Scaling event tracking can be a challenge. But with a smart combination of AWS' services, this can be achieved with little effort and costs. This talk shows how Blinkist moved Web and Mobile Analytics in-house with a globally distributed and fully managed solution for little cost thanks to AWS.

1. Tracking & Business Intelligence
A brief journey through Blinkist’s stack

2. About me
• Sebastian Schleicher, Director of Engineering @Blinkist
https://about.me/sebastian.schleicher
• With Blinkist since the very beginning
2

3. What’s in it for you?
• How we went from expensive and disconnected event tracking
to an affordable and fully connected Business Intelligence solution
• Many buzzwords and product names…
3

4. Tracking “Classics”

5. Tracking “Classics”
5
API
Website
Backend
Data sync

6. –The new Marketing guy in your team
„Google Analytics is my bible. Can’t I just quickly add it to the
website? It’s really easy….“

7. Tracking “Classics”
7
API
Website
Backend
Data sync
analytics.js

8. –An Anonymous Product Manager
“Mixpanel has it all to run AB tests, e-mail and push campaigns.
I saw on their website that they also have artificial intelligence!
Engineers only have to add this tiny SDK. It’s sooo easy….“

9. Tracking “Classics”
9
API
Website
Backend
SDK
Data sync
analytics.js

10. –A Data Analyst
“Give me a Postgres DB and I’m happy.“

11. Tracking “Classics”
11
API
Website
Backend
SDK
Data sync
analytics.js
Analytics
Postgres
Copy
production data

12. Tracking “Classics”
12
API
Website
Backend
SDK
Data sync
analytics.js
Analytics
Postgres
Copy
production data
Analyst
SQL
Marketing
Product

13. We created Information

14. We created Information Silos…

15. Tracking “Classics”
15
API
Website
Backend
SDK
Data sync
analytics.js
Analytics
Postgres
Copy
production data
Analyst
SQL
Marketing
Product

16. A little example

17. –A prospective investor during Due Diligence
“What is your Customer Lifetime Value?“

18. Customer Lifetime Value
18
Product
28€
Marketing
44€
Analyst
37€
Investor
CIAO

19. That’s the moment when you realize:
We need to take BI seriously

20. –A prospective investor during Due Diligence
“What is your Customer Lifetime Value?“

21. 36.50€
Customer Lifetime Value
21
Analyst
CLV=
*
* better don’t apply this formula for determining your CLV 🤓

22. Customer Lifetime Value
22
Product
36.50€
Marketing
36.50€
Analyst
36.50€
How much do
you need?
Investor

23. Tracking requirements
• All data integrated inside a central BI database
• Backend data
• Web & Mobile tracking
• External vendor data (payment etc)
• … whatever comes in the future
23

24. Tracking “Classics”
24
API
Website
Backend
SDK
Data sync
analytics.js
Analytics
Postgres
Copy
production data

25. Tracking “Classics”
25
API
Website
Backend
SDK
Data sync
analytics.js
Redshift
The right DB
for the jobCopy
production data

26. Now let us
in-house Web tracking.

27. Web tracking
27
API
Website
Backend
SDK
Data sync
analytics.js
Redshift
Copy
production data
Copy
production data

28. Web tracking
28
API
Website
Backend
SDK
Data sync
piwik.js
analytics.js
Redshift
*New name: https://matomo.org/
*
Copy
production data

29. Self-hosted Web tracking
29
Website
Backend
NGINX
Postgres
Copy with
Data Pipeline
Copy with
custom scripts
Redshift
API
tracking.example.com
Custom
access log
processing
action_name=Something
idsite=1
rec=1
r=322284
url=https://mywebsite.com/a_page
… Quite some columns 😎
Running on
Digital Ocean
GET https://tracking.example.com/t?action_name=Something…..piwik.js

30. Why is Google Analytics
reporting more visitors?

31. Slow network roundtrips
31
Digital Ocean
Machine in Frankfurt
200ms
400ms
300ms
100ms
400ms
400ms
300ms
Estimations based on
https://wondernetwork.com/pings

32. Self-hosted Web tracking
• Everything is under our control
• Data available inside Redshift
• Running outside of AWS on Digital Ocean
• Custom-configured NGINX and PostgresDB
• Custom log processing and data copy breaks sometimes
• Poor performance/latency leads to data inconsistencies
32

33. How to have this
managed by AWS?

34. AWS powered Web tracking
34
Website
Backend
CloudFront
Copy with
Data Pipeline
Redshift
API
tracking.example.com
S3 Bucket
Store Access Logs
S3 Bucket
/t (image/gif)
Lambda
Notify putObject
Kinesis Firehose
Parse and putRecords
GET https://tracking.example.com/t?action_name=Something…..piwik.js
Load data

35. No delays with CloudFront
35

36. AWS powered Web tracking
• Very cost effective 🤫🤓
• Everything is under our control
• Data available inside Redshift
• Native citizen in our stack
• All managed AWS services
• Little latency leads to better data consistency
36

37. Mobile tracking
37
API
Website
Backend
SDK
Data sync
Enrich
user data
CloudFront,
Lambda and
Firehose
piwik.js
Enrich
user data
Copy data
Load data
analytics.js
Redshift

38. Mixpanel mobile tracking
38
API Backend
SDK
Data sync
Copy data
Redshift

39. Mixpanel mobile tracking
• Fully managed service
• Great self-service User Interface
• Amazing SDKs
• Becomes very expensive
• Data extraction is tricky
• Many unused features
39

40. Doesn’t AWS have something?

41. AWS has something
41
Amazon
Pinpoint
Push/Email
Messaging
Customer
Segmentation
Multi-Channel
Campaigns
Application
Analytics

42. Pinpoint mobile tracking
42
API Backend
SDK
Data sync
Redshift
Amazon
Pinpoint
Kinesis
Firehose

43. Pinpoint mobile tracking
• Everything is under our control
• Data available inside Redshift
• Native citizen in our stack
• All managed AWS services, CloudFront backed
• Very affordable (100 million events per month for free!)
• Rather young product (but sufficient for our need)
• Very basic User Interface
43

44. Redshift ecosystem
44
API
Website
Backend
SDK
Data sync
CloudFront,
Lambda and
Firehose
piwik.js
Load data
analytics.js
Redshift
Pinpoint and
Kinesis Firehose
Load data

45. Redshift ecosystem
45
Backend
CloudFront,
Lambda and
Firehose
Redshift
Pinpoint and
Kinesis Firehose
Analyst
I can’t always query against raw data
but need to aggregate and visualize it!

46. “I can’t always query against
raw data but need to
transform and visualize it!”

47. Redshift ecosystem
47
Backend
CloudFront,
Lambda and
Firehose
Redshift
Pinpoint and
Kinesis Firehose
AnalystTransformations
Redshift
Monitoring &
Optimizations
Visualization
& Dashboards
Product Marketing

48. Recap

49. Technologies used
• Redshift for central BI database
• Some ETL for production databases
• Piwik.js and CloudFront, Lambda and Firehose for Web tracking
• Pinpoint and Kinesis for Mobile tracking
49

50. Tracking requirements
• All data integrated inside a central BI database ✅
• Backend data ✅
• Web & Mobile tracking ✅
• External vendor data ✅ (didn’t mention it, but it’s there)
• Ready for the future
50

51. A few thoughts
• Raw data requires expertise on how to deal with it 😐
• Tools for Redshift aren’t cheap (Matillion, Periscope, etc) 😕
• Data autonomy is amazing 🤩
• This solution is fully managed and scales for us 🤗
51

52. Looking ahead

53. More tracking requirements
• Data quality has to improve
• Tracking data should be centrally available in realtime:
• Create realtime monitoring
• Send events to backend systems or external services
• Immediate feedback
• Data transformation and enrichment should happen in realtime
53

54. A real-time ETL on Kinesis
would be cool.

55. Realtime ETL Pipeline
55
Backend
Piwik
Redshift
Pinpoint ETL Tool
Kinesis
Firehose
S3 Sink
Lambda
Another
System
Sanitize &
Quality Control
Enrich Data
Running on Lambda

56. There was no solution.
So we built one …

57. ETL Tool
Welcome Alchemist
57
Backend
Piwik
Redshift
Pinpoint Alchemist
Kinesis
Firehose
S3 Sink
Lambda
Another
System
Sanitize &
Quality Control
Enrich Data
Running on Lambda

58. Alchemist Pipeline
58
Input Transformations Output
Pipeline Data Pipeline Data
Pipeline Data
Faulty
Data
Sane
Data
AdapterAdapter

59. CloudFront Example
59
S3 Input
Kinesis Output
Cloudfront Log
Transformation
Unzip
Transformation
Quality Control
Transformation
SQS Output
Pipeline
Data
Sane
Data
Faulty
Data
Check faulty data
and react quickly
CF Logs

60. Alchemist input examples
60
Backend Alchemist
Kinesis
Pinpoint
Kinesis
Alchemist
Piwik
CF Logs
Alchemist
Webhooks
API Gateway
Alchemist
Kinesis

61. is Open Source
https://github.com/blinkist/alchemist
Currently only available in JavaScript
Licensed under MIT

62. Key takeaways

63. Key takeaways
• Tracking can be scalable & affordable 💸
• Data autonomy is 🚀
• Start collecting raw data early while it’s manageable
• Get your Business Intelligence homework done.
Define and collect metrics that you can trust!
63

64. Thank you for your attention.