Presented at Snowplow London Meetup, 8 February 2017 Bruce Pannaman, data scientist at Busuu, talked about why they are using Snowplow to validate and enrich data, enable one source of truth across different data sources, cope with peaks and troughs in the data stream, and easily integrate with third party systems such as Intercom, a customer messaging platform. One of Busuu’s future projects is to load multiple A/B tests into the apps and monitor their results in real time.

1. Snowplow Meetup
London, February 2017

2. 2
busuu is the world’s leading social
network for language learning
Language courses Social network
+
• Access to native speakers
• Peer to peer text corrections
• High quality courses in 12 languages
• Beginner to advanced intermediate level
1 2

3. How does busuu work?
Most important
vocabulary
Key
grammar
Practice with
native speakers
Faster
fluency
busuu is a complete self-study and language practice environment
3

4. busuu 2016
What sort of data do we use?
● Front end tracking data
● Progress data
● Backend db data
● Third party data

5. Why did we
look at using
Snowplow

6. busuu 2016
Problems
My data says
X, why does
yours say Y?
Cloudwatch
Alert!
“Why can’t i find
the results of my
A/B test till
tomorrow again?
“Oh my god, do
we really have to
put yet another
tracker in?”

7. busuu 2016
Scalability

8. busuu 2016
Batch vs Real time

9. busuu 2016
Reconciliation

10. busuu 2016
Then we thought...
Can we use
snowplow
framework for
more than just
analytics?

11. busuu 2016
Too many SKDs and trackers

12. busuu 2016
Snowplow delivery
How do we get
Snowplow to deliver
the events to
everybody/thing that
needs it, instead of
adding more trackers
to the frontend

13. Tech Stack

14. busuu 2016
Data Collection Phase
Events Events Backend
Data
API Calls
Yet to be done
Scala Stream
Collector

15. busuu 2016
Processing
15
Stream
Enrich
Raw Data Enriched Data

16. busuu 2016
Processing
Validation
● Customised busuu event
schemas
● Different based on environment
Enrichments
● ip lookup
● Forex Conversion

17. busuu 2016
Distribution
17
Results
back to
App/SiteMachine Learning
Models
Yet to
be done

18. busuu 2016
Plug & Play Integrations
18
● One source of truth
● Scalability
● Third party systems can be added very quickly

19. busuu 2016
Lambda?
19
Parse through each
field of enriched
data looking for
custom schema
name
One lambda function
per type of data and
per integration
Relay required data to
third party service
through REST api or
given python client

20. Problem areas
20 busuu 2016

21. busuu 2016
Main implementation bugbears
1. Strict Multi Platform Schemas
2. Offline mode delay
3. Device vs Collector Timestamps

22. Future
Improvements
22 busuu 2016

23. busuu 2016
Future projects
● Live A/B test trains & results
● Live machine learning results in app
● Automated alerting on complex company metrics.

24. Thanks!
Bruce Pannaman

25. busuu 2016
Frontend event data
● Track and find issues in user behavior.
● Insight into product usage
● A/B Testing
● CRM cohorting
● In-app message cohorting

26. busuu 2016
Progress Data
● What has a user learnt?
● How is our content performing
● What is their language level?
● Vocabulary lists

27. busuu 2016
Backend Data
● What are the user’s attributes
● Social relationships (friends)
● Writing exercises and comments

28. busuu 2016
Third Party
● Payments
● CRM performance
● App store metadata (review etc.)
● PPC data