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Hadoop at Hari Technologies Institute

₹ 14,000  
No # 3, V R K H Building Vivekananda Layout Opposite to Home town Beside Biriyani Zone , Marathahalli, Bengaluru, Karnataka, ,
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About The Course:-

This course will cover concept such as HDFS, MapReduce, sqoop, hive, hbase, pig, etc.,.

Who Should Take this?

Professionals who are aspiring to make a career in big data analytics using Hadoop framework.

Data warehousing professionals, Business and Data analyst, Java developer, Software Engineers.


Basic knowledge of any object oriented programming language.

Experience on working in Linux environment is also valuable.

Course Objectives:-

After completion of the course you should be able to :

    • Understanding Big Data and Hadoop
    • Hadoop Architecture and HDFS
    • Hadoop MapReduce Framework – I
    • Hadoop MapReduce Framework – II
    • Advance MapReduce
    • Pig
    • Hive
    • Advance Hive and HBase
    • Advance HBase
    • Oozie and Hadoop Project Explanation with Architecture

Module 1: Introduction

Topics covered on introduction
  • Introduction
    • Introduction to Big Data and Hadoop
    • Traditional IT Analytics Approach
    • Introduction to Big Data and Hadoop Data Explosion
    • Types of Data Big Data and Its Sources Characteristics of Big Data

Module 2: HDFS Introduction

Topics covered on HDFS
  • Introduction to Hadoop
  • Regular File System vs. HDFS
  • Hadoop Core Components HDFS & MapReduce
  • HDFS Characteristics
  • HDFS Key Features
  • HDFS Architecture

Module 3: MapReduce

Topic 1: Understanding MapReduce
  • Introduction to YARN
  • YARN Infrastructure
  • Applications Running on YARN Application Startup in YARN Introduction to MapReduce
  • Map Execution Distributed Two Node
  • MapReduce Essentials
  • MapReduce Jobs
  • MapReduce and Associated Tasks
  • Hadoop Job Work Interaction
  • Joins in MapReduce Reduce Side Join
  • Replicated Join &Composite Join Cartesian product
Topic 2: MapReduce Programs
  • MapReduce Examples: Word Count
  • Hadoop MapReduce programming in JAVA Max word Length
  • Number of Employees in an Organization
  • Minimum Salary of an organization
  • Maximum Salary of an organization
  • Average Salary of an organization
  • Total Salary of an Organization
  • For Each Gender group, Number of employees
  • For each department – calculate average salary
  • Filtering rows using MapReduce(Map only)
  • Filtering Columns using MapReduce(Map only)
  • Generating new columns using MapReduce(Map Only)
  • Performing Transformations using MapReduce(MapReduce)
  • MapReduce example: Number of words per line
  • Multiple Input files

Module 4: Sqoop

Topics covered on Sqoop
  • Introduction to Flume
  • Flume Architecture and Components
  • Introduction to Sqoop
  • Sqoop Architecture and Components

Module 5: HIVE

Topic 1: HIVE Introduction, Installation and Configuration
  • Introduction to Hive
  • Hive Architecture & Components Driver
  • Hive Thrift Server
  • Basics of Hive Query Language Data Model Tables
  • Data Types in Hive
  • Data Model Partitions Bucketing in Hive
  • Serialization and Deserialization
  • Hive File Formats
  • Hive Query Language
  • Hive Query Language Extensibility User-Defined Function
  • Built-In Functions Other Functions in Hive
  • UDF/ UDAF vs. MapReduce Scripts
  • New Features supported in Hive
  • Advanced Data Analytics Demo Determining Word Count Demo Partitioning with Hive Demo
Topic 2: DDL, DML, SQL Operations and Excercise
  • DDL Operations:-
    • Metadata Store
  • DML Operations/ SQL Operations:-
    • Queries
    • Select and Filters
    • Group By
    • Multi table Insert
    • Streaming
  • Exercise:-
    • MovieLens
    • Apache log
Topic 3: HIVE Architecture
  • Data Store
  • Metastore
  • Architecture
  • Interface
  • HQL
  • Compiler
  • Optimizer
Topic 4: UDF in HIVE
  • How to write hive UDF example in JAVA
  • Convert Int to Float
  • Convert the column uppercase to lowercase
  • Hive Partition
  • Hive Buckets Optimization Techniques
  • Log analysis using Hive

Module 6: HBase

Topics covered on HBase
  • Introduction to Hbase
  • HBase Architecture & Components
  • Storage Model of HBase
  • Row Distribution of Data between
  • Region Servers Data Model &
  • Data Storage in HBase When to Use HBase HBase vs. RDBMS
  • HBase Architecture & Components Storage Model of HBase
  • Row Distribution of Data between Region Servers
  • Data Model & Data Storage in HBase
    When to Use HBase HBase vs. RDBMS

Module 7: PIG

Topic 1: PIG Introduction
  • Introduction to Pig
  • Pig Architecture and Components
  • Additional Libraries for Pig
  • Steps to Run a Sample Program to Test Pig
  • Getting Datasets for Pig Development
  • Loading and Storing Methods Script Interpretation
  • Various Relations
  • Various Pig Command Pig Execution Modes
Topic 2: Installing and Configuring PIG
  • Download and Install from Apache
  • Running Pig
  • Local
  • Cluster
  • Cloud
  • Command Line options
Topic 3: Grunt
  • Understanding Grunt
  • HDFS Command in Grunt
  • Controlling Pig from Grunt
Topic 4: PIG Data Model
  • Problem Statement and data model
  • Input and output
  • Load
  • Store
  • Dump
  • Relational Operators
  • Foreach
  • Filter
  • Group
  • OrderBy
  • Distinct
  • Join
  • Limit
  • Sample
  • Parallel
  • User Defined Functions
  • Registering UDF
  • Defining UDF
  • Calling Static JAVA functions
  • Union
  • OOZIE and Flume Introduction
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Price: ₹ 14,000
Start-End Dates: 22 Oct 16 - 21 Nov 16
Course Duration: 30 days
Instructional Level: Appropriate for All
Live Projects
Doubt Clearing Sessions
Reading Material
EMI Option
Online Support
Post completion course access
Practice Exams
Placement assistance
Refund Policy
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