MapReduce can be defined as the sub-module of Hadoop that offer huge scalability of data spread across numerous of commodity clusters. MapReduce comprises of two things that work consecutively to process the analytics. The process in both the different parts is done in a parallel manner helping save a lot of time while working with significant data. In the traditional data analysis approach, the data was analyzed serially and MapReduce overcomes that problem.

As it’s named sound, it involves mapping and reducing process which is done by mappers and reducers. The dataset gets divided equally among different mappers and all of the processes or analyses the data in a parallel manner. Once the mapper produces the outcome, reducers come in to generate the outcome. The role of the reducer is to collect the data from all the mappers and then process their outcome to get the final result.

For instance, if Flipkart needs to find out the total sell in 2018 in Mumbai. The entire process will flow below.

• The entire dataset will be divided into months which means the sell data of one year will be divided into 12 months like how much they made each month from which location.

• The dataset will be then assigned to 12 mappers.

• Each mapper will find out in which city and how of how much the goods were sold.

• After the mappers generate the report, now it comes to the turn of reducers.

• The reducers will grab the sell value from every month for Mumbai location.

• Eventually, they will all sell value to generate the outcome.

In this MapReduce training course, you will learn something that is going to be the next big thing soon, generating lots of opportunities in the new future. You will learn how to work with mass data, unstructured data. Working with various kinds of data and try to get all of them on the same page anyway is what you will study here. In technical terms, you will be getting a practical insight into the working of data scientists. In addition to data processing, you will also learn to develop a program in HIVE, PIG, MapReduce, and Sqoop.

Every organization has its requirement for data analysis so it is very important to develop a customized program that can generate the desired output. You will see and learn how the sub-modules of Hadoop like PIG or HIVE could be used to reduce the complexity of the program. In addition to all those vital things, you will learn which framework should you use and in which case. By the time you come to the end of the MapReduce certification, you will be enough cognizant to play with abundant data.