This document discusses the use of Apache Kafka as the heart of ING's event-driven architecture. It outlines how ING implemented Kafka locally at first for fraud detection, then expanded to clusters for higher availability across data centers and regions. A global event bus structure was created using mirroring to allow events to be streamed and consumed across applications in different countries and regions on a single logical event bus. Governance, metadata and data lineage are important as events are used across multiple regions and drive by regulations.

3. 3
1. Earn the primary relationship
2. Develop analytics skills to understand our customers better
3. Increase the pace of innovation to serve changing customer needs
4. Think beyond traditional banking to develop new services and business models
Empowering people to stay a step
ahead in life and in business.
Simplify &
Streamline
Operational
Excellence
Performance
Culture
Lending
Capabilities
Purpose
Customer
Promise
Strategic
Priorities
Enablers
Creating a differentiating customer experience
Clear and Easy Anytime, Anywhere Empower Keep Getting Better

4. Trends in the banking landscape continue to evolve
4
Customer
Behaviour
Competitive
Landscape
Technology
Fintech
SocietyRegulation
People’s time is precious;
they don’t want to spend it
on finance
Products have become commoditised; the only way
to differentiate is through the experience
Regulatory uncertainty
continues
Digitalisation is erasing
borders
Ability to leverage new
technologies will define
future competitive
advantage

5. ING is more and more becoming an IT company in Financial
Services.
5
58 Datacenters world wide
globally scalable banking platform
ING Software Development Centre outranksMicrosoft as IT employer of choice
“Software is eating the world”
Marc Andreessen
#INGlovesIT
360° Agile way of working by ING
Develop analytics skills to understand our customers better
ING adopts Spotify model
10.000 Dev/ops Engineers
IT is the Bank!

6. My first trip to New York 15 years ago left me with many
pictures, and an interesting anecdote.
6

7. Offering our customer actionable insights is the foundation of
the new banking experience
78
Relevant
Actionable
Real Time
Personal

8. All of this is driven by an Event based architecture, with
Apache Kafka at the heart of it.
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Producers
Datalake
C2R
(Chaos to
Relevance)
Models
Rules Machine
learning
Streaming Engine
Channel Log Location
SoR
Raw events
Business-relevant Events
Regional Global
Streaming Data Platform
Customer
Communication
Consumers
Kafka Event Bus

9. Like any organisation, we started small, and implemented
Kafka locally for one project, Fraud detection in NL.
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Kafka
Java
producer
library
Java
consumer
library
librdkafka
NGINX
Authenticating
Proxies
OCBB NGINX squad
API
toolkit /
RIAF
OCBB API platform squad
Integration for API audit logging
events (in progress)
Real-time event data
streaming to store in
Cassandra cluster
Cassandra Distributed
Data platform
OCBB Cassandra squad
Fraud detection internet
and mobile banking
Fraud Tribe detection squad
MobileAPI’s
OCH mobile app squads
Fraud Tribe cyber security / ISP
Cyber security systems
(ArcSight / ELK / Hadoop)
TracING / Wasstraten
Application monitoring
(ELK, log4all and SpotOn)

10. As Kafka became more important, and had higher availabity
requirements, we started to implement clusters.
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EventBus – Kafka & ZooKeeper
producers
consumers
DC 1 – AZ1 DC 1 – AZ1 or AZ2DC 1 – AZ2
producersproducers
consumersconsumers

11. With the need for events across countries or regions, we set
up a global event bus structure using mirror maker
Local Cluster in NLApplication NL
Application NL
Application NL
Application DE
Application DE
Application DE
Local Cluster in PL
Application PL
Application PL
Application PL
Global Cluster
Local Cluster in DE
Principles:
• Logically there is ONE Event bus for ING
• Each region/DC pair has its own local cluster.
• Local producer/consumer only produces/consumes to/from the local
cluster.
• There is only one global cluster. Local clusters will have mirror
makers to produce/consume to the global cluster.
• Local clusters do not produce/consume to/from among each other.
• Only global business relevant events (global topic) will be published
to the global cluster.
• Local clusters can subscribe to certain global topics.

12. I was very much triggered by this tweet from Neha
12

13. But I have a different opinion on where we are in this journey
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Ferd Scheepers, Chief Information Architect ING

14. Regulations and usability of Events across multiple regions
drive the need for meta-data, governance and lineage.
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Raw events
Business-relevant Events
Regional Global
Kafka Event Bus

15. Thank you!