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.

Technology

Capturing and
managing player
data is hard

S3

Input data

Code Elastic Name
MapReduce node

S3

Input data

Code Elastic Name
MapReduce node

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cluster

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Input data

Code Elastic Name
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Code Elastic Name
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MapReduce node S3 + SimpleDB

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with Elastic MapReduce

Steady state Steady state
Batch processing

Click stream analysis for Best Buy

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Each day

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Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

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Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia

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Understanding Player Behaviour

1. Understanding Player Behaviour

2. Player behaviour is priceless

3. Play statistics

4. Social graph

5. Monitor and iterate

6. Increase playability

7. Increase DLC sales

8. Increase advertising engagement

9. Ask questions of player behaviour

10. Ask questions of player behaviour data

11. Step 1: data collection

12. Database

13. Player data is complex

14. Player data is plentiful

15. Player data is fast moving

16. Capturing and managing player data is hard

17. Database can become bottleneck

18. DynamoDB

19. Step 2: analytics

20. Hadoop

21. Elastic MapReduce

22. Managed

23. Flexible

24. Java (or Ruby, Python etc)

25. Data warehouse

26. S3 Input data

27. S3 Input data Code Elastic MapReduce

28. S3 Input data Code Elastic Name MapReduce node

29. S3 Input data Code Elastic Name MapReduce node Elastic cluster

30. S3 Input data Code Elastic Name MapReduce node HDFS Elastic cluster

31. S3 Input data Code Elastic Name MapReduce node Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster

32. S3 Input data Code Elastic Name Output MapReduce node S3 + SimpleDB Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster

33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

46.

47. DynamoDB integrates with Elastic MapReduce

48. Combine

49. HiveQL queries

50. Backup and restore

51. Data movement

52. Import/Export

53. Multipart upload

54. Multipart, parallel results delivery

55. Direct Connect

56. Scale control

57. Resize running job ﬂows

58. 14 hours Time remaining: 14 hours

59. 14 hours Time remaining: 7 hours

60. Time remaining: 3 hours

61. Balance cost and performance

62. Resize based on usage patterns

63. Steady state Steady state Batch processing

64. Perfect for Spot

65. Cluster types

66. Small

67. High memory High CPU or both

68. HPC

69. 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

70. Click stream analysis for Madden Workﬂow time from 2 days to 8 hours Procurement time from 2 months to 5 minutes $13k per month 500% increase return on advertising spend

71. Web log analysis and recommendation engine $29.9 million in sales 842 million page views 434 Gb of page logs 97 million ‘favourites’

Understanding Player Behaviour

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