5. Confidential & ProprietaryGoogle Cloud Platform 5
Diverse Data Sources
Data from user
acquisition
campaigns
Data from Google
Play and App Store
Turnkey gaming
metrics
(e.g. player churn and
spend predictions from
Play Games Services)
7. Confidential & ProprietaryGoogle Cloud Platform 7
Custom game events
Custom logs
Custom player telemetry
specific to your games
Diverse Data Sources
8. Confidential & ProprietaryGoogle Cloud Platform 8
Continuum of Gaming Analytics
Standard metrics:
● DAU, MAU, ARPPU
● Player Progression
● Feature Engagement
● Spend
● Retention / Churn
● Daily revenue targets
● Fraud and cheating
Key indicators specific to your game:
● Activity in communities, joining
guilds, # of friends in-game
● Reached meaningful milestone
or achievement
● Time to first meaningful transaction
● Player response to specific A/B tests
Turnkey Custom
9. ● How many players made it to stage 12?
● What path did they take through the stage?
● Health and other key stats at this point in time?
● Of the players who took the same route where a
certain condition was true, how many made an in-
app purchase?
● What are the characteristics of the player segment
who didn’t make the purchase vs. those who did?
● Why was this custom event so successful in driving
in-app purchases compared to others?
Ask custom questions
Confidential & ProprietaryGoogle Cloud Platform 9
10. 秘密 / 占有情報Google Cloud Platform 10
3 Things to Remember
秘密 / 占有情報Google Cloud Platform 10
Speed up from Batch to Real-Time
Speed up Development Time
Speed up Batch Processing1
3
2
16. Confidential & ProprietaryGoogle Cloud Platform 16
Some of DeNA's Hadoop+Hive woes:
● Many bottlenecks & failure points
● 3 hour data ingestion lag
● Too many analysts at peak time
● Slow queries
● ...
47. 秘密 / 占有情報Google Cloud Platform 47
Reads game data published in near real-time, and
uses that data to perform two separate processing
tasks:
● Calculates the total score for every unique
user and publishes speculative results for
every ten minutes of processing time.
● Calculates the team scores for each hour that
the pipeline runs using fixed-time windowing..
● In addition, the team score calculation uses
Dataflow's trigger mechanisms to provide
speculative results for each hour (which
update every five minutes until the hour is
up), and to also capture any late data and
add it to the specific hour-long window to
which it belongs.
Leaderboard Example
秘密 / 占有情報Google Cloud Platform 47
48.
49. 秘密 / 占有情報Google Cloud Platform 49
http://goo.gl/vz1Cj5
● UserScore: Basic Score Processing in Batch
● HourlyTeamScore: Advanced Processing in
Batch with Windowing
● LeaderBoard: Streaming Processing with
Real-Time Game Data
● GameStats: Abuse Detection and Usage
Analysis
Cloud Dataflow and Spark examples
Sample Code on Github
秘密 / 占有情報Google Cloud Platform 49
50. Confidential & ProprietaryGoogle Cloud Platform 50
US Mobile Game Company goes Real-time Streaming
Streaming Pipeline
BigQuery
Analytics Engine
Cloud Pub/Sub
Asynchronous messaging
Real
Time
Events
Cloud Dataflow
Parallel data processing
32 4
Streaming Pipeline
iOS
1
Real-time Events
52. Building what’s next 52
Time to Understanding
Typical Big Data
Processing
Programming
Resource
provisioning
Performance
tuning
Monitoring
Reliability
Deployment &
configuration
Handling
growing scale
Utilization
improvements
53. Building what’s next 53
Time to Understanding
Big Data with Google:
Focus on insight,
not infrastructure.
Programming
55. Confidential & Proprietary 55Google Cloud Platform
speed 10B logs TBs of info 10x faster
Provisions new services
in seconds instead
of days
Google App Engine syncs
with BigQuery to automatically
store tens of billions
of application logs so TabTale
can analyze issues
on a moment's notice
Run queries on terabytes
of information
in a few seconds
Can now deliver new backend
features 10 times faster
without dealing with
infrastructure maintenance
“Our ability to provision new services in seconds saves us a lot of time,
since it used to take days. The gaming industry is characterized by short-
term projects, so it’s important for us to have a backend that is flexible
and works fast.”
60. Confidential & ProprietaryGoogle Cloud Platform 60
TensorFlow open source
manifestation of our ML capability
Machine Learning - TensorFlow Machine Learning - Vision API
Label / Entity Detection, Facial
Detection, OCR, Logo Detection, Safe
Search
Machine Learning - Cloud Dataproc
Managed Hadoop, Hive, Spark
90 secs to start cluster
61. Confidential & ProprietaryGoogle Cloud Platform 61
Like you, Google is committed to gaming
Use Google’s latest technologies to build,
distribute, and monetize your games
63. 秘密 / 占有情報Google Cloud Platform 63
3 Things to Remember
秘密 / 占有情報Google Cloud Platform 63
Speed up from Batch to Real-Time
Speed up Development Time
Speed up Batch Processing1
3
2