Hadoop Ecosystem Architecture Components
First of all let’s understand the Hadoop Core Services in Hadoop Ecosystem Architecture Components as its the main part of the system.
Hadoop Core Services:
Apache Hadoop is developed for the enhanced usage and to solve the major issues of big data. Hadoop uses an algorithm called MapReduce. This is basically built on a principle of “Divide & Rule”. A very huge amount of data can be divided into multiple parts and sent to process in different CPU nodes in parallel.
Hadoop framework is potential enough to generate applications which are capable to run statistical analysis on a huge amount of data in a cluster of computers. A single server scaled up to N number of machines by which each machine ultimately offering local computing and storage facility. Let’s understand with example later.
Key Points of Hadoop Core Services:
- Hadoop is the Framework for working with Big Data.
- Hadoop framework application works on a structure which allows distributed storage and analyse across a bundle of computers.
- Servers can be added or removed from the cluster of dynamically without causing any interruption to the operations.
Apache Hadoop Ecosystem Architecture and It’s Core Components:
As its core Hadoop has two major layers and two other supporting modules.
There five building blocks inside Hadoop Ecosystem Architecture Components:
- As its name refers it’s a collection of Java libraries and utilities that are required by/common for other Hadoop modules.
- These libraries contain all the necessary Java files and scripts required to start Hadoop. So Hadoop common becomes one basic module of Apache Hadoop framework along with other three major modules and hence becomes the Hadoop core.
- Yet Another Resource Navigator (YARN) framework in simple words can be termed as Resource Manager. This framework is basically used for job scheduling and efficient cluster resource management.
- It takes the responsibility of providing the computational resource (e.g., CPU storage memory devices, etc) required for application executions.
Hadoop Distributed File System (HDFS™):
- HDFS framework is suitable for applications having large data sets. HDFS is a perfect framework to deal with BIG data.
- Its advantage is that it is designed to be deployed on low-cost hardware. HDFS is responsible for providing permanent, reliable and distributed storage. HDFS provides unrestricted, high-speed access to the data application. This is typically used for storing Inputs & Outputs.
- MapReduce framework acts as the very core and integral part of the Hadoop architecture. Our main process to deal with large amount of datasets in less time and in efficient manner.
- In order to solve any complex problem what we need to do is to break it into multiple small sets of problems.
- Broken small set of problems quickly we need to solve it parallel. MapReduce does the same.
- It is one efficient framework to handle large volume data-sets by breaking into multiple data-sets and assigning it to a cluster of computers to work parallel at same time.
A detailed explanation on all of these Hadoop Ecosystem Architecture Components will be look into in the coming sections.