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Hadoop Online Training

Hadoop Online Training    
 
   
 
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Hadoop Online Training Course Details:

Hadoop is an open-source framework that supports to store and process processing of large data sets in a distributed computing environment across clusters of computers using simple programming models. It is designed to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system supports rapid data transfer rates among nodes and allows the system to continue operating uninterrupted even in case of a node failure or significant number of nodes become inoperative thus reduces the risk of catastophic system failure.

Hadoop Online Training Course Details


The Hadoop course includes both Hadoop Development and Admin Training. the course has been designed to give you basic understanding about Big Data technologies. The course make you understand what the Big Data is and the need of Hadoop to process the date.

Hadoop Online Course Details   Course Features
Course
Hadoop Online Training
Duration
30 days
Demo Class
Available
Content
See below


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Hadoop Bigdata Course Contents

 

Hadoop Bigdata Course Content

Introduction to BIG Data and Hadoop

1. BigData Inroduction

  • What is Big Data
  • Why Big Data
  • Evolution of Big Data
  • Objectives
  • Data Explosion
  • Types of Data
  • Need for Big Data
  • Big Data and Its Sources
  • Leveraging Multiple Data Sources
  • Traditional IT Analytics Approach
  • Big Data Technology Capabilities
  • Big Data Use Cases
  • Handling Limitations of Big Data

2. Hadoop Introduction

  • Introduction to Hadoop
  • History and Milestones of Hadoop
  • Organizations Using Hadoop
  • Hadoop Eco-System
  • Hadoop Framework
  • Hadoop vs RDBMS
  • Hadoop vs SAP Hana vs Teradata
  • How ETL tools works in Hadoop
  • Hadoop Requirements and supported versions
  • Use cases of Hadoop

3. HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper Introduction

4. What is the scope of Hadoop?

Hadoop Architecture n Deployment

  • Objectives
  • Key Terms
  • Ubuntu Server Introduction
  • Installing Ubuntu Server
  • Hadoop Installation Prerequisites
  • Installing Hadoop
  • Hadoop Multi-Node Installation Prerequisites
  • Steps for Hadoop Multi-Node Installation
  • Single-Node Cluster
  • Multi-Node Cluster
  • Performing Clustering of the Hadoop Environment
  • Hadoop Cluster Using Commodity Hardware
  • Hadoop Configuration
  • Hadoop Core Services
  • Apache Hadoop Core Components
  • Error Handling

Hadoop Distributed File System ( HDFS )

  • HDFS Introduction
  • HDFS Design and role in Hadoop
  • Features of HDFS

5 Daemons of Hadoop and its functionality

1 Name Node and its functionality
2 Secondary Name Node and its functionality
3 Job Tracker and its functionality
4 Data Node and its functionality
5 Task Tracker and its functionality

File Reading and Writing
Network Topology
Basic Configuration for HDFS
Data Organization
Blocks
Replication
Rack Awareness
HDFS Federation
Scaling HDFS
Performance Tuning
HDFS Cluster Administration
How to Write the Data into HDFS
How to Read the Data from HDFS
Accessing HDFS - Basic UNIX commands
Command line Interface commands

Map Reduce

  • Understanding Map Reduce
  • Map Reduce Components
  • Map Reduce Architecture
  • Map Reduce Internals


Data flow in MapReduce
o Splits
o Mapper
o Portioning
o Sort and shuffle
o Combiner
o Reducer

Basic Configuration of MapReduce
MapReduce life cycle
o Driver Code
o Mapper
o and Reducer

How Map Reduce Works
Anatomy of Map Reduce job run
Job submission
Job initializationTask assignment
Job completion
Job scheduling
Job failures
Shuffle and sort

Build MapReduce Application
Writing Map Reduce Programs
Map Reduce API’s
Data Types
Configuring development environment
Running on cluster

Input Formats in MapReduce
Output Formats in MapReduce
Distributed Cache
Map Reduce Features

Counters
Types of Counters
o Task Counters
o Job Counters
o User Defined Counters
o Propagation of Counters

Sorting

Joins
o Map-side Joins
o Reducer-side Joins
o Replicated -Joins
Side data distribution
Map Reduce combiner
Map Reduce partitioner
Map Reduce Administration
Performance Tuning
Hands on exercises

PIG
Objectives
Pig Overview
When should PIG use?
How Pig Works
Components of Pig
Installation
Pig Latin
Pig latin command
Pig latin relational operators
Pig latin diagnostic operators
Data types
Expressions
Basic PIG Programing
Modes of Execution in PIG
PIG Interactive Modes
Pig with HDFS
Creating Tables
Loading and Manipulating Tables Data
Data Analysis using pig Latin
Pig UDF’s

HIVE
Introduction to HIVE
Why Hive
Hive Charecteristics
HIVE Meta Store
HIVE Architecture
Hive Data Types : Primitive, Complex types
Tables in HIVE : Managed Tables, External Tables
Basics of Hive Query Language
Hive Query Language
Running Hive
Programming in Hive
user-Defined Function
Built-In Functions
Other Functions in Hive
Partition
Joins in HIVE
HIVE UDF’s and UADF’s with Programs

Sqoop
Introduction to SQOOP
Use of SQOOP
Connect to mySql database
SQOOP commands
Sqoop Processing
Sqoop Execution Process
Importing Data Using Sqoop
Eval
Sqoop Connectors
Joins in SQOOP
Importing Data to Hive and HBase
Exporting Data to HBase

Hbase

HBase Overview
What is Hbase
Hbase Architecture
Hmaster, Zookeeper, Region Servers, Regions
HBase Components
HBase Installation
HBase Basic configuration
What is No SQL
SQL vs. NOSQL
HBase Data Model
Categories of NoSQL Data Bases
HBase Key Design
HBase Operations

Zookeeper

Introduction Zookeeper
What is ZooKeeper
Features of ZooKeeper
ZooKeeper Data Modal
ZooKeeper Operations

OOZIE

Introduction to OOZIE
Why OOZIE
Use of OOZIE
Where to use?
OOzie Work flow

Flume

Introduction to Flume
Why Flume
Uses of Flume
Flume Architecture
Flume Model
Flume Goals

Hadoop Ecosystem

Ecosystem
Objectives
Ecosystem Components

Hands on Project with Explanation

 

 

 

 

 

 

 
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