Map Reduce: what is it and how it relates to Big Data

At this very moment, a multitude of applications, when used and thanks to internal algorithms, are collecting data. Data about processes, people, systems and companies which result in a huge volume of information. The challenge for companies which collect this data is to store, process and analyze them. This is where Map Reduce comes into play but, what is it exactly?

Map Reduce is, fundamentally, a tool with a particular programming model used in Big Data thanks to its capacity to divide and parallelly process huge volumes of information.

Initially, Map Reduce was a system used by Google to analyze its search results but, as time passed and Big Data evolved and grew, it ended up integrating in Apache Hadoop, an ecosystem where different components coexist, focused on Big Data-related work.

In this article, we’re going to explore what this tool is, how Map Reduce works and how it integrates exactly within the most used technologies and programming models in Big Data. Are you interested? Keep reading!

 

Map reduce: what is it and what are its integrations

Big Data experts need to be proficient in the use of certain tools in order to be able to work with big data volumes coming from different sources. As such, this tool presents a significant capacity for data processing and facilitates data splitting in order to work on data simultaneously or in parallel.

But what exactly is Map Reduce? It’s a programming model or pattern that integrates within the Apache Hadoop framework. Map Reduce is used, as we’ve mentioned above, to access big quantities of data that are stored in Hadoop’s archive system (HDFS).

 

Map Reduce is one of the most important components in order to make Hadoop work

 

The work of Map Reduce is to facilitate the simultaneous processing of huge quantities of data. In order to do so, it divides petabytes of data in smaller fragments and process them in parallel in Hadoop servers.

Map Reduce not only sends data where the application is hosted, but it’s also directly executed from where data is found. This accelerates the processing of this data.

As an example, Map Reduce is able to take data blocks of 256 MB each and process up to 5 TB of data at a cluster made of 20,000 servers. This capacity for fragmenting and parallel processing of information significantly reduces required times in comparison to other traditional processing methods.

Map Reduce used to be the only method through which data hosted in Hadoop’s HDFS could be restored, although this is no longer the case. Today, Hadoop offers other additional components that make consultations through SQL types of requests. However, given the features and advantages of MapReduce, these combine with it to improve data processing efficiencies.

 

How does Map Reduce work?

Do you now know Map Reduce a bit more and how it integrates in Hadoop? Let’s now see how this programming model works. To sum up, Map Reduce works through two main functions: Map and Reduce. Does it sound simple? Let’s go a bit more into it.

 

Map

The Map function takes input data and divides them in smaller blocks. Then, each block is assigned a mapper (a Hadoop server that executes the Map Reduce functions) in order to process it.

For example, if we wish to process a data archive that presents 100 registers, 100 mappers can be executed, each of them dedicated to each data entry, or 50 mappers with two data inputs each.

Hadoop will decide how many mappers are to be used to process data, according to the data quantities that will be processed and the available memory blocks in each server.

 

Reduce

The Reduce function starts working after mappers complete data processing. This is applied in parallel to each of the groups created by the Map function.

Reduce processes data in such a way that they’re simplified and read in a sequential manner, so that it executes an output file for each of the processed tasks.

 

Combine and divide

Combining is an optional process within data processing with this tool. This is a reductor that is executed in an individual manner in each server. It further reduces data and simplifies them before the Reduce function is executed.

This facilitates data classification, as there is less data to work with. At the same time, combined data are divided in order to be able to transfer them to Reduce.

 

How is Map Reduce useful for Big Data?

At the beginning of this article, we’ve mentioned how Map Reduce was born as a programming model that used Google to analyze its search results. This was a specific need of this technological giant: to reduce the quantity of input information and divide data processing for better efficiencies.

This need could already be extrapolated to other applications. As time passed, Big Data has increasingly settled and seen Map Reduce as a useful tool to integrate within data processes.

This tool is the seed of parallel processing, which facilitated the management of big quantities of data, both structured and unstructured.

It was in 2006 when Apache Hadoop was released with a Map Reduce implementation which left its mark, in the positive sense, in the history of Big Data, boosted its implementation and significantly improved working processes with big quantities of data.

As we’ve mentioned above, Map Reduce is currently combined with other integrated components in Hadoop to offer a better and more efficient work when it comes to Big Data processing.

 

Learn Big Data analysis!

You now know Map Reduce a bit better: what it is, how Map Reduce works and how it integrates in the use of Hadoop for Big Data. However, if you want to be proficient in this framework, you’ll need training. An specialized course will help you become a data scientist, data architect or data consultant, among other professions.

What better option than resorting to experts in order to find it? At Tokyo School we’re specialists in training around new technologies and couldn’t miss the opportunity to join the Big Data revolution. With our Big Data course you’ll be able to get trained to become one of today’s most demanded professional profiles.

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