The document discusses the evolution and significance of big data, detailing its increase from 5 exabytes in 2003 to 2.5 exabytes generated daily by 2011. It defines key concepts such as big data, business intelligence, statistics, machine learning, and predictive analytics while highlighting its potential impacts on security, society, and various sectors including commerce and sports. Additionally, it mentions major milestones in big data development, such as the creation of Hadoop and Spark.

1. B I G D A T A L I T T L E
D E V I C E S
W H A T I T W I L L D O T O U S A N D F O R U S

2. W H A T I S B I G D A T A ?
0 - 2 0 0 3
5 exabytes
2 0 1 1
2.5 exabytes per day

3. P E R S P E C T I V E S
1MB 1GB 1TB 2PB 5EB

4. W H E R E ’ S I T C O M I N G F R O M ?
Source: domo.com 2012

5. W H A T D O E S I T L O O K L I K E ?

6. D E F I N I T I O N S
• Big Data: unstructured data, don’t know what questions are yet
• Business Intelligence: structured data, know what the questions
you want answered
• Statistics: structured data, not realtime, no action taken as a
result
• Machine Learning: creation of algorithms and applying them to
data sets in an attempt to learn from data
• Predictive Analytics: extracting existing data to predict trends

7. W H Y N O W ?
• 2003: Doug Cutting & Mike Cafarella, Nutch
• 2004:Google Labs: Map Reduce
• 2006:Doug Cutting moves to Yahoo and creates Hadoop
• 2008: Yahoo open sources Hadoop, Apache Software Foun
• 2009: Matei Zaharia starts Spark at UC Berkley
• 2013: Spark open sourced under Apache

8. M A P R E D U C E
Traditional / Sequential
Map
Reduce

9. S P A R K
x 100
Map
Reduce

10. C A S E S
W H A T I T W I L L D O T O U S

11. S E C U R I T Y - P R I V A C Y
N S A P R I S M

12. P R O F I L I N G

13. V U L N E R A B I L I T
Y
• Target
• Home Depot
• Michaels
• Blue Cross Blue Shield
• Sony Entertainment

14. S O C I E T Y

15. C O M M E R C E
A M A Z O N D A S H

16. C O M M E R C E
A M A Z O N

17. C A S E S
W H A T I T W I L L D O F O R U S

18. S P O R T S
S A B E R M E T R I C S ( M O N E Y B A L L )
95%
5%

19. P R O D U C T I V I T Y
G O O G L E N O W

20. P O L I T I C S
O B A M A C A M P A I G N 2 0 1 2

21. S C I E N C E
M O N T E R E Y B A Y A Q U A R I U M R E S E A R C H I N S T I T U T E

22. H E A L T H
A P P L E R E S E A R C H K I T
xt, Stanford says that it would normally take a national year-long effort to get that kind of scale. The flood of dat

23. M O R E R E A D I N G
• http://www.domo.com/blog/2014/04/data-never-sleeps-2-0/
• http://www.redorbit.com/education/reference_library/general-2/history-of/1113190638/the-history-of-
mobile-phone-technology/
• http://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/
• http://www.wired.com/2015/04/robots-roam-earths-imperiled-oceans/?mbid=nl_041315
• http://www.allbusiness.com/what-does-your-supermarket-know-about-you-15611312-1.html
• http://www.geekwire.com/2015/baseball-analytics-mystery-mlb-team-uses-a-cray-supercomputer-to-
crunch-data/
• http://www.geekwire.com/2015/this-big-data-startup-just-raised-cash-to-analyze-driver-behavior-creating-
safety-scores-for-individual-
motorists/?utm_source=GeekWire+Daily+Digest&utm_campaign=20eb1892b3-daily-digest-
email&utm_medium=email&utm_term=04e93fc7dfd-20eb1892b3-
233387065&mc_cid=20eb1892b3&mc_eid=7b61e5049a
• http://www.newyorker.com/culture/culture-desk/the-horror-of-amazons-new-dash-button
• https://www.amazon.com/oc/dash-button
• http://harvardmagazine.com/2014/03/why-big-data-is-a-big-deal http://www.businessinsider.com/big-data-
is-growing-thanks-to-mobile-2013-1http://venturebeat.com/2015/04/03/how-microsofts-using-big-data-to-
predict-traffic-jams-up-to-an-hour-in-advance/
• http://www.engadget.com/2015/04/13/ibm-watson-health-
cloud/?utm_source=Feed_Classic_Full&utm_medium=feed&utm_campaign=Engadget&?ncid=rss_full

24. ?