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Introduction to Big Data and Hadoop Program


Big Data is the data which can not be processed by traditional database systems i.e.Mysql,Sql.
Big data consist of data in the structured ie.Rows and Coloumns format ,semi-structured i.e.XML records and Unstructured format i.e.Text records,Twitter Comments.

Hadoop is an software framework for writing and running distributed applications that processes large amount of data.
Hadoop framework consist of Storage area known as Hadoop Distributed File System(HDFS) and processing part known as MapReduce programming model.
Hadoop Distributed File System is a filesystem designed for large-scale distributed data processing under framework such as Mapreduce.
Hadoop works more effectively with single large file than number of smaller one.
Hadoop mainly uses four input formats-FileInput Format,KeyValueTextInput Format,TextInput Format,NLineInput Format.
Mapreduce is Data processing model consist of data processing primitives called Mapper and Reducer.
Hadoop supports chaining MapReduce programs together to form a bigger job.We will explore various joining technique in hadoop
for simultaneously processing multiple datasets.Many complex tasks need to be broken down into simpler subtasks,each accomplished by an individual Mapreduce jobs.
For example,from the citation data set you may be interested in finding ten most cited patents.A sequence of two Map reduce jobs can do this.

Hadoop clusters which supports for Hadoop HDFS,MapReduce ,Sqoop ,Hive ,Pig , HBase , Oozie , Zookeeper, Mahout , NOSQL , Lucene/Solr,Avro,Flume,Spark,Ambari Hadoop is designed for offline processing and analysis of large-scale data.
Hadoop is best used as a write-once,Read-many-times type of datastore.
With the help of hadoop large dataset will be divided into smaller (64 or 128 MB)blocks that are spread among many machines in the clusters via Hadoop Distributed File System.

The key functions of hadoop are,
1)approachable-Hadoop runs on Huge clusters of appropriate Hardware apparatus.
2)Powerful-Because it is intentional to run on clusters of appropriate Hardware apparatus ,Hadoop is architect with the presumption of repeated hardware malfunctions.It can handle most of such failures.
3)Resizable-Hadoop mearsures sequentially to hold large data by including more nodes to the cluster.
4)Simple-Hadoop allows users to speedly write well-organized parallel codes.

Topics and Structure of Pro Package


1. programmers, architects, and project managers who are into Database/Programming and exploring for great job opportunities in Hadoop

2.Any Graduate/Post-Graduate, who is aspiring a great career towards the cutting edge technologies

Topics and Structure PRO package:

A)RDBMS Vs Hadoop

B)Introduction to Java

C)Introduction to HDFS & Understanding cluster environment

D)Understanding Map-Reduce Basics, Types & Formats

E)HIVE

F)PIG

G)SQOOP

H)Oozie

I)HBASE

J)Flume

K)Mahout

M)Solr

N)Ambari

O)Zookeeper

P)Basics of Spark

Q)Basics of MongoDB

R)Product based Ecommerce application Project(Demo project)

Who can do this Course ?

Fresher
Data Anaslyst
Database Administrators
Business developers
Software Testers

Duration

Three month (Daily 2 hrs...5 days a week)

Career Opportunities:

Big data Engineer
Hadoop Developer
Business Analyst

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What our Students have to say about the training:

Shane Melaugh
“ I have been doing Hadoop Course and I am verge of completing the course.Trainer and Staff of the institute is really good, reason to say this I got quality training through them. I would love to refer my friends to this institute . I had a nice experience."
Prashant Aher
Software Developer
Shane Melaugh
“ I have been doing Hadoop Course and I am verge of completing the course.Trainer and Staff of the institute is really good, reason to say this I got quality training through them. I would love to refer my friends to this institute . I had a nice experience."
Anshika Solanki
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