The objective of the blog is to give a basic idea on Big Data Hadoop to those people who are new to the platform. This article will not make you ready for the Hadoop programming, but you will get a sound knowledge of Hadoop basics and its core components. You will also get to know why people started using Hadoop and why it became so popular in the short-time span only.
To prepare these tutorials, I took reference from multiple books and prepared this gentle definitive guide for beginners in Hadoop. This tutorial will surely provide perfect guidance to help you in deciding your career as a Hadoop professional or why to choose Hadoop as the primary career choice?
A Gentle Introduction to the big data Hadoop
Hadoop is an open-source Apache framework that was designed to work with big data. The main goal of Hadoop is data collection from multiple distributed sources, processing data, and managing resources to handle those data files.
People are usually confused between the terms Hadoop and the big data. Few people use these terms interchangeably but it should not be. In actual, Hadoop is a framework designed to work with big data. The popular modules that every Hadoop professional should know about either he is a beginner or advanced user include – HDFS, YARN, MapReduce, and Common.
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- HDFS (Hadoop Distributed File System) – This core module provides access to big data distributed across multiple clusters. With HDFS, Hadoop gets access to multiple file systems too, as required by the organizations.
- Hadoop YARN – This module helps in managing resources and scheduling jobs across multiple clusters that stores the data.
- Hadoop MapReduce – MapReduce works similar to Hadoop YARN but it is designed to process large data sets.
- Hadoop Common –This module contains a set of utilities that support three other modules. Some of the other Hadoop ecosystem components are Oozie, Sqoop, Spark, Hive, or Pig etc.
What Hadoop isn’t?
Now we will discuss what Hadoop is not so that related confusion with the terminology can be avoided quickly.
- Hadoop is not Big Data – People are usually confused between the terms Hadoop and the big data. Few people use these terms interchangeably but it should not be. In actual, Hadoop is a framework designed to work with big data.
- Few people consider Hadoop as an operating system or set of packaged software apps, but it is neither operating system nor a set of packaged software apps.
- Hadoop is not a brand, but an open source framework that can be used by registered brands based on their requirements.
Core Elements of Hadoop Modules
“Detailed discussion on Hadoop Modules or Core elements to give you valuable insights on Hadoop framework and how it actually works with big data”
HDFS – Hadoop Distributed File System
This core module provides access to big data distributed across multiple clusters of commodity servers. With HDFS, Hadoop gets access to multiple file systems too, and it can work with almost any file system exists as of now.This is the primary requirement by organizations so Hadoop Framework became so popular in shorter time span only. The functionality of the HDFS core module makes it the heart of Hadoop framework.
HDFS keeps track of files how they are distributed or stored across the clusters. Data is further divided into blocks and blocks need to access wisely to avoid redundancy.
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Hadoop YARN – Yet another Resource Navigator
YARN helps in managing resources and scheduling jobs across multiple clusters that stores the data. The key elements of the module include Node Manager, Resource Manager, Application Master, etc.The “Resource Manager” assigns resources to the application. The “Node Manager” manages those resources on different machines like CPU, network or memory, etc. The “Application Master” works as a library for the other two components and sits between the two. It helps in resource navigation so that tasks can be executed successfully.
MapReduce works similar to Hadoop YARN but it is designed to process large data sets. This is a method to allow parallel processing on distributed servers. Before actual data is processed, MapReduce converts large blocks into smaller data sets that are named as “Tuples” in Hadoop.
“Tuples” are easy to understand and work on when compared to larger data files. When data processing is complete by MapReduce then work is handed over to the HDFS module to process the final output. In brief, the goal of MapReduce is to divide large data files into smaller chunks that are easy to handle and process.
Here the word MAP refers to Map, Tasks, and Functions. The objective of “Map” process is to format data into key-value pairs and assigning them to different nodes. After this “reduce” function is implemented to reduce large data files into smaller chunks or “Tuples”. One of the important components of MapReduce function is JobTracker that checks out how jobs are
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This module contains a set of utilities that support three other modules. Some of the other Hadoop ecosystem components are Oozie, Sqoop, Spark, Hive, or Pig etc.
Why Hadoop is just loved by the organizations processing Big Data?
The way Hadoop process big data is just incredible. This is the reason why Hadoop Framework is just loved by the organizations that have to deal with voluminous data almost daily. Some of the prominent users of Hadoop include – Yahoo, Amazon, eBay, Facebook, Google, IBM, etc.
Today, Hadoop has made a prominent name in the industries that are characterized by the big data and handles more sensitive information that could be used to provide further valuable insights. They can be used for all business sectors like Finance, Telecommunications, Retail sector, online sector, government organizations, etc. The uses of Hadoop don’t end here, but it sure gives you an idea about Hadoop growth and career prospects in reputed organizations. If you also wanted to start your career as Hadoop professional then join a Hadoop training program at JanBask Training right away.
We hope you enjoyed reading this article. If you breezed through this article and the information discussed later then you might be interested in checking out the details on Hadoop training programs and career opportunities too. Feel free to write us for further queries as we like to entertain your queries quickly after expert advice only.