Hadoop Introduction Course Overview
Hadoop - an open source framework for working efficiently with Big data concepts. Hadoop allows you to access data that occurs distributed among clustered computers, process the data and manage the resources across the clustered computers.
Hadoop is only a framework to work with Big data and remains as a part of the big data ecosystem. As we referred Hadoop has a framework, it is made up of core modules which are supported by a large ecosystem. The various modules are Hadoop Distributed File System (HDFS), Hadoop YARN, Hadoop MapReduce and Hadoop Common.
Hadoop is part of Data Science, and you can use existing Hadoop to try new things. Hadoop works well in different environments, and this paves the way for job opportunities for Hadoop certified personnel to develop applications, handle the output and manage the environment.
What will you learn in Hadoop Introduction Course?
During this course, you will learn hadoop step by step:
Understand the definition of Hadoop
Use Hadoop ecosystem
Work with Hadoop Distributed File System
Use the Hadoop security features
Identify the real-world applications of Hadoop
Why get enrolled in this Course?
Enroll in this course to
Understand Hadoop
Use the Hadoop components
Understand the Core components like MapReduce
Use the Hadoop Distributed File System
Learn about streaming and Multi-Node clusters
View the applications of Hadoop in different industries
Learn about security in Hadoop
Hadoop Basic Course Offerings
Live/Virtual Training in the presence of online instructors
Quick look at Hadoop Introduction Course Details, Contents, and Demo Videos
Quality Training Manuals for easy understanding
Anytime access to Reference materials
Gain your Course Completion Certificate on the Topic
Guaranteed high pay jobs after completing certification
Hadoop Introductory Course Benefits
Learn the basics of Hadoop
Understand the Hadoop ecosystem
Explain the working methodology of Hadoop
Learn to use the Hadoop Distributed File System (HDFS)
Gain skills in using the Hadoop security concepts
Identify the use cases of Hadoop
Learn the core components of Hadoop like MapReduce and Hadoop ecosystem
Audience
Data Engineers
Data Scientists
Software Developers and Architects
Analytics Professionals
Senior IT professionals
Data Management Professionals
Business Intelligence Professionals
Project Managers
Graduates willing to build a career in Big Data Analytics
Prerequisite for learning Hadoop Introduction
Basic knowledge of Big Data
Basic understanding of Linux Operating System
Familiar with Scala, Python, or Java programming languages.
Hadoop Fundamentals Syllabus and Course Content
Lesson 1: Hadoop Introduction
1.1. What is Hadoop?
1.2. Definition of Big data
1.3. Open source software for Hadoop
Lesson 2: Hadoop Benefits
This lesson explains all the benefits of Hadoop thereby explaining how you can make decisions for your organization based on the analysis performed using Hadoop
2.1. Benefits of Hadoop
Lesson 3: Hadoop Use Cases
In this chapter, you will understand how effectively Hadoop is applicable in real-time environment like retail, finance, telecommunications industry
3.1. Use of Hadoop in real time scenario
Lesson 4: Hadoop Security
The Hadoop ecosystem supports Hadoop Security. Many changes occur in the way of authentication and providing service
4.1. Hadoop Security methods
4.2. Hadoop Authentication process
4.3. Hadoop Access and Permissions
Lesson 5: About Core elements of Hadoop
The core elements of Hadoop includes the Hadoop Distributed Filesystem(HDFS), MapReduce, YARN, and Common.Hadoop works with any distributed file system, but the essential one is the HDFS which is the heart of Hadoop technology. HDFS allows to manage data files and store them across the cluster. Data gets stored in the form of blocks and each server in the cluster stores data from different blocks.
5.1. Overview of HDFS
5.2. Overview of MapReduce
5.3. Overview of Yarn
5.4. Overview of Common
FAQs
1.Why should we enroll for this course?
Now Big Data and Hadoop are a high demand in the market. The increasing requirement for work increases the job opportunities in different industries. Enroll in this course to grab those opportunities and gain a real career growth.
2. What are the advantages of Hadoop?
The following are the benefits of Hadoop:
Very efficient and automatically distributes the data and works across machines.
Allows user to write and test distributed systems quickly
Hadoop is not hardware dependent and is designed to detect and handle failures.
Add or remove servers from the cluster dynamically
It is open source and also compatible on all platforms since it is java based.
3. What is the relationship between Big Data and Hadoop?
Big Data is becoming popular for data processing and management while Hadoop is a technology framework. Hadoop serves as a gateway for enabling smooth working of Big data or for working with large data sets that are available in distributed environment.
Big data holds an enormous amount of data that needs relational databases for processing while Hadoop helps overcome the RDBMS limitations and makes it easier to process large data.
4. What is meant by Distributed systems?
Distributed systems consist of some computers that are connected and managed so that they automatically share the job processing load among the connected computers.
6. What is DFS?
DFS stands for Distributed File System that allows users from physically distributed computers to share their data and storage resources using a conventional file system.
7. What does Hadoop replace?
Hadoop replaces the traditional data processing and reporting system which is already in use by the new decision-making technique. A wide range of organizations found Hadoop useful as it helps them better understand their customers, competitors, supply chains, risks, and opportunities.
8. What are not supported points to remember in Hadoop?
You must remember that
Hadoop is not the same as BigData
Hadoop is not an Operating System
Hadoop is not a brand name
9. Define MapReduce?
MapReduce is a framework for writing applications to process large amounts of data.These data are dealt with In parallel, on large clusters of commodity hardware in a reliable manner.
The major advantage of MapReduce is easy scaling of data processing over multiple computing nodes.
10. What is the definition of Yarn?
YARN stands for Yet Another Resource Negotiator. Yarn manages resources across cluster environment. Tasks like resource management, job scheduling, and job management get broken into separate daemons by Yarn for easy management.
11. What is meant by Common?
One of the elements of Hadoop Core is Common and is useful as it gives ways for Hadoop framework to manage hardware failures.