Apache Hadoop is an excellent open-source big data technology platform that allows the use of computer networks to perform complex processing and come up with results that are always available even when a few nodes are not available for functional processing. There are a few important Hadoop core components that govern the way it can perform through various cloud-based platforms. The core components are often termed as modules and are described below:
The Distributed File System
The first and the most important of the Hadoop core components is its concept of the Distributed File System. It allows the platform to access spread out storage devices and use the basic tools to read the available data and perform the required analysis. This is a unique file system because it sits above the individual file system of the network node computers and allows unmatched functionality.
The DFS of Hadoop can perform the required data acquisitions without worrying about the operating system of the individual computers. This allows the network to employ greater power and never face the problem of having to comply with the different computer systems available for use. It also allows the connection to other core components, such as MapReduce.
MapReduce
MapReduce is another of Hadoop core components that combines two separate functions, which are required for performing smart big data operations. The first function is reading the data from a database and putting it in a suitable format for performing the required analysis. This is the function that we know more as a mapping activity. It essentially allows a platform to prepare the data for the analytical needs in a common format to allow any computer to do the next step.
The next step in this core component is that of a mathematical operation. This operation is termed as reduction, because it usually reduces the available map to a set of well-defined values that describe an important statistical value. Together, these functions are present in a single module and perform the entire operation which provides information from the available data sources.
Hadoop Common
It is also one of the Hadoop core components and it brings that tools which allow any computer to become part of the Hadoop network regardless of the operating system or the present hardware. This module uses Java tools and parts that create a system like the virtual machine and allow the Hadoop platform to store data under its specific file system.
It is a component aptly named as common because it offers the required common functionality, which eliminates the difference between the different hardware nodes which may be connected to the network at any given time.
YARN
The fourth of the Hadoop core components is YARN. It is the component which manages all the information sources that store the data and then run the required analysis. It is a system which manages the available resources in a network cluster, as well as schedule the processing tasks to come up with a smart solution, for every big data need on the system.
Although there are other components that are now becoming part of Hadoop Core Components, these four components still remain the basic unit behind this excellent big data technological platform.
