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Finding out more from your
advertising campaigns using
       data analytics
1 instance for 100 hours
1 instance for 100 hours
       Video encoding
  Customer activity analysis
       Twitter parsing
             …
1 instance for 100 hours
            =
100 instances for 1 hour
Why would you need 100
     instances?
      (or even more)
Big Data
Many definitions

Very large volume           Three V’s:            Social
 with low density            Velocity        interactions and
  of information             Variety         web activity data
                             Volume


                     Amongst many others..
Storage       Big Data                Compute
           Unconstrained data growth


                                         95% of the 1.2 zettabytes of
                               ZB        data in the digital universe is
                                         unstructured
                                         70% of of this is user-
                           EB            generated content
                                         Unstructured data growth
                                         explosive, with estimates of
                  PB                     compound annual growth
                                         (CAGR) at 62% from 2008 –
GB    TB                                 2012.
                                                               Source: IDC
Storage          Big Data              Compute
                     Where does it come from?




Web sites                                                    Sensor data
Blogs/Reviews/Emails/Pictures                   Weather, water, smart grids
Social Graphs                                             Images/videos
Facebook, Linked-in, Contacts                      Traffic, security cameras
Application server logs                                            Twitter
Web sites, games                      50m tweets/day 1,400% growth per
                                                                  year
Why now?
Storage          Big Data            Compute
                            Why now?




Web sites                                                  Sensor data
Blogs/Reviews/Emails/Pictures                 Weather, water, smart grids
Social Graphs                                           Images/videos
Facebook, Linked-in, Contacts                    Traffic, security cameras
Application server logs                                          Twitter
Web sites, games                       50m tweets/day 1,400% growth per
                                                                   year
Storage          Big Data            Compute
                            Why now?




Web sites                                                  Sensor data
                                              Weather, water, smart grids
Mobile connected world
Blogs/Reviews/Emails/Pictures
Social Graphs                                           Images/videos
Facebook, Linked-in, Contacts                    Traffic, security cameras
            (more people using, easier to collect)
Application server logs                                          Twitter
Web sites, games                       50m tweets/day 1,400% growth per
                                                                   year
Storage          Big Data            Compute
                            Why now?




Web sites                                                  Sensor data
                                              Weather, water, smart grids
  More aspects of data
Blogs/Reviews/Emails/Pictures
Social Graphs                                           Images/videos
Facebook, Linked-in, Contacts                    Traffic, security cameras
            (variety, depth, location, frequency)
Application server logs                                          Twitter
Web sites, games                       50m tweets/day 1,400% growth per
                                                                   year
Storage          Big Data            Compute
                            Why now?




Web sites                                                  Sensor data
                                              Weather, water, smart grids
Possible to understand
Blogs/Reviews/Emails/Pictures
Social Graphs                                           Images/videos
Facebook, Linked-in, Contacts                    Traffic, security cameras
            (not just answer specific questions)
Application server logs                                          Twitter
Web sites, games                       50m tweets/day 1,400% growth per
                                                                   year
What’s different?
We can collect more
There is more
And data has gravity…
Storage   Big Data               Compute
          Data has gravity




App                Data                               App




                             http://blog.mccrory.me/2010/12/07/data-gravity-in-the-clouds/
Storage       Big Data                  Compute
          …and inertia at volume…




                     Data




                                    http://blog.mccrory.me/2010/12/07/data-gravity-in-the-clouds/
Storage         Big Data                Compute
    …easier to move applications to the data




                        Data




                                    http://blog.mccrory.me/2010/12/07/data-gravity-in-the-clouds/
Cloud has the power to
       process
Lorem ipsum dolor sitStorage     Big Data                  Compute
 met,        consectetur   Bring compute capacity to the data
 dipiscing elit. Etiam
                                                                     Lorem ipsum dolor
 uis ligula neque, eget
                                                                     amet,         consecte
 enenatis           sem. Personal
                                                                     adipiscing elit. Etia
Suspendisse non eros
                                                                     quis ligula neque, eg
 ulla, at placerat nibh.
Cras id lectus mattis est
                         Very large dataset                          venenatis            se
                                                                     Suspendisse non er
 llamcorper      blandit.seeks      strong          &                nulla, at placerat nibh
Proin ut nisi vitae enim
 ulputate        tempor. consistent compute for                      Cras id lectus mattis
Phasellus id commodo                                                 est       ullamcorper
 ros.    Mauris       necshort term relationship,                    blandit. Proin ut nisi
 ignissim turpis. Nunc                                               vitae enim vulputate
                         possibly longer. GSOH a                     tempor. Phasellus id
 Cras id lectus mattis   plus          aws.amazon.com                commodo          eros.
                                                                     Mauris nec dignissim
 est       ullamcorper
                                                                     turpis. Nunc
Storage    Big Data            Compute
          From one instance…
Storage   Big Data        Compute
          …to thousands
Storage   Big Data          Compute
          and back again…
The revolution
have data
have data

can store
have data

can store   can analyse
economically
fast
Who is your customer really?
    What do people really like?
What is happening socially with
               your products?
 How do people really use your
                    products?
34
Finding Out More with Data Analytics and AWS
Lesson 1: don’t leave your Amazon
    account logged in at home

Lesson 2: use the data you have to
    drive proactive marketing
1 instance for 100 hours
            =
100 instances for 1 hour
Small instance = $8
Finding Out More with Data Analytics and AWS
Amazon Elastic MapReduce
But what is it?
A framework
Splits data into pieces
Lets processing occur
   Gathers the results
S3 + DynamoDB                      Input data




Code    Elastic              Name                                Output
       MapReduce             node                             S3 + SimpleDB


                      Queries
                                                       HDFS
                       + BI
                   Via JDBC, Pig, Hive
                                         Elastic cluster
Very large
 click log
 (e.g TBs)
Lots of actions
             by John Smith




Very large
 click log
 (e.g TBs)
Lots of actions
             by John Smith




Very large
 click log
 (e.g TBs)    Split the
               log into
             many small
                pieces
Process in an
             Lots of actions   EMR cluster
             by John Smith




Very large
 click log
 (e.g TBs)    Split the
               log into
             many small
                pieces
Process in an
             Lots of actions   EMR cluster
             by John Smith




Very large
 click log
 (e.g TBs)    Split the            Aggregate
               log into            the results
             many small              from all
                pieces              the nodes
Process in an
             Lots of actions   EMR cluster
             by John Smith




Very large                                       What
 click log                                       John
 (e.g TBs)    Split the            Aggregate
               log into            the results
                                                 Smith
             many small              from all     did
                pieces              the nodes
Very large                                       What
 click log                                       John
 (e.g TBs)   Insight in a fraction of the time   Smith
                                                  did
1 instance for 100 hours
            =
100 instances for 1 hour
Small instance = $8
1 instance for 1,000 hours
             =
1,000 instances for 1 hour
Small instance = $80
Features powered by Amazon Elastic
           MapReduce:
     People Who Viewed this Also Viewed
             Review highlights
     Auto complete as you type on search
         Search spelling suggestions
                Top searches
                     Ads

200 Elastic MapReduce jobs per day
       Processing 3TB of data
Finding Out More with Data Analytics and AWS
Finding Out More with Data Analytics and AWS
Data Analytics

3.5 billion records             Execute batch processing data sets
                                ranging in size from dozens of                   “Our first client
71 million unique cookies       Gigabytes to Terabytes                   campaign experienced
1.7 million targeted ads                                                     a 500% increase in
                                Building in-house infrastructure to
required per day                analyze these click stream datasets           their return on ad
                                requires investment in expensive           spend from a similar
                                “headroom” to handle peak demand.               campaign a year
                                                                                         before”


                            User recently
                            purchased a            Targeted Ad
                            sports movie
                            and is searching     (1.7 Million per day)
                            for video games
Want to try some of this?

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Finding Out More with Data Analytics and AWS

  • 1. Finding out more from your advertising campaigns using data analytics
  • 2. 1 instance for 100 hours
  • 3. 1 instance for 100 hours Video encoding Customer activity analysis Twitter parsing …
  • 4. 1 instance for 100 hours = 100 instances for 1 hour
  • 5. Why would you need 100 instances? (or even more)
  • 7. Many definitions Very large volume Three V’s: Social with low density Velocity interactions and of information Variety web activity data Volume Amongst many others..
  • 8. Storage Big Data Compute Unconstrained data growth 95% of the 1.2 zettabytes of ZB data in the digital universe is unstructured 70% of of this is user- EB generated content Unstructured data growth explosive, with estimates of PB compound annual growth (CAGR) at 62% from 2008 – GB TB 2012. Source: IDC
  • 9. Storage Big Data Compute Where does it come from? Web sites Sensor data Blogs/Reviews/Emails/Pictures Weather, water, smart grids Social Graphs Images/videos Facebook, Linked-in, Contacts Traffic, security cameras Application server logs Twitter Web sites, games 50m tweets/day 1,400% growth per year
  • 11. Storage Big Data Compute Why now? Web sites Sensor data Blogs/Reviews/Emails/Pictures Weather, water, smart grids Social Graphs Images/videos Facebook, Linked-in, Contacts Traffic, security cameras Application server logs Twitter Web sites, games 50m tweets/day 1,400% growth per year
  • 12. Storage Big Data Compute Why now? Web sites Sensor data Weather, water, smart grids Mobile connected world Blogs/Reviews/Emails/Pictures Social Graphs Images/videos Facebook, Linked-in, Contacts Traffic, security cameras (more people using, easier to collect) Application server logs Twitter Web sites, games 50m tweets/day 1,400% growth per year
  • 13. Storage Big Data Compute Why now? Web sites Sensor data Weather, water, smart grids More aspects of data Blogs/Reviews/Emails/Pictures Social Graphs Images/videos Facebook, Linked-in, Contacts Traffic, security cameras (variety, depth, location, frequency) Application server logs Twitter Web sites, games 50m tweets/day 1,400% growth per year
  • 14. Storage Big Data Compute Why now? Web sites Sensor data Weather, water, smart grids Possible to understand Blogs/Reviews/Emails/Pictures Social Graphs Images/videos Facebook, Linked-in, Contacts Traffic, security cameras (not just answer specific questions) Application server logs Twitter Web sites, games 50m tweets/day 1,400% growth per year
  • 18. And data has gravity…
  • 19. Storage Big Data Compute Data has gravity App Data App http://blog.mccrory.me/2010/12/07/data-gravity-in-the-clouds/
  • 20. Storage Big Data Compute …and inertia at volume… Data http://blog.mccrory.me/2010/12/07/data-gravity-in-the-clouds/
  • 21. Storage Big Data Compute …easier to move applications to the data Data http://blog.mccrory.me/2010/12/07/data-gravity-in-the-clouds/
  • 22. Cloud has the power to process
  • 23. Lorem ipsum dolor sitStorage Big Data Compute met, consectetur Bring compute capacity to the data dipiscing elit. Etiam Lorem ipsum dolor uis ligula neque, eget amet, consecte enenatis sem. Personal adipiscing elit. Etia Suspendisse non eros quis ligula neque, eg ulla, at placerat nibh. Cras id lectus mattis est Very large dataset venenatis se Suspendisse non er llamcorper blandit.seeks strong & nulla, at placerat nibh Proin ut nisi vitae enim ulputate tempor. consistent compute for Cras id lectus mattis Phasellus id commodo est ullamcorper ros. Mauris necshort term relationship, blandit. Proin ut nisi ignissim turpis. Nunc vitae enim vulputate possibly longer. GSOH a tempor. Phasellus id Cras id lectus mattis plus aws.amazon.com commodo eros. Mauris nec dignissim est ullamcorper turpis. Nunc
  • 24. Storage Big Data Compute From one instance…
  • 25. Storage Big Data Compute …to thousands
  • 26. Storage Big Data Compute and back again…
  • 30. have data can store can analyse
  • 32. fast
  • 33. Who is your customer really? What do people really like? What is happening socially with your products? How do people really use your products?
  • 34. 34
  • 36. Lesson 1: don’t leave your Amazon account logged in at home Lesson 2: use the data you have to drive proactive marketing
  • 37. 1 instance for 100 hours = 100 instances for 1 hour
  • 41. But what is it?
  • 42. A framework Splits data into pieces Lets processing occur Gathers the results
  • 43. S3 + DynamoDB Input data Code Elastic Name Output MapReduce node S3 + SimpleDB Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster
  • 44. Very large click log (e.g TBs)
  • 45. Lots of actions by John Smith Very large click log (e.g TBs)
  • 46. Lots of actions by John Smith Very large click log (e.g TBs) Split the log into many small pieces
  • 47. Process in an Lots of actions EMR cluster by John Smith Very large click log (e.g TBs) Split the log into many small pieces
  • 48. Process in an Lots of actions EMR cluster by John Smith Very large click log (e.g TBs) Split the Aggregate log into the results many small from all pieces the nodes
  • 49. Process in an Lots of actions EMR cluster by John Smith Very large What click log John (e.g TBs) Split the Aggregate log into the results Smith many small from all did pieces the nodes
  • 50. Very large What click log John (e.g TBs) Insight in a fraction of the time Smith did
  • 51. 1 instance for 100 hours = 100 instances for 1 hour
  • 53. 1 instance for 1,000 hours = 1,000 instances for 1 hour
  • 55. Features powered by Amazon Elastic MapReduce: People Who Viewed this Also Viewed Review highlights Auto complete as you type on search Search spelling suggestions Top searches Ads 200 Elastic MapReduce jobs per day Processing 3TB of data
  • 58. Data Analytics 3.5 billion records Execute batch processing data sets ranging in size from dozens of “Our first client 71 million unique cookies Gigabytes to Terabytes campaign experienced 1.7 million targeted ads a 500% increase in Building in-house infrastructure to required per day analyze these click stream datasets their return on ad requires investment in expensive spend from a similar “headroom” to handle peak demand. campaign a year before” User recently purchased a Targeted Ad sports movie and is searching (1.7 Million per day) for video games
  • 59. Want to try some of this?