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Hadoop at MakingITsimplest Institute





₹ 15,000   ( ₹ 18,000  | 15% Off)
503/1, 2nd cross, SN arcade, SP Layout, ,
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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.

Pre-Requisites:-

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 :

  • Understand Hadoop Architecture
  • Understand the concept of Hadoop distributed file system
  • Concepts of MapReduce
  • Working with Sqoop, Hive and Pig
  • Implement Hbase

Module 1: Introduction

Topics covered on introduction
  • Introduction
    • What is Hadoop
    • History of Hadoop
    • How Hadoop name was given
    • Problems with Large-Scale systems and Need for Hadoop
    • Where Hadoop is being used
    • Understanding distributed systems and Hadoop
    • Hands-On Exercise: Using HDFS commands
    • Distributed computing
    • Parallel Computing
    • Concurrency
    • Data Past, Present and Future
    • Computing Past, Present and Future
    • NoSQL
    • Hadoop Streaming
    • Distributing Debug Scripts
    • Getting Started with Eclipse
  • Hadoop Stack
    • HIVE and Pig
    • HDFS
  • Lab 1: Hadoop Hands-on
    • Installing Hadoop Single Node Cluster(CDH4)
    • Understanding Hadoop configuration files

Module 2: HDFS Introduction

Topics covered on HDFS
  • Architecture
  • File Systems
  • 5 daemons of Hadoop
  • Name node and its functionality
  • Data node and its functionality
  • Job Tracker and its functionality
  • Task trace and its functionality
  • Secondary Name node and its functionality
  • Data Storage in HDFS
    • Introduction on Blocks
    • Data Replication

Module 3: MapReduce

Topic 1: Understanding MapReduce
  • How MapReduce works
  • Data flow in MapReduce
  • Map operation
  • Reduce operation
  • MapReduce Program in JAVA using ECLIPSE
  • Counting Words with Hadoop-Running your first Program
  • Writing MapReduce drivers, Mappers and Reducers in JAVA
  • Creating Input and Output Format in MapReduce Job
    • Text Input Format
    • Key Value Input Format
    • Sequence File Input Format
  • Real World “MAPREDUCE” Problems
  • MapReduce Job
  • Java word count code walkthrough
  • How to debug MapReduce Jobs in Local Pseudo cluster mode
  • Combiner (Mini Reducer) and Partioner
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
  • Installing Sqoop
  • Configure Sqoop
  • Import RDMS data to Hive using Sqoop
  • Export from Hive to RDMS using Sqoop
  • Updating an existing Data Set
  • Exporting into a subset of columns
  • Transferring an entire table
  • Specifying a target directory
  • Importing only a subset of data
  • Protecting your Password
  • Using a file format other than CSV
  • Compressing Imported data
  • Controlling Parallelism
  • Importing all your tables

Module 5: HIVE

Topic 1: HIVE Introduction, Installation and Configuration
  • Running Hive
  • Configuration Management overview
  • Runtime Configuration
  • Hive, MapReduce, Local- Mode
  • Loading data into Hive tables
  • Mathematical functions in Apache hive
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
  • Hbase Introduction
  • Where to use Hbase
  • Hbase basics
    • Column families
    • Scans
  • Hbase Architecture
    • Master Server
    • Regions
    • Region Server
  • Hbase and RDMS
  • DDL Operations
  • Hbase Create Data
  • Hbase Read Data

Module 7: PIG

Topic 1: PIG Introduction
  • Pig and Dataflow
  • Pig philosophy
  • Pig and Hadoop
  • Pig vs. Hive
  • Why Pig
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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making IT simplest hadoop bangalore course
Price: ₹ 15,000
Start-End Dates: 15 Oct 16 - 14 Nov 16
Course Duration: 30 days
Discount: 25%
Instructional Level: Appropriate for All
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