Hadoop MapReduce: With the constant advancement of technology, many things in the medium have evolved, and data storage capacity is one of them.
However, the reading of this data has yet to follow the evolution, and it is for the solution of problems like this that Hadoop appears, free software developed by the Apache Software Foundation in Java language. Its main focus is processing a large amount of data as efficiently as possible.
This framework is used especially in distributed computing environments where clusters are used. When it was developed, Hadoop had as one of its main objectives the realization of the expansion of a server to several other machines, which would provide local computing and storage.
Now that you know a little about Hadoop let’s discuss its relationship with Big Data and MapReduce. Good reading!
Relationship Of Big Data With Hadoop
As already mentioned, Hadoop is used for processing Big Data workloads because it is a highly scalable tool. To increase the processing capacity of the Hadoop cluster, a few more servers are added with the minimum memory and CPU requirements to meet the needs well.
It’s also worth mentioning that Hadoop features a high level of availability and durability, even during parallel processing computational analytic workloads. Hadoop is the perfect choice for data-intensive workloads, as it has the right combination of durability, scalability, and availability.
Hadoop Configuration Modules
Hadoop comprises modules, each responsible for carrying out an essential task for operating computer systems specially programmed for data analysis. Next, we’ll talk more about what these modules are and what each one of them does. Look!
Distribution Of File Systems
Being one of the most important modules, this one allows data to be properly stored in a simpler and more accessible format between numerous linked storage devices. This is a method used through a computer to store data that can be found and used in the future.
The computer’s operating system usually determines this process. However, a Hadoop system uses its file software, which sits above the host computer systems, meaning it can be accessed from any other computer with a compatible operating system.
This module receives this terminology due to its two basic operations: reading the database, formatting them appropriately for analysis, and performing mathematical operations. It performs operations, such as counting the number of women over 25 years old in a database. MapReduce guarantees the tools responsible for exploring the data in different ways.
Hadoop Common is the module in charge of providing some tools in Java language for the operating systems on the users’ computers so that they can read the data stored in the Hadoop file system.
As the fourth and final module, we have Yarn. A simple module, but one that is nonetheless important, is responsible for managing the resources of the systems responsible for storing the data and performing the analysis.
MapReduce was cited as one of the modules supported by Hadoop. However, this is a very important part of the framework and covers a wide area in Hadoop MapReduce’s relationship with Big Data. Therefore, we will talk more about the characteristics and advantages of MapReduce below.
This tool has as its main feature the solution of the problem related to the reading and writing of data. With all the advances technology has given, the capacity of disks and some other storage equipment has increased dramatically; however, the speed of writing and reading this data has remained the same.
For this, MapReduce’s solution is reading and writing in parallel, using several disks, each with a fraction of all the data. Therefore, if we have a single HD with all the data and divide it into another 100 HDs, each with 1% of the total, their processing will be 100 times faster.
However, reading or writing data in parallel can cause two very common problems. The first is that, in addition to processing being 100 times faster, the chances of data loss also increase a lot. To avoid this problem, numerous backup copies of the data stored on other disks are usually made.
The second problem is generated by the fact that data analysis tasks demand that a combination of data spread across several different disks be made. However, this problem only causes a little headache. That’s because MapReduce works by “un-shuffling” this data spread out, processing it through a combination of keys and values found on different disks.
The Hadoop processing model, MapReduce, has numerous advantages, making it useful within Big Data solutions. However, the great advantage that can be highlighted among them is based on the fact that whoever is programming does not need to worry about the details present in parallel processing, as well as with task scheduling, for example. This whole part is controlled internally by Hadoop.
Another great advantage is that it is very simple and easy to use. The developer who works with MapReduce can learn some of the theoretical parts, which would be massive data processing and distributed file systems.
Hadoop MapReduce is, without a doubt, a tool that greatly facilitates the solution of problems generated by Big Data. When used correctly, it can generate indispensable benefits and be the solution your company is looking for to improve results.