Question - When to use MapReduce with Big Data.
Answer -
MapReduce is a programming model created for distributed computation on big data sets in parallel. A MapReduce model has a map function that performs filtering and sorting and a reduced function, which serves as a summary operation.
MapReduce is an important part of the Apache Hadoop open-source ecosystem, and it’s extensively used for querying and selecting data in the Hadoop Distributed File System (HDFS). A variety of queries may be done depending on the broad spectrum of MapReduce algorithms possible for creating data selections. In addition, MapReduce is fit for iterative computation involving large quantities of data requiring parallel processing. This is because it represents a data flow rather than a procedure.
The more enhanced data we produce and accumulate, the higher the need to process all that data to make it usable. MapReduce’s iterative, parallel processing programming model is a good tool for creating a sense of big data.