Hadoop Course Online

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The Contents

About Hadoop01

Types of data comes under big data02

Benefits of big data03

Solution to process big data04

The Contents

Hadoop architecture05

MapReduce06

Hadoop Distributed File System07

Working with Hadoop08

The Contents

Advantages of Hadoop09

Hadoop Environment setup10

Overview of HDFS11

Features of HDFS12

The Contents

Architecture of HDFS13

Operations of HDFS14

Hadoop MapReduce15

Big Data And Hadoop For Beginners16

About Hadoop

Hadoop is an open source software

framework which allows the user to store and

process a large amount of data.

Hadoop consists of computer clusters which

are built from commodity software.

Hadoop framework was designed by Apache

software foundation, and it was originally

released on 25th Dec 2011.

The storage section is often referred as

Hadoop Distributed File System (HDFS), and

the processing part is performed by using

MapReduce programming model.

Types of data comes under big data

The big data has data generated by

various applications and devices.

Different types of data which come

under the category of big data

black box data,

social media data,

power grid data,

search engine data,

transport data,

stock exchange data.

Benefits of big data

In hospitals, big data plays a vital role,

and the data analytics are storing the

patients medical history using big data

technologies

This will help the doctors to provide

quick service to the patients.

The main challenges of big data are

capturing data, storage, curation,

searching, transfer, sharing, analysis

and presentation.

Solution to process big data

If we have the small amount of data, we

can store and process the data using the

traditional approach.

In this approach, the data is typically

stored in RDBMS like MS SQL Server,

Oracle database, etc.

While dealing with huge amount of data, it

is not possible for storing and processing

the data using the traditional database

server.

Hadoop architecture

Hadoop framework has four modules

such as Hadoop YARN, Hadoop common,

Hadoop MapReduce, and Hadoop

Distributed File System (HDFS).

MapReduce

Hadoop MapReduce is a software

framework which is used to write

applications for processing the vast

amount of data with the help of

thousands of nodes.

Hadoop Distributed File

System

HDFS provides the distributed file system

that is used to run massive clusters of

small computer machines in fault

tolerant and reliable manner. This

distributed system is based on the Google

file system (GFS).

Working with Hadoop

The application or user submits the job to the Hadoop client. Then the Hadoop client sends the job and its configuration to the job tracker which is responsible for splitting and distributing the configuration to the slaves. Job tracker is also responsible for scheduling the works and monitored them only then it can provide the status to the job client. After completing this process, the task trackers on different nodes perform the job using MapReduce algorithm, and finally, the output files are stored in the file system.

Advantages of Hadoop

Since Hadoop is an open source framework,

so it is compatible with all the platforms.

We can add or remove the servers

dynamically. This process doesnt interrupt

the Hadoop in any way.

The users can write and test the distributed

systems quickly using Hadoop framework.

Hadoop Environment setup

Linux operating system supports the

Hadoop framework. The Linux users can

easily setup the Hadoop environment on

their computers. Before starting to install

the Hadoop, the users have to setup the

Linux using Secure Shell (SSH). If the users

have the OS other than Linux, then they

need to install the software called

Virtualbox that have the Linux OS inside it.

Overview of HDFS

HDFS stores huge amount of data and

provides easier access. This distributed file

system is fault tolerant, and it is designed

with low-cost hardwares.

Features of HDFS

It provides file authentication and

permissions.

Interaction with HDFS is possible using

common interface system which is provided

by Hadoop.

HDFS is perfectly suitable for distributed

storage and processing purposes.

Architecture of HDFS

Hadoop distributed file system follows the

master-slave architecture which has the

following components such as namenode,

datanode, and block.

Intention of HDFS

Process the large datasets efficiently

Fault detection and recovery

Hardware at data

Operations of HDFS

Firstly the users have to format the

configured HDFS file system and start the

distributed file system.

Then listing files in HDFS which means

loading information into the server.

After that, the users have to insert data into

Hadoop Distributed File System.

Retrieve the data from HDFS.

Finally shut down the HDFS.

Operations of HDFS

Firstly the users have to format the

configured HDFS file system and start the

distributed file system.

Then listing files in HDFS which means

loading information into the server.

After that, the users have to insert data into

Hadoop Distributed File System.

Retrieve the data from HDFS.

Finally shut down the HDFS.

Hadoop MapReduce

MapReduce is used to process the enormous amount of data, and it is otherwise known as processing technique. This algorithm performs two tasks to process the data completely. The works include the map and reduce. Here map is used to convert a set of data into another set of data.

Hadoop MapReduce

The individual elements are splitting into tuples. Then the output of the map is taken as the input by the reduce task which combines the data tuples into the smaller set of tuples. MapReduce program executes the process in three stages comprises of map stage, shuffle stage and reduce stage.

Big Data And Hadoop For Beginners

Beginner, Students, Manager, and

Developer, can take this course if you are

interested in learning Big Data. This 3 hours

hadoop course online has six sections with 1

article and six supplemental resources.

The prime motto of this course is making

you understand the Hadoop components and

its complex architectures.

Hadoop Course Online 2017

Hadoop Tutorial

Learn Big Data

Learn Hadoop And MapReduce For

Big Data Problems

Big Data And Hadoop For Beginners

Hadoop Course Online

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