Hadoop Fundamentals Course Overview
Hadoop is a framework that is very efficient for distributed storage and computing. Hadoop enables distributed processing of large data across clusters using a very simple programming model.
The Hadoop design allows scaling up from single servers to thousands of machines, and each of them is capable of performing local computation and storage. Hadoop is Fault-tolerant, scalable, Efficient, reliable distributed storage system
Organizations can make decisions based on the analysis of data sets and variables that are made possible by Hadoop. The exposure to a large quantity of data using Hadoop, enables you to get a view of customers, opportunities, operations and risks.
Hadoop Programming Level1 Certification course enables you to gain working knowledge on the Map Reduce and YARN. Map Reduce is a data processing component of Hadoop.
MapReduce performs data processing and distributes the tasks across the nodes. It is divided into two namely Map and reduce. Map converts a dataset into another dataset, and in this process, individual datasets fall into key and value pairs.
Reduce collects the output files from a map and integrates the data tuples into a smaller set of tuples. Reduce performs this task only after the completion of Map task.
Yarn stands for yet another Resource Negotiator, and typically used for processing framework. Yarn helps to construct a framework on Hadoop. This framework allows o allocate specific applications to the cluster resources and MapReduce also becomes one among these cluster resources.
What will you learn as a Hadoop Beginner?
During this course, you will learn to:
1. Understand the working of Hadoop
2. Use Hadoop Architecture and the Hadoop Distributed File System
3. Setup and learn the installation procedures
4. Use HDFS Operations
5. Modify configuration parameters
6. Use MapReduce and YARN
Why get enrolled in Hadoop Basics Course?
Enroll in this course to
1. Understand Hadoop
2. Use the Hadoop architecture
3. Understand the Core components like MapReduce
4. Use the Hadoop Distributed File System
5. Learn about streaming and Multi-Node clusters
Course Offering
1. Live/Virtual Training in the presence of online instructors
2. Quick look at Course Details, Contents, and Demo Videos
3. Quality Training Manuals for easy understanding
4. Anytime access to Reference materials
5. Gain your Course Completion Certificate on the Topic
6. Guaranteed high pay jobs after completing certification
Hadoop Fundamentals Learning Benefits
1. Learn the basics of Hadoop
2. Understand the Hadoop Architecture
3. Explain the working methodology of Hadoop
4. Learn to use the Hadoop Distributed File System (HDFS)
5. Gain skills in using the Hadoop administration concepts
6. 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 Fundamentals
Basic knowledge of Big Data
Basic understanding of Linux Operating System
Familiar with Scala, Python, or Java programming languages.
Hadoop Fundamentals Course Content
Lesson 1: Hadoop Introduction
Any projects that require infrastructure for distributed computing and large-scale data processing makes use of Hadoop.
Class 1:
What is Hadoop
Definition of Big data
Class 1.2:
Open source software for Hadoop
Working methodology of Big Data on the Cloud
Lesson 2: Hadoop Architecture
This lesson teaches you about the Hadoop Architecture, Hadoop architecture is developed upon two independent frames namely HDFS and MapReduce.
The namenode in Hadoop manages the filesystem namespace. Datanodes store and retrieve blocks based on the client's requirements or if requested by the namenode, and report back to the namenode regularly with lists of blocks that they are storing.
Class 2.1:
Introduction
Hardware Failure
Class 2.2:
Streaming Data Access
Large Data Sets
Simple Coherency Model
NameNode and DataNodes
Lesson 3: Hadoop Administration
Hadoop maintains separate configuration files for each Hadoop node in the cluster. Hadoop Administrators ensure that they are always in sync across the system. This lesson teaches you to use the cluster components and modify the configuration parameters.
Class 3.1:
Add and remove nodes from a cluster
Verify the health of a cluster
Start and stop cluster components
Class 3.2:
Modify Hadoop configuration parameters
Set up a rack topology
Lesson 4: Hadoop Components and Ecosystem
The framework in Hadoop gets converted into a comfortable place for performing all big data activity using the Hadoop ecosystem. The ecosystem serves your unique requirements.
Class 4.1:
About MapReduce philosophy
About Pig and Hive
Class 4.2:
Pig and Hive use in Hadoop environment
Flume and Sqoop usage to move data into Hadoop
Usage of Oozie for scheduling and controlling Hadoop job execution
FAQs
1. Who uses Hadoop?
Many organizations use Hadoop in their research and production environment.
2. Why should we enroll for this course?
Big Data and Hadoop are high demand in the market, and this increasing need opens many more job opportunities in different industries. Enroll in this course to grab those opportunities and gain an excellent piece of work opportunity.
3. What are the features of Hadoop?
Features of Hadoop:
Scalable: This feature enables you to add new nodes without changing the existing data formats and loading process
Cost effective: Hadoop helps in a sizeable decrease in the storage cost as it allows to store an enormous amount of data and operate on that data in parallel.
Flexible: Hadoop combines and aggregates data in any format from multiple sources and performs in-depth analysis on that data.
Fault tolerant: The system redirects work to another location even in case you lose a working node. It allows the process to continue without missing a beat.
4. What are the components in Hadoop?
Hadoop four core components are HDFS, MapReduce, YARN, and Common. However, for some of the commercially available framework solutions, there are three well-known components available namely Spark, Hive, and Pig. All the components in Hadoop help to create applications and process data.