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INN530 - Assignment 2, Big data and cloud computing for management
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This is my delivery for assignment 2 in INN530 - Online Information Systems at QUT, created by Simen Fivelstad Smaaberg

This is my delivery for assignment 2 in INN530 - Online Information Systems at QUT, created by Simen Fivelstad Smaaberg

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INN530 - Assignment 2, Big data and cloud computing for management Presentation Transcript

  • 1. Big data and cloud computing formanagementBySimen Fivelstad Smaaberg (n8661260)
  • 2. Abstract Big data What is it? What possibilities and challenges lays within? Cloud computing Software as a Service Platform as a Service Infrastructure as a Service How does big data relate to cloud computing? Google / Facebook How does Google and Facebook handle big data? What big data/cloud services do they offer for others touse?
  • 3. Background Internet today: a social web Huge amounts of data are created by users daily Creates your digital footprint Need for methods to handle all these data The data is used for analysis purposes Improve customer experiences Increase revenues
  • 4. Big Data The vast volume of data in existence (Arthur, E2013) Tweets, likes, videos, images, comments and so on Group of collected information, spans 3 V’s of datamanagement (Gartner 2011)Figure 1: Big data 3V’s ( Datameer 2013)
  • 5. Volume Big data is Big Exists in one size: large Challenges Physical storage space Logical structuring Scaling
  • 6. Volume Big data is Big Exists in one size: large Challenges Physical storage space Logical structuring Scaling Opportunities Finding trends, patterns and relationships between data Possibilities for in depth analysis
  • 7. Variety Challenges Infrastructure to handledifferent kinds of media isrequired. Challenging forengineersFigure 2: Big Data (Orange 2011)
  • 8. Variety Challenges Infrastructure to handledifferent kinds of media isrequired. Challenging forengineers Opportunities Finding patterns andrelationships betweendifferent types of dataFigure 2: Big Data (Orange 2011)
  • 9. Velocity Big data can have different kinds of time-sensitivity Real time vs non real time Challenges Infrastructure that can handle different kinds of time-sensitivity
  • 10. Velocity Big data can have different kinds of time-sensitivity Real time vs non real time Challenges Infrastructure that can handle different kinds of time-sensitivity Opportunities Combine slow moving data with fast moving timeconstrained data to give the user a better user experience
  • 11. Big data success story: Santam Insurance About South Africas largest short-term insurance company Problem 6-10% of premium revenue were fraud Solution Big Data prediction analysis
  • 12. Big data success story: Santam Insurance About South Africas largest short-term insurance company Problem 6-10% of premium revenue were fraud Solution Big Data prediction analysis Result First four months: 1.98 million USD saved First three years: ROI of 244% Insurance fraud syndicate disceovered
  • 13. « Big data has the potential to change the waygovernments, organizations, and academicinstitutions conduct business and makediscoveries, and its likely to change how everyonelives their day-to-day lives »– Susan Hauser, VP Microsoft Enterprise and partnergroup (Microsoft Enterprise team 2013)
  • 14. Cloud computing Running applications elsewhere and accessing them through yourcomputer You get an “infinite” amount of storage space and computing power You pay for what you useFigure 3: Cloud computing
  • 15. Big Data and Cloud Computing Big data requires enormous amounts of storagespace Costly to build and maintain Huge engineering challenges
  • 16. Big Data and Cloud Computing Big data requires enormous amounts of storagespace Costly to build and maintain Huge engineering challenges Solution: Cloud Computing! Put your data and programs into “the cloud” Avoid the hardware problem of big data Pay for the computing power you actually use, Opens up for smaller companies stepping into big data analysis
  • 17. Big Data and Cloud Computing Big data requires enormous amounts of storagespace Costly to build and maintain Huge engineering challenges Solution: Cloud Computing! Put your data and programs into “the cloud” Avoid the hardware problem of big data Pay for the computing power you actually use, Opens up for smaller companies stepping into big data analysis Issues: Privacy and trust You put your data into someone elses hand Is that someone trustworthy?
  • 18. Google and Hadoop 2004: Google revolutionized the field of Big Dataand Cloud Computing Released papers describing how they handled thesetopics
  • 19. Google and Hadoop 2004: Google revolutionized the field of Big Dataand Cloud Computing Released papers describing how they handled thesetopics From this Yahoo spawned Hadoop Platform that can process and analyse huge amounts ofdata on interconnected commodity servers Great fit for cloud computing Data is spread out and duplicated across servers Data is analysed in parallel through MapReduce Backbone of Twitter, Facebook, Yahoo and eBay
  • 20. Google Huge competitor in the field of big data and cloudcomputing Big data used internally Index searches Provide email services Provide advertizing External Big data and cloud services Software as a Service: Google docs, Gmail etc Platform as a Service: Google app engine Infrastructure as a Service: Google compute engine Gives developers access to the same infrastructure google itselfis run on
  • 21. Facebook Forerunner in the field of Big Data Handling massive amounts of data daily 2.5 billion status updates, wall posts, photos, videos andcomments 2.7 billion likes 300 million uploaded photos 500 Tb new integrated data every day (2012)
  • 22. Facebook Forerunner in the field of Big Data Handling massive amounts of data daily 2.5 billion status updates, wall posts, photos, videos andcomments 2.7 billion likes 300 million uploaded photos 500 Tb new integrated data every day (2012) Facebook is run on top of Hadoop 100 Petabytes of storage Underpins analysis and everyday services New data is put into one of their Hadoop clusters physicallyresiding in one of their data centers Data is analysed when needed or at specific intervals(hourly/daily) through MapReduce
  • 23. Facebook Insight Tool that provides page owners (both facebook pages andordinary web pages) with metrics about their content.(Facebook 2013) Number of visits Facebook referrals Visitor demographics (age, gender, location, language) Connects Facebook’s big data with users visiting your page toprovide metrics that can be used to improve your business’sonline performance Generated through Facebook’s Hadoop clusterFigure 4: User demographics (Campalyst 2012)
  • 24. Facebook graph search Search engine for your social circle (Bea, F 2013) Allows for search on relationships betweenpeople, likes, comments, photos etc. Possible because ofFacebook’s big data Privacy concernsFigure 5: Facebook restaurant search Figure 6: Facebook TV show search
  • 25. The Future Big data and cloud computing is in constant change More and more usage areas are found Medical diagnostics Weather forecasts Particle physics Fraud prevention and detection Etc. Prediction: We have barely seen the start of itsdominance
  • 26. References Arthur, E. 2013. "Big Data“. Alaska Business Monthly, vol. 29, no. 1, pp. 72-72. Retrieved fromhttp://search.proquest.com.ezp01.library.qut.edu.au/docview/1271622055 Gartner. 2011. “Solving Big Data Challenge involves more than just managing volumes of data”.Accessed June 4, 2013. http://www.gartner.com/newsroom/id/1731916 Strickland, J. “How Cloud Computing Works”. Accessed June 6, 2013.http://computer.howstuffworks.com/cloud-computing/cloud-computing.htm Gartner. “Software as a Service (SaaS)”. Accessed June 6. 2013. http://www.gartner.com/it-glossary/software-as-a-service-saas/ Chong, R. 2011. “The perfect marriage: Hadoop and Cloud”. Accessed June 4, 2013.http://thoughtsoncloud.com/index.php/2011/10/the-perfect-marriage-hadoop-and-cloud/ Microsoft Enterprise Team. “The Big Bang: How the Big Data Explosion Is Changing theWorld”. Last Modified March 27, 2013.http://www.microsoft.com/enterprise/it-trends/big-data/articles/The-Big-Bang-How-the-Big-Data-Explosion-Is-Changing-the-World.aspx#fbid=8RIFw1BLCG2 Metz, C. 2011. “How Yahoo Spawner Hadoop, the Future of Big Data”. Accessed June 5, 2013.http://www.wired.com/wiredenterprise/2011/10/how-yahoo-spawned-hadoop/all/1 Big Data Insights. 2013. “How Facebook uses Hadoop and Hive”. Accessed June 05, 2013.http://hortonworks.com/blog/how-facebook-uses-hadoop-and-hive/ Facebook. “Insights”. Last modified May 30, 2013.https://developers.facebook.com/docs/insights/ Bea, F. 2013. “How Facebook’s Graph Search Works…Sort Of". Accessed June 05, 2013.http://www.digitaltrends.com/social-media/how-facebook-graph-search-works/ IBM. 2013. “IBM Business Analytics SPSS: Santam insurance”. Last modified May 28, 2013.http://www-01.ibm.com/software/success/cssdb.nsf/CS/SANS-985HX2?OpenDocument&Site=default&cty=en_us
  • 27. References - Illustrations Datameer. 2013. “What is Big Data?”. DigitalImage. Viewed June 8, 2013.http://www.datameer.com/product/big-data.html Orange. 2011. “Analyst insight”. Digital Image.Viewed June 11, 2013. http://www.orange-business.com/en/magazine/analyst-insight-december-2011 Campalyst. 2012. “How to measure website visitors’demographics: hidden Facebook Insights gem”.Digital Image. Viewed June 11, 2013.http://blog.campalyst.com/2012/10/10/how-to-measure-website-visitors-demographics-hidden-facebook-insights-gem/