The document discusses big data analytics using Hadoop. It provides an introduction to big data and the 5 V's of big data. It then discusses limitations of relational database management systems for big data. The document outlines the history and development of Hadoop. It describes the components, architecture and advantages of Hadoop. Some applications of Hadoop for big data analytics are also highlighted along with disadvantages. The conclusion reiterates that Hadoop is an open source tool for handling big data analytics.

1. Presented By
Sarita Bagul
TE Computer
Seat No.T120414208
Under the guidance
Asst.Prof.B.A.Khivsara
A Seminar On

2. Introduction
Literature Survey
Working of Hadoop in Big Data Analytics
Advantages and Disadvantages of Hadoop
Application of Big Data Analytics Using Hadoop
Conclusion
References
Outlines

4. BIG DATA

5. What is Big Data?
“A massive volume of both structured and
unstructured data that is so large that it's difficult to
process with traditional database and software
techniques”.

6. 5 Vs of Big Data

7.  Big data analytics is the process of collecting,
organizing and analyzing large sets of data (called big
data) to discover patterns and other useful information.

Big Data Analytics

9.  In this illustrated that in olden days through RDBMS tools
,the data was less and easily handled by RDBMS but
recently it is difficult to handle huge data, which is
preferred as “big data”.
Relational database management system
Relational Databases Are Not Designed To Handle Change
Cost
No support for complex object such as documents,video,images etc.
Relational databases have limits on field lengths.
No support for unstructured data.

10.  2006 - Yahoo! created Hadoop based on GFS and MapReduce (with Doug Cutting
and team)
 2007 - Yahoo started using Hadoop on a 1000 node cluster
 Jan 2008 - Apache took over Hadoop
 Jul 2008 - Tested a 4000 node cluster with Hadoop successfully
 2009 - Hadoop successfully sorted a petabyte of data in less than 17 hours to
handle billions of searches and indexing millions of web pages.
 Dec 2011 - Hadoop releases version 1.0
 Aug 2013 - Version 2.0.6 is available
 Nov 2014: Release 2.6.0 available
 Dec, 2015: Release 2.6.3 available
 Oct, 2016: Release 2.6.5 available
Old Version Of Hadoop

11.  It limits scalability
 Availability Issue
 Problem with Resource Utilization
 Limitation in running non-MapReduce Application
Disadvantages of old versions of hadoop

12.  25 January, 2017: Release 3.0.0-alpha2
available
 This is the second alpha in a series of planned
alphas and betas leading up to a 3.0.0 GA
release. The intention is to "release early,
release often" to quickly iterate on feedback
collected from downstream users.
Latest Version Of Hadoop

13.  To overcome the disadvantages of RDBMS, Hadoop is
introduced in market.
 Hadoop is an open source, Java-based programming
framework that supports the processing and storage of
extremely large data sets in a distributed computing
environment.
HADOOP

14.  There are many old technologies already present used for big
data handling but each one of them has some advantages and
disadvantages. There are number of technologies are there few of
them are mentioned below:
 Column-oriented databases
 NoSQL databases
 MapReduce
 Hive
 Pig
 WibiData
 PLATFORA
 Apache Zeppelin
 Hadoop
Working Of Hadoop In Big Data Analytics

15. Architecture Of Hadoop

16. There are
two main
components
of Hadoop.
• MapReduce
• HDFS
Components Of Hadoop

18.  NoSQL (originally referring to SQL. or relational.)
database provides a mechanism for storage and
retrieval of data that is modeled in means other than the
tabular relations used in relation databases (RDBMS).
 This is backend database of hadoop.
NoSQL

19. Health Care Applications
IOT
Social Media
Applications of Hadoop

21. Scalable
Cost effective
Flexible
Fast
Resilient to failure
Advantages of Hadoop

22. Security Concerns
Not Fit for Small Data
Vulnerable By Nature
Disadvantages of Hadoop

23.  Hadoop which is an open source software is a popular
framework tool to handle the big data and used for big
data analytics.
Conclusion

24.  [1] Sethy, Rotsnarani, and Mrutyunjaya Panda "Big Data Analysis using Hadoop:
A Survey." International Journal 5.7 (2015).
 [2] Bhosale, Harshawardhan S., and Devendra P. Gadekar. "A Review Paper on
BigData and Hadoop." International Journal of Scientic and Research Publications
4.10 (2014): 1.
 [3] ]http://research.ijcaonline.org/volume108/number12/pxc3900288.pdf
 [4] https://en.wikipedia.org/wiki/Big data
 [5] Tom White,.Hadoop, The denitive guide.,OfReilly,3rd Edition
 [6] https://www.google.co.in/?gfe rd=cr&ei=ayKnWJWmDe x8AfDyLnQDg&gws
rd=ssl#q= hadoop + tutoria+ppt
 [7] https://www.google.co.in/?gfe rd=cr&ei=ayKnWJWmDe x8AfDyLnQDg&gws
rd=ssl#q= hadoop
References

25. [8] Bernice Purcell “The emergence of gbig datah technology and analytics “Journal of Technology
Research 2013.
[9] https://www.google.co.in/search?q=Hadoop%2 C + a + distributed + framework +for + Big + Data
&ie=utf-8&oeutf-8 &client = firefox ab&gfe rd = cr&ei =glXJWJyDMIKM4gL89IPACg
[10] Gupta, Bhawna, and Kiran Jyoti. "Big data analytics with hadoop to analyze targeted attacks
on enterprise data." (IJCSIT) International Journal of Computer Science and Information
Technologies 5.3 (2014): 3867-3870.
[11] Russom, Philip. "Big data analytics." TDWI best practices report, fourth quarter (2011): 1-35.
[12] http://blogs.mindsmapped.com/bigdatahadoop/hadoop-advantages-and-disadvantages/
[13]http://www.tutorialspoint.com/articles/what-is-nosql-and-is-it-the-next-big-trend-in-databases
[14] http://www.tutorialspoint.com/MongoDB/MongoDB-Application.htm
[15]http://www.w3resource.com/mongodb/nosql.php
[16] https://www.dezyre.com/article/5-healthcare-applications-of-hadoop-and-big-data/85
[17] https://www.tutorialspoint.com/hadoop/hadoop_enviornment_setup.htm