Today’s market is surely data-driven as everything is surviving on data only. This is not just an IT industry story, rather everywhere this is the case. There are numerous databases and data warehouses to take care of the huge amount of data that is generated on daily basis, yet without the speed to process this data, these databases seems burdensome. MapReduce is a framework that makes the task of writing applications easier by the parallel processing of vast amounts of data on large clusters of commodity hardware in a reliable manner.

In Hadoop world, MapReduce is considered as a job that follows split-apply-combine strategy while analyzing the data. It divides the input dataset into small and independent chunks that are further processed by the map tasks. These chunks are processed parallelly.  The output of the maps is then sorted first, and later placed in reduce tasks. Thus the output of MapReduce works as input for reduce tasks. Then this reduce task combines these data tuples into a smaller set of tuples.

MapReduce is as simple as a configuration change. Once the application is being written in the MapReduce form, it scales the application to run over a huge number of machines (ranging from hundreds to tens of thousands too). So Hadoop developers themselves stated that this MapReduce model is easy for scaling the data processing over multiple computing nodes. So simple scalability is a major advantage of a MapReduce model.

MapReduce is a five-step parallel & distributed computation job. These five steps are stated below:

1. • Map input – MapReduce model assigns the input key valueK1 for each processor would work upon.
   • Map() code – Map() is being run once for each key value K1. The output generated is marked as key value K2.
   • Shuffle step – The above generated key value K2 is then assigned to processor to work upon. The processor is being assigned with the key value associated Map-generated data.
   • Reduce() code – Reduce() is being run once for each key value K2.
   • Final output – This step is for collecting all the Reduce output and thus the final outcome has been produced.

Apart from good and simple scalability, there are several other benefits of MapReduce that adds up the feathers to the Hadoop’s cap. Let us have a quick look at these perks:

• Cost-effectiveness – Growing data means increasing cost of managing it. But with MapReduce model, this issue has been evicted swiftly.
• Fast – The tools that are used for MapReduce programming are located in the same servers, thus enhances the speed of processing the data.

Apart from these two, security, flexibility, availability and simplicity are among the advantages of this model.

With the growing rate of data, demand for Hadoop is also rising up and so is the demand of Hadoop experts too. We, at Besant Technologies provides the best Hadoop training in Chennai, best in terms of both quality as well as cost too. We have professional Hadoop gurus to train you.

Keeping a bull’s eye on the market stats, we also recommend the IT aspirants to have a good Big Data Hadoop training in Bangalore that will cover all the aspects – ranging from HDFS to MapReduce, Pig to Hive, and much more. Choosing us over other institute is an assurance of getting a good Hadoop job in IT as we provide both theoretical as well as hands-on sessions along with 100% assistance in job placement!