Why Use Hadoop MapReduce Programming?

Mar 28, 2018

Hadoop MapReduce

Its an age dominated by big data. Big data jobs are flourishing, and companies are paying over the odds for data scientists. All news revolves around data analytics, machine learning, and artificial intelligence. All such advanced technologies have to start somewhere. It all begins with the generation and processing of big data sets. Appropriate tools are required to handle such massive data sets which can then be used further for machine learning, AI systems, or generating business insights. Of multiple tools available in the market right now, Hadoop MapReduce is one of the most preferred data processing application, which is based on the Apache Hadoop framework. In short, Hadoop is an open-source software framework which stores data in a distributed file system, and its processing part is called MapReduce. The MapReduce framework is used by several players in the e-commerce industry including Amazon, Yahoo, and Zventus for high volume data processing. So why is MapReduce application so popular and what are the advantages of using it?

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Simple Coding Model

Programmers using MapReduce framework needs to specify two program functions namely map function and reduce function. MapReduce uses a simple coding model as the programmer doesn’t have to implement parallelism, distributed data passing, or any other complexity. It not only simplifies the coding process but also reduces the amount of time taken to create analytical routines.


The Hadoop architecture is highly scalable, as companies only need to add new nodes when they need to increase data storage and computational power. The structure allows it to distribute large datasets across plenty of inexpensive servers which can operate parallelly. Hadoop MapReduce programming enables companies to run applications from large sets of nodes that can use thousands of terabytes of data.

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Any sort of data mining process requires substantial computational power, which translates to higher power needs. Also, instead of buying dedicated servers and workstations, companies can just keep on adding new systems to the existing server to increase their computational power. Traditional relational database management systems incur high costs when scaled to levels of Hadoop MapReduce. As a result, businesses have to classify their data storage needs and downsize data to get rid of data that they think weren’t necessary. In such a process, companies might end up deleting raw data to serve short-term priorities. Hadoop MapReduce allows the storage and processing of data in a very affordable manner. The storage cost per terabyte of data has reduced from thousands of dollars to a few hundred dollars with this application.


Hadoop MapReduce programming allows businesses to have access to new sources of data and operate on various types of data. Since the programming enables enterprises to access both structured and unstructured data, significant value can be derived by gaining insights from multiple sources of data. Additionally, it also offers support for multiple languages and from sources ranging from social media, email to clickstream. Since MapReduce processes simple key-value pairs, it supports data type including images, meta-data, and large files. Consequently, programmers feel that MapReduce is easier to deal with than DBMS for irregular data set.

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Since Hadoop used a distributed file system, data is stored in a cluster and is easier to map. Hadoop MapReduce programming can access data much faster wherever they are stored in the server. The speed is so impressive that it can skim through terabytes of unstructured data in a matter of minutes.


Hadoop MapReduce programming used HDFS and HBase security platforms which only allows access to approved users to operate on the data. Thereby, it protects unauthorized access to data and enhances system security.

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