SlideShare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
SlideShare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Successfully reported this slideshow.
Activate your 14 day free trial to unlock unlimited reading.
1.
BigQuery in Social Gaming
Yan Cui, Senior Developer
Davinder Pank, Social BI Manager
2.
Who is Gamesys?
• Founded in 2001
• #1 in the UK
• Handle $5 Billion in turnover annually
• First company to launch real money gaming on Facebook
• Employ 1,000 globally
5.
Events driven Analysis
Enables deeper ad hoc analysis
Analysis goes only as far as the data
Finer the grain, bigger the volume
6.
Be Ready for Success
Jackpotjoy Slots Bingo Lane Here Be Monsters
DAU 600,000+ 150,000+ 40,000+
Events (monthly) 5 billion+ 500 million+ 700 million+
1 TB+ 200 GB+ 500 GB+
7.
• Ensure the health of our defined KPIs across our products –
Jackpotjoy Slots, Bingo Lane and Here Be Monsters
• Develop player insights to better improve the depth with
which users engage with our games
Social Gaming Business Intelligence,
Gamesys
BigQuery
8.
Why BigQuery?
Scales
Managed
Fully managed architecture,
allows instant project
startup, and rapid time to
insight
Easy to learn, minimal
transition period, especially
for those moving from
traditional relational
databases
SQL
Grows with your project,
scales horizontally from 100
thousands to 100 Bn's of
rows with no loss of
performance on interactive
queries
9.
Monitoring KPI Health
Reporting
Apps Script Spreadsheets
Cloud
Storage
Big
Query
ETL
12.
Developing Player Insights
Big Joins
No need for temporary
tables, let’s us get to the
results we need in as few
steps as possible
How BigQuery features and functions allow us to better
explore our data
13.
Developing Player Insights
• Our analysis hinges on being able to compare behaviour amongst players
with similar in-game maturity to one another
• Where we gain some of our most invaluable insights are where changes in
player behaviour lie outside the norm of what we would expect
How BigQuery features and functions allow us to better
explore our data
14.
Developing Player Insights
Window Functions
Rank and partition allow us
to compare fairly player
engagement and
monetisation metrics across
players with the same in-
game maturity. Reduces
dependence on external
tools.
Lead and lag allow us to
easily identify segments
exhibiting interesting
changes in behaviour
How BigQuery features and functions allow us to better
explore our data
18.
JackpotJoy Slots
http://apps.facebook.com/jackpotjoyslots
Bingo Lane
http://apps.facebook.com/bingolane
Here Be Monsters
http://apps.facebook.com/herebemonsters
Building a MMORPG
http://bit.ly/1hjqoL8
http://slidesha.re/18MD4XY
Google I/O 2013 – Here Be BigQuery
http://bit.ly/1fHjbce
0 likes
Be the first to like this
Views
Total views
1,749
On SlideShare
0
From Embeds
0
Number of Embeds
183
You have now unlocked unlimited access to 20M+ documents!
Unlimited Reading
Learn faster and smarter from top experts
Unlimited Downloading
Download to take your learnings offline and on the go
You also get free access to Scribd!
Instant access to millions of ebooks, audiobooks, magazines, podcasts and more.
Read and listen offline with any device.
Free access to premium services like Tuneln, Mubi and more.