Why MapReduce is a popular data processing paradigm?
What is the need of MapReduce to process the data?
Why MapReduce paradigm is available in the lots of Big Data frameworks: Hadoop, Spark, Flink, MongoDB, etc.
MapReduce is a data processing paradigm in itself. This was one of its kind data processing and has been transformative. While using MapReduce we are moving computation to data which is less costly as compared to when data is moved to the computation.
Before the development of Hadoop MapReduce, the huge volume of data processing was very difficult as hundreds and thousands of processors (CPU) were needed to handle the huge amount of data. Moreover, parallelization and distribution were also not possible with huge data sets. Map reduce makes these things possible and easy on top of that, it also provides I/O scheduling, status, and monitoring of job.
MapReduce is a Fault Tolerant programming model present as the heart of Hadoop ecosystem. Because of all the above features, MapReduce has become the favourite of the industry. This is also the reason that it is present in lots of Big Data Frameworks.
To prove its importance I would like to add an example given in the book Hadoop – The Definitive Guide as Mailtrust, Rackspace’s mail division, used Apache Hadoop for processing email logs. One ad hoc query they wrote was to find the geographic distribution of their users. In their words:
“This dataset was very essential, Rackspace arranges the hadoop MapReduce program to run periodically every month, now they will be using these insights to help us decide which data centers to place new mail servers (and other resources) in as they grow.”
By bringing 1000s of gigabytes of data and provide the tools to analyze it, the Rackspace team were able to gain the insights of the data that they otherwise would never have had, and they were able to use what they had learned to improve the service for their customers.