UnderstandingPlayer Behaviour
Player behaviour   is priceless
Play statistics
Social graph
Monitor and iterate
Increase playability
IncreaseDLC sales
Increase advertising    engagement
Ask questionsof player behaviour
Ask questionsof player behaviour       data
Step 1: data collection
Database
Player data is   complex
Player data is   plentiful
Player data is fast moving
Capturing andmanaging player  data is hard
Database canbecome bottleneck
DynamoDB
Step 2: analytics
Hadoop
Elastic MapReduce
Managed
Flexible
Java(or Ruby, Python etc)
Data warehouse
S3Input data
S3        Input dataCode     Elastic       MapReduce
S3        Input dataCode     Elastic     Name       MapReduce     node
S3        Input dataCode     Elastic     Name       MapReduce     node                            Elastic                 ...
S3        Input dataCode     Elastic     Name       MapReduce     node                                      HDFS          ...
S3        Input dataCode     Elastic               Name       MapReduce               node                         Queries...
S3        Input dataCode     Elastic               Name                           Output       MapReduce               nod...
DynamoDB integrateswith Elastic MapReduce
Combine
HiveQL queries
Backup and restore
Data movement
Import/Export
Multipart upload
Multipart, parallel      results     delivery
Direct Connect
Scale control
Resize running job       flows
14 hoursTime remaining: 14 hours
14 hoursTime remaining: 7 hours
Time remaining: 3 hours
Balance cost and  performance
Resize based on usage patterns
Steady state                      Steady state               Batch processing
Perfect for Spot
Cluster types
Small
High memory  High CPU   or both
HPC
Click stream analysis for Best Buy       3.5 billion records    71 million unique cookies    1.7 million targeted ads    1...
Click stream analysis for Madden   Workflow time from 2 days to 8 hoursProcurement time from 2 months to 5 minutes         ...
Web log analysis and recommendation engine           $29.9 million in sales          842 million page views           434 ...
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
Understanding Player Behaviour
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Understanding Player Behaviour

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An introduction to analytics, map/reduce and DynamoDB on AWS. Slides from the 'Powering games with Amazon Web Services' event in London.

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  • Understanding Player Behaviour

    1. 1. UnderstandingPlayer Behaviour
    2. 2. Player behaviour is priceless
    3. 3. Play statistics
    4. 4. Social graph
    5. 5. Monitor and iterate
    6. 6. Increase playability
    7. 7. IncreaseDLC sales
    8. 8. Increase advertising engagement
    9. 9. Ask questionsof player behaviour
    10. 10. Ask questionsof player behaviour data
    11. 11. Step 1: data collection
    12. 12. Database
    13. 13. Player data is complex
    14. 14. Player data is plentiful
    15. 15. Player data is fast moving
    16. 16. Capturing andmanaging player data is hard
    17. 17. Database canbecome bottleneck
    18. 18. DynamoDB
    19. 19. Step 2: analytics
    20. 20. Hadoop
    21. 21. Elastic MapReduce
    22. 22. Managed
    23. 23. Flexible
    24. 24. Java(or Ruby, Python etc)
    25. 25. Data warehouse
    26. 26. S3Input data
    27. 27. S3 Input dataCode Elastic MapReduce
    28. 28. S3 Input dataCode Elastic Name MapReduce node
    29. 29. S3 Input dataCode Elastic Name MapReduce node Elastic cluster
    30. 30. S3 Input dataCode Elastic Name MapReduce node HDFS Elastic cluster
    31. 31. S3 Input dataCode Elastic Name MapReduce node Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster
    32. 32. S3 Input dataCode Elastic Name Output MapReduce node S3 + SimpleDB Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster
    33. 33. DynamoDB integrateswith Elastic MapReduce
    34. 34. Combine
    35. 35. HiveQL queries
    36. 36. Backup and restore
    37. 37. Data movement
    38. 38. Import/Export
    39. 39. Multipart upload
    40. 40. Multipart, parallel results delivery
    41. 41. Direct Connect
    42. 42. Scale control
    43. 43. Resize running job flows
    44. 44. 14 hoursTime remaining: 14 hours
    45. 45. 14 hoursTime remaining: 7 hours
    46. 46. Time remaining: 3 hours
    47. 47. Balance cost and performance
    48. 48. Resize based on usage patterns
    49. 49. Steady state Steady state Batch processing
    50. 50. Perfect for Spot
    51. 51. Cluster types
    52. 52. Small
    53. 53. High memory High CPU or both
    54. 54. HPC
    55. 55. Click stream analysis for Best Buy 3.5 billion records 71 million unique cookies 1.7 million targeted ads 13 Tb of clickstream logs Each day
    56. 56. Click stream analysis for Madden Workflow time from 2 days to 8 hoursProcurement time from 2 months to 5 minutes $13k per month500% increase return on advertising spend
    57. 57. Web log analysis and recommendation engine $29.9 million in sales 842 million page views 434 Gb of page logs 97 million ‘favourites’

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