Hadoop Training in Chennai and 100% placement: Hadoop Training in Chennai

Hadoop Training in Chennai

BIGDATA – HADOOP

Hadoop Developer / Analyst / Hadoop (Java + Non- Java) Track

How we are Different from Others : Covers each topics with Real Time Examples . Covers 10 Real time project and more than 80+ Assignments which is divided into Basic , Intermediate and Advanced . Trainer from Real Time Industry with 10 years experience in DWH. Working as BI and Hadoop consultant having 3+ years in Bigdata & Hadoop real time implementation and migrations.

This is completely hands own training , which covers 90% Practical And 10% Theory .Here in Radical Technologies , we will take all prerequisite like Java ,SQL, which is required to learn Hadoop Developer and Analytical skills. This way We will accommodate technology illiterate and Technical experts in the same session and at the end of the training , they will gain the confidence that , they got upskilled to a different level.

· 8 Domain Based Project With Real Time Data

· 5 POC

· 82 Assignments

· 25 Real Time Scenarios On 16 Node Clusters

· Smart Class

· Basic Java

· DWH Concept

Hadoop Certifications : Radical is accredited with Pearson Vue and Kriterion , We do conduct Exams in every month and we have 100% Passing record for all the students who completed course form Radical technologies . most demanding Hadoop Exams are Hortonworks and Cloudera certifications

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Exam Preparation : After the course We provide all of our candidates free exam preparation session , which will guide them to pass the Respective modules of Hadoop exams.

Registration Process : We never take any registration fee from the candidate without experiencing our training quality.Once you satisfied with the demo , you can register with full payment and avail discount . We have installment facility also.

Bigdata Hadoop Syllabus

For whom Hadoop is?

IT folks who want to change their profile in a most demanding technology which is in demand by almost all clients in all domains because of below mentioned reasons-

· Hadoop is open source (Cost saving / Cheaper)

· Hadoop solves Big Data problem which is very difficult or impossible to solve using highly paid tools in market

· It can process Distributed data and no need to store entire data in centralized storage as it is there with other tools.

· Now a days there is job cut in market in so many existing tools and technologies because clients are moving towards a cheaper and efficient solution in market named HADOOP

· There will be almost 4.4 million jobs in market on Hadoop by next year.

Please refer below mentioned links:

http://www.computerworld.com/article/2494662/business-intelligence/hadoop-will-be-in-most-advanced-analytics-products-by-2015–gartner-says.html

Can I Learn Hadoop If I Don’t know Java?

Yes,

It is a big myth that if a guy don’t know Java then he can’t learn Hadoop. The truth is that Only Map Reduce framework needs Java except Map Reduce all other components are based on different terms like Hive is similar to SQL, HBase is similar to RDBMS and Pig is script based.

Only MR requires Java but there are so many organizations who started hiring on specific skill set also like HBASE developer or Pig and Hive specific requirements. Knowing MapReuce also is just like become all-rounder in Hadoop for any requirement.

Why Hadoop?

· Solution for BigData Problem

· Open Source Technology

· Based on open source platforms

· Contains several tool for entire ETL data processing Framework

· It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.

Course Content ,

Hadoop Introduction

· Why we need Hadoop

· Why Hadoop is in demand in market now a days

· Where expensive SQL based tools are failing

· Key points , Why Hadoop is leading tool in current It Industry

· Definition of BigData

· Hadoop nodes

· Introduction to Hadoop Release-1

· Hadoop Daemons in Hadoop Release-1

· Introduction to Hadoop Release-2

· Hadoop Daemons in Hadoop Release-2

· Hadoop Cluster and Racks

· Hadoop Cluster Demo

· How Hadoop is getting two categories Projects-

· New projects on Hadoop

· Clients want POC and migration of Existing tools and Technologies on Hadoop Technology

· How Open Source tool (HADOOP) is capable to run jobs in lesser time which take longer time in

· Hadoop Storage – HDFS (Hadoop Distributed file system)

· Hadoop Processing Framework (Map Reduce / YARN)

· Alternates of Map Reduce

· Why NOSQL is in much demand instead of SQL

· Distributed warehouse for HDFS

· Most demanding tools which can run on the top of Hadoop Ecosystem for specific requirements in specific scenarios

· Data import/Export tools

Hadoop Installation and Hands-on on Hadoop machine

· Hadoop installation

· Introduction to Hadoop FS and Processing Environment’s UIs

· How to read and write files

· Basic Unix commands for Hadoop

· Hadoop FS shell

· Hadoop releases practical

· Hadoop daemons practical

ETL Tool (Pig) Introduction Level-1 (Basics)

· Pig Introduction

· Why Pig if Map Reduce is there?

· How Pig is different from Programming languages

· Pig Data flow Introduction

· How Schema is optional in Pig

· Pig Data types

· Pig Commands – Load, Store , Describe , Dump

· Map Reduce job started by Pig Commands

· Execution plan

ETL Tool (Pig) Level-2 (Complex)

· Pig- UDFs

· Pig Use cases

· Pig Assignment

· Complex Use cases on Pig

· XML Data Processing in Pig

· Structured Data processing in Pig

· Semi-structured data processing in Pig

· Pig Advanced Assignment

· Real time scenarios on Pig

· When we should use Pig

· When we shouldn’t use Pig

· Live examples of Pig Use cases

Hive Warehouse (Introduction to Hive Warehouse and Differentiation between SQL based Datawarehouse and Hive) Level-1 (Basics)

· Hive Introduction

· Meta storage and meta store

· Introduction to Derby Database

· Hive Data types

· HQL

· DDL, DML and sub languages of Hive

· Internal , external and Temp tables in Hive

· Differentiation between SQL based Datawarehouse and Hive

Hive Level-2 (Complex)

· Hive releases

· Why Hive is not best solution for OLTP

· OLAP in Hive

· Partitioning

· Bucketing

· Hive Architecture

· Thrift Server

· Hue Interface for Hive

· How to analyze data using Hive script

· Differentiation between Hive and Impala

· UDFs in Hive

· Complex Use cases in Hive

· Hive Advanced Assignment

· Real time scenarios of Hive

· POC on Pig and Hive , With real time data sets and problem statements

Map Reduce Level-1 (Basics)

· How Map Reduce works as Processing Framework

· End to End execution flow of Map Reduce job

· Different tasks in Map Reduce job

· Why Reducer is optional while Mapper is mandatory?

· Introduction to Combiner

· Introduction to Partitioner

· Programming languages for Map Reduce

· Why Java is preferred for Map Reduce programming

· POC based on Pig, Hive, HDFS, MR

NOSQL Databases and Introduction to HBase Level-1 (Basics)

· Introduction to NOSQL

· Why NOSQL if SQL is in market since several years

· Databases in market based on NOSQL

· CAP Theorem

· ACID Vs. CAP

· OLTP Solutions with different capabilities

· Which Nosql based solution is capable to handle specific requirements

· Examples of companies like Google, Facebook, Amazon, and other clients who are using NOSQL based databases

· HBase Architecture of column families

Map Reduce Advanced and HBase Level-2 (Complex)

· How to work on Map Reduce in real time

· Map Reduce complex scenarios

· Introduction to HBase

· Introduction to other NOSQL based data models

· Drawbacks of Hadoop

· Why Hadoop can’t work for real time processing

· How HBase or other NOSQL based tools made real time processing possible on the top of Hadoop

· HBase table and column family structure

· HBase versioning concept

· HBase flexible schema

· HBase Advanced

Zookeeper and SQOOP

· Introduction to Zookeeper

· How Zookeeper helps in Hadoop Ecosystem

· How to load data from Relational storage in Hadoop

· Sqoop basics

· Sqoop practical implementation

· Sqoop alternative

· Sqoop connector

· Quick revision of previous classes to fill the gap in your understanding and correct understandings

Flume , Oozie and YARN

· How to load data in Hadoop that is coming from web server or other storage without fixed schema

· How to load unstructured and semi structured data in Hadoop

· Introduction to Flume

· Hands-on on Flume

· How to load Twitter data in HDFS using Hadoop

· Introduction to Oozie

· How to schedule jobs using Oozie

· What kind of jobs can be scheduled using Oozie

· How to schedule jobs which are time based

· Hadoop releases

· From where to get Hadoop and other components to install

· Introduction to YARN

· Significance of YARN

Hue, Hadoop Releases comparison, Hadoop Real time scenarios Level-2 (Complex)

· Introduction to Hue

· How Hue is used in real time

· Hue Use cases

· Real time Hadoop usage

· Real time cluster introduction

· Hadoop Release 1 vs Hadoop Release 2 in real time

· Hadoop real time project

· Major POC based on combination of several tools of Hadoop Ecosystem

· Comparison between Pig and Hive real time scenarios

· Real time problems and frequently faced errors with solution

SPARK and Scala Level-1 (Basics)

· Introduction to Spark

· Introduction to scala

· Basics Features of SPARK and Scala available in Hue

· Why Spark demand is increasing in market

· How can we use Spark with Hadoop Eco System

· Datasets for practice purpose

SPARK and Scala Level-2 (Complex)

· Spark use cases with real time scenarios

· Spark Practical with advanced concepts

· Scala platform with complex use cases

· Real time project use cases examples based on Spark and Scala

· How we can reduce

Hadoop Training in Chennai and 100% placement

Thursday, 28 January 2016

Hadoop Training in Chennai

Hadoop Training in Chennai

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