Hadoop Big Data Demo On 4Th Feb At 7 Am(Ist)

February 2, 2014

Email: [email protected]

Contact No: +91 9036 298 699 (or) +91 78 29 29 7899

Hadoop big data demo on 4th feb at 7 AM(IST) || 3rd feb at 8.30 PM(EST)

www.onlinetrainings9.com

Hadoop Introduction

 Introduction to Data and System

 Data Lifecycle Management

 Data Properties

 Types of Data

 Introduction of system

 Problems with traditional large-scale systems

 Types of Systems & scaling

 What is Big Data

 Challenges in Big Data

 Challenges in Traditional Application

 New Requirements

 What is Hadoop

 Brief history of Hadoop

 Features of Hadoop

 Hadoop v/s RDBMS

 Hadoop Ecosystem’s overview

Administrate Hadoop Distribute File System(HDFS)

 Concepts

 Blocks

 Replication

 Version File

 Safe mode

 Namespace IDs

 Reading and Writing in HDFS

 Understanding Name Node

 Understanding Data Node

 Understanding Secondary Name Node

 Understanding Job Tracker

 Understanding Task Tracker

 HDFS Shell Commands

 Hadoop Admin Commands

 Hands On Exercise

 Accessing HDFS using API

 Understanding HDFS Java classes and methods

 HDFS Nextgeneration Concepts.

 Hands On Exercise

Setting up Hp Hp Hadoop Cluster for Apache Hadoop

 Installation in detail

 Creating Ubuntu image in VMware

 Downloading Hadoop

 Installing SSH

 Configuring Hadoop

 Download ,Installation & Configuration Hive

 Download ,Installation & Configuration Pig

 Download ,Installation & Configuration Sqoop

 Download ,Installation & Configuration Hive

 Installing MySql in Hadoop cluster.

 Download and work with Cloudera Image.

Configuring Hadoop in Different Modesdoop

 Local Mode

 Running without HDFS

 Pseudo-distributed Mode

 Running all daemons in a single node

 Fully distributed mode

 Running daemons on dedicated nodes

Cluster Maintenance

 Managing Hadoop Processes

 Starting and Stopping Processes with Init Scripts

 Starting and Stopping Processes Manually

 HDFS Maintenance Tasks

 Adding a Datanode

 Decommissioning a Datanode

 Checking Filesystem Integrity with fsck

 Balancing HDFS Block Data

 Dealing with a Failed Disk

 MapReduce Maintenance Tasks

 Adding a Tasktracker

 Decommissioning a Tasktracker

 Killing a MapReduce Job

 Killing a MapReduce Task

 Dealing with a Blacklisted Tasktracker

Map Reduce Programming

 Understanding block and input splits

 Common Input and Output Formats

 MapReduce Data types

 Understanding Writable and WritableComparable (Introduction)

 Data Flow in MapReduce Application

 Understanding MapReduce problem on real datasets(stocks).

 MapReduce Skeleton in Details

 Writing MapReduce Application

o Understanding Mapper function

o Understanding Reducer Function

o Understanding Driver

 Understanding Tool Runner

 Hands on Exercise

 MapReduce Continued

 Using Combiner

 Using Distributed Cache

 Passing the parameters to mapper and reducer

 Hands On Exercise

 Writing Custom key values

 Hands On Exercise

 Designed Use Cases for common problems.

Advanced MapReduce Programming

 MapReduce Chaining.

 Customized Input Formats and Output Formats

Monitoring and debugging on a Production Cluster

 Counters

 Skipping Bad Records

 Running in local mode

Tuning for Performance in MapReduce

 Reducing network traffic with combiner

 Partitioners

 Reducing the amount of input data

 Using Compression

 Reusing the JVM

 Running with speculative execution

Hive

 Hive concepts

 Hive architecture

 Install and configure hive on cluster

 Different type of tables in hive

 Hive library functions

 Buckets

 Partitions

 Joins in hive

o Inner joins

o Outer Joins

 Hive UDF

PIG

 Pig basics

 Install and configure PIG on a cluster

 PIG Library functions

 Pig Vs Hive

 Write sample Pig Latin scripts

 Modes of running PIG

o Running in Grunt shell

 Running as Java program

 PIG UDFs

Sqoop

 Install and configure Sqoop on cluster

 Connecting to RDBMS

 Installing Mysql

 Import data from Oracle/Mysql to hive

 Export data to Oracle/Mysql

 Internal mechanism of import/export

HBase

 HBase concepts

 HBase architecture

 Region server architecture

 File storage architecture

 HBase basics

 Column access

 Scans

 HBase use cases

 Install and configure HBase on a multi node cluster

 Create database, Develop and run sample applications

 Access data stored in HBase using clients like Java, Python and Pearl

 Map Reduce client to access the HBase data

HIGHLIGHTS

 INTERVIEW QUESTIONS
 SAMPLE RESUMES

February 2, 2014

any tool free training needed

February 2, 2014

any tool free training needed

February 2, 2014

any tool free training needed

February 2, 2014