Seminar on Hadoop in Big Data Analytics
Presented by Sarita Bagul, this seminar delves into the working, advantages, and disadvantages of Hadoop in Big Data Analytics. Explore the application of Big Data Analytics using Hadoop with references to relational database management systems and the evolution of Hadoop from its inception by Yahoo to its latest releases.
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Presentation Transcript
A Seminar On Presented By Sarita Bagul TE Computer Seat No.T120414208 Under the guidance Asst.Prof.B.A.Khivsara
Outlines Introduction Literature Survey Working of Hadoop in Big Data Analytics Advantages and Disadvantages of Hadoop Application of Big Data Analytics Using Hadoop Conclusion References
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 .
Big Data Analytics Big Data Analytics 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.
Relational database management system 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 bigdata . 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.
Old Version Of Hadoop 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
Disadvantages of old versions of hadoop It limits scalability Availability Issue Problem with Resource Utilization Limitation in running non-MapReduce Application
Latest Version Of Hadoop Latest Version Of Hadoop 25 January, 2017: Release 3.0.0 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. 25 January, 2017: Release 3.0.0- -alpha2 available alpha2
HADOOP 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.
Working Of Hadoop In Big Data Analytics 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
Components Of Hadoop There are two main components of Hadoop. MapReduce HDFS
NoSQL NoSQL 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.
Applications of Hadoop Applications of Hadoop Health Care Applications IOT Social Media
Advantages of Hadoop Advantages of Hadoop Scalable Cost effective Flexible Fast Resilient to failure
Disadvantages of Hadoop Disadvantages of Hadoop Security Concerns Not Fit for Small Data Vulnerable By Nature
Conclusion Hadoop which is an open source software is a popular framework tool to handle the big data and used for big data analytics.
References [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
[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
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