Demystifying Big Data
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The document discusses 'big data', defining it as large and complex datasets that are difficult to process with traditional tools, and highlights the rapid growth of data generation over time. It contrasts big data with little data, emphasizing the unstructured and disparate nature of big data compared to the rigid structure of little data. The document also outlines applications and users of big data across various sectors, as well as posing critical questions regarding data storage, costs, and utilization.
Demystifying Big Data
- 1. Demystifying Big Data Aaron Weber, Product Architect Spiral16
- 2. Since 2007, Spiral16′s group of developers, data analysts and researchers have been quietly building one of the most powerful data mining platforms on the planet, backed up by one of the most experienced social data analysis teams in the industry. Spiral16′s social media and web research platform and data analysis services are designed to help everyone from CEOs to CMOs, marketing and PR agencies, and researchers. Demystifying Big Data 11/8/2012 2
- 3. So what is Big Data? Demystifying Big Data 11/8/2012 3
- 4. So what is Big Data? “A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools.” - Wikipedia Demystifying Big Data 11/8/2012 4
- 5. So what is Big Data? Exabytes Created By Year 3000 2500 2000 1500 1000 500 0 2006 2007 2008 2009 2010 2011 2012 Demystifying Big Data 11/8/2012 5
- 6. So what is Big Data? In 2008 we were generating as much stored data from the dawn of civilization to 2003 every two days. And that rate is predicted to double every two years. Demystifying Big Data 11/8/2012 6
- 7. So what is Big Data? Demystifying Big Data 11/8/2012 7
- 8. So what is Big Data? Inevitable Demystifying Big Data 11/8/2012 8
- 9. Big Data vs Little Data Little Data = Relational Databases Demystifying Big Data 11/8/2012 9
- 10. Big Data vs Little Data Big Data = Non-Relational Databases Demystifying Big Data 11/8/2012 10
- 11. Big Data vs Little Data Demystifying Big Data 11/8/2012 11
- 12. Big Data vs Little Data • Structured Demystifying Big Data 11/8/2012 12
- 13. Big Data vs Little Data • Structured • Organized Demystifying Big Data 11/8/2012 13
- 14. Big Data vs Little Data • Structured • Organized • Hierarchical Demystifying Big Data 11/8/2012 14
- 15. Big Data vs Little Data • Structured • Organized • Hierarchical • Rigid Demystifying Big Data 11/8/2012 15
- 16. Big Data vs Little Data Demystifying Big Data 11/8/2012 16
- 17. Big Data vs Little Data • Unstructured Demystifying Big Data 11/8/2012 17
- 18. Big Data vs Little Data • Unstructured • Disparate Demystifying Big Data 11/8/2012 18
- 19. Big Data vs Little Data • Unstructured • Disparate • Non-Hierarchical Demystifying Big Data 11/8/2012 19
- 20. Big Data vs Little Data • Unstructured • Disparate • Non-Hierarchical • Reusable Demystifying Big Data 11/8/2012 20
- 21. So why Big Data? • Find trends in existing data Demystifying Big Data 11/8/2012 21
- 22. So why Big Data? • Find trends in existing data • Better consumer targeting Demystifying Big Data 11/8/2012 22
- 23. So why Big Data? • Find trends in existing data • Better consumer targeting • Predictive analysis Demystifying Big Data 11/8/2012 23
- 24. So why Big Data? • Find trends in existing data • Better consumer targeting • Predictive analysis • Making better use of what you already know Demystifying Big Data 11/8/2012 24
- 25. Who is using Big Data? Demystifying Big Data 11/8/2012 25
- 26. Who is using Big Data? o Financial Services o Healthcare o Retail o Marketing o Politics Demystifying Big Data 11/8/2012 26
- 27. The Business of Big Data Demystifying Big Data 11/8/2012 27
- 28. The Business of Big Data Vendor (Founded) Founded Funding (in $US mil.) # of Institutional Rounds Investors SAC Capital, The Founders Fund, Glynn Capital, In-Q-Tel, Reed Elsevier Palantir 2004 $301 7 Ventures, Ulu Ventures, Youniversity Ventures and Jeremy Stoppelman Mu Sigma 2004 $133 2 General Atlantic and Sequoia Capital Silver Lake Sumeru, Accel-KKR, Invus Opera Solutions 2004 $84 1 Financial Advisors, JGE Capital and Tola Capital Accel Partners, Greylock Partners and Cloudera 2008 $81 4 Meritech Capital Partners New Enterprise Associates, Sequoia 10gen 2008 $73.4 5 Capital, Flybridge Capital and Union Square Ventures Amazon, Menlo Ventures, Mohr Davidow Ventures, Bay Partners, ParAccel 2005 $73 5 Walden International, Tao Venture Capital Partners and Silicon Valley Bank Andreesen Horowitz, General Catalyst, O’Reilly AlphaTech Ventures, Windcrest GoodData 2007 $53.5 3 Partners, Tenaya Capital and Next World Capital Ignition Partners, August Capital, JK&B Splunk(1) 2003 $40 3 and Sevin Rosen Funds Meritech Capital, Lightspeed Venture DataStax 2010 $38.7 3 Partners, Sequoia Capital and Crosslink Capital 1010data 2000 $35 1 Norwest Venture Partners Demystifying Big Data 11/8/2012 28
- 29. The Business of Big Data Demystifying Big Data 11/8/2012 29
- 30. So what is Big Data? Inevitable Demystifying Big Data 11/8/2012 30
- 31. So what is Big Data? Inevitable And that’s a good thing. Demystifying Big Data 11/8/2012 31
- 32. Big Data’s Big Questions o What about the data storage and utilization we already have? o How do we know if our data is Big Data? o What are the primary costs of big data? o What answers can I get from our data? o Where do we begin? Demystifying Big Data 11/8/2012 32
- 33. Better data. Better decisions. 7171 West 95th Street Suite 310 Overland Park, KS 2208 913.944.4500 www.spiral16.com Aaron Weber aaron.weber@spiral16.com
Editor's Notes
- #5: So how large and complex are we talking about?
- #6: In fact we’ve moved past exabytes into zetabytes. To put this in perspective: That’s 2.7 TRILLION gigabytes of data.
- #7: So what is Big Data?
- #8: There is simply no way with our current levels of technology to process data in the manner we’re used to. Big Data is the technological path we have to take if we have any desire to make sense of the world we’re creating every day.
- #9: There is simply no way with our current levels of technology to process data in the manner we’re used to. Big Data is the technological path we have to take if we have any desire to make sense of the world we’re creating every day.
- #10: Little Data is a misnomer here. In the real world we’re still talking about massive sets of data. Warehouses full of it, in fact. What we’re really talking about is non-relational vs relational databases. An illustration:
- #11: Little Data is a misnomer here. In the real world we’re still talking about massive sets of data. Warehouses full of it, in fact. What we’re really talking about is non-relational vs relational databases. An illustration:
- #12: Little Data is a misnomer here. In the real world we’re still talking about massive sets of data. Warehouses full of it, in fact. What we’re really talking about
- #13: But more importantly, Big Data is something else:
- #14: But more importantly, Big Data is something else:
- #15: But more importantly, Big Data is something else:
- #16: But more importantly, Big Data is something else:
- #17: But more importantly, Big Data is something else:
- #18: But more importantly, Big Data is something else:
- #19: But more importantly, Big Data is something else:
- #20: But more importantly, Big Data is something else:
- #21: This last one is the important one: Non-structured data left in its original state is infinitely reusable. Instead of dozens or hundreds of silos of information, you can reduce data (and management) duplication for a unified pool of information that can be used for vastly different ends.
- #22: So how large and complex are we talking about?
- #23: So how large and complex are we talking about?
- #24: So how large and complex are we talking about?
- #25: So how large and complex are we talking about?
- #28: Gartner’s Hype Cycle – The Peak of Inflated Expectations
- #33: So how large and complex are we talking about?