The document discusses Zalando's transition to a data mesh architecture from a traditional centralized data lake, highlighting the roles of product and technology teams in data generation and consumption. It emphasizes the importance of treating data as a product and implementing decentralized governance while ensuring data quality through conscious decisions and resource allocation. The approach aims to improve organizational scalability by allowing cross-functional, domain-specific teams to manage their own data, thus avoiding bottlenecks.

1. Data Mesh in Practice
Max Schultze - max.schultze@zalando.de
Arif Wider - awider@thoughtworks.com
25-06-2020
How Europe’s Leading
Online Platform for Fashion
Goes Beyond the Data Lake
@mcs1408 @arifwider

2. 2
Max Schultze
● Lead Data Engineer
● MSc in Computer Science
● Took part in early
development of Apache Flink
● Retired semi-professional
Magic: the Gathering player
Who are we?
Arif Wider
● Lead Technology Consultant
● Head of AI, ThoughtWorks Germany
● Scala & FP enthusiast
● Coffee geek

3. 3
TABLE OF
CONTENTS
Zalando Analytics Cloud Journey
What’s this Data Mesh?
Data Mesh in Practice

4. 4
Zalando Analytics Cloud
Journey

5. 5
Legacy Analytics
DWH

6. 6
Messaging
Bus
Data Lake
Legacy Evolving

7. 7
Zalando’s Data Lake
Ingestion
Storage
Serving

8. 8
Zalando’s Data Lake
Web
Tracking
Event Bus
DWH
Data Center
Ingestion
Storage
Serving

9. 9
Zalando’s Data Lake
Web
Tracking
Event Bus
DWH
Data Center
Ingestion
Storage
Serving
Metastore

10. 10
Zalando’s Data Lake
Data CatalogWeb
Tracking
Event Bus
DWH
Data Center
Ingestion
Storage
Serving
Metastore
Fast Query Layer
Processing Platform

11. 11
Centralization Challenges
Datasets provided by data agnostic infrastructure team
● Lack of ownership
?

12. 12
Field_A Field_B
Record_1
Record_2
Record_3
Datasets provided by data agnostic infrastructure team
● Lack of ownership
Pipeline responsibility on data agnostic infrastructure team
● Lack of quality
Centralization Challenges

13. 13
Centralization Challenges
Datasets provided by data agnostic infrastructure team
● Lack of ownership
Pipeline responsibility on data agnostic infrastructure team
● Lack of quality
Organizational scaling
● Central team becomes the bottleneck

14. 14
A Recurring Pattern
Source domain
product teams
generating data
Teams, decisions
makers, data scientists
consuming data
Data & ML engineers
maintaining the
data platform

15. 15
A Recurring Pattern
Source domain
product teams
generating data
Teams, decisions
makers, data scientists
consuming data
Data & ML engineers
maintaining the
data platform

16. 16
A Recurring Pattern
Source domain
product teams
generating data
Teams, decisions
makers, data scientists
consuming data
Data & ML engineers
maintaining the
data platform

17. 17
A Recurring Pattern
Source domain
product teams
generating data
Data & ML engineers
maintaining the
data platform
Teams, decisions
makers, data scientists
consuming data

18. 18
Why is that?
central
data platform

19. 19
Why is that?
checkout
service
checkout
events

20. 20
What is Data Mesh?
Old wine applied to new bottles…
→ Product Thinking
→ Domain-Driven Distributed Architecture
→ Infrastructure as a Platform
… creates value from Data
https://martinfowler.com/articles/data-monolith-to-mesh.html by Zhamak Dehghani

21. 21
Data as a Product
Data
Product
What is my market?
What are the desires of
my customers?
What “price” is justified?
How to do marketing?
What’s the USP?
Are my customers happy?

22. 22
Domain-Driven Distributed Architecture… applied to Data
Domain
22

23. 23
Domain-Driven Distributed Architecture… applied to Data
Domain
23
→ The Data Product is the
fundamental building block
Aggregated
Domain

24. 24
Domain-Driven Distributed Architecture… applied to Data
Discoverable
Addressable
Self-describing
Trustworthy
Interoperable
(governed by open
standard)
Secure (governed by
global access control)
Domain
24
→ The Data Product is the
fundamental building block
Aggregated
Domain

25. 25
...backed by domain-agnostic self-service data infrastructure
Data Infra as a Platform
Storage, pipeline, catalogue, access control, etc
Data infra
engineers
Discoverable
Addressable
Self-describing
Trustworthy
Interoperable
(governed by open
standard)
Secure (governed by
global access control)
Domain
25
→ The Data Product is the
fundamental building block
Aggregated
Domain

26. 26
It’s a mindset shift
FROM TO
Centralized ownership Decentralized ownership
Pipelines as first class concern Domain Data as first class concern
Data as a by-product Data as a Product
Siloed Data Engineering Team Cross-functional Domain-Data Teams
Centralized Data Lake / Warehouse Ecosystem of Data Products

27. 27
Data Mesh in Practice

28. 28
Recap:
● From Bottleneck to Infra Platform
Data Mesh in Practice
Data Infra as a Platform
Storage, pipeline, catalogue, access control, etc

29. 29
Recap:
● From Bottleneck to Infra Platform
● From Data Monolith to Interoperable Services
Data Mesh in Practice
Data Infra as a Platform
Storage, pipeline, catalogue, access control, etc
central
data
platform

30. 30
Data Lake Storage
Metadata Layer
Central Services with Global Interoperability

31. 31
Data Lake Storage
Metadata Layer
Bring Your Own Bucket (BYOB)

32. 32
Data Lake Storage
Processing Platform
Metadata Layer
Central Processing Platform

33. 33
Data Lake Storage
Processing Platform
Metadata Layer
Simplify Data Sharing

34. 34
Central Services with Global Interoperability
Decentralized ownership does not imply decentralized infrastructure!
Interoperability is created through convenient solutions of a self service platform.
Decentral Storage Central Infrastructure
Decentral Ownership Central Governance

35. 35
Recap:
● Datasets provided through pipelines of data agnostic infrastructure teams
Data Mesh in Practice
?

36. 36
How to Ensure Data Quality?
Make conscious decisions
● Opt-in instead of default storage

37. 37
How to Ensure Data Quality?
Make conscious decisions
● Opt-in instead of default storage
● Classification of data usage

38. 38
Data Quality - A Contract between Consumer and Producer
Behavioral changes for data producers
● Data is a product not a by-product

39. 39
Behavioral changes for data producers
● Data is a product not a by-product
● Dedicate resources to
○ Understand usage
○ Ensure quality
Data Quality - A Contract between Consumer and Producer

40. 40
Data Mesh in Practice
How Europe’s Leading
Online Platform for Fashion
Goes Beyond the Data Lake
Max Schultze
max.schultze@zalando.de
@mcs1408
Arif Wider
awider@thoughtworks.com
@arifwider