributed file system which is commonly referred to as the HDFS. These files are fed into a input split where each file is split with respect to the format and the category before it is fed into the MapReduce system.

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