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Kafka in the Enterprise -
Our Two-Year Journey to Build a Data Streaming Platform
Christopher Arthur, Product Owner / Lead Architect
Benny Lee, Solutions Architect
1
2
3
4
5
6
Commonwealth Bank of Australia
Agenda
About us
Where it all started?
Use Cases
Where we are now
Lessons we have learnt (so
far…..)
2
Questions
About us
• Founded in 1911
• Australia's leading provider of financial services.
• Total assets – over $700 billion in USD
3
Source: Based on the Banks Daily 2018 ranking as at May 31, 2018
Where it all started?
• Current Integration Patterns
4
• Digital growth
• Analytical workload demand
• Real time fraud monitoring and detection
• Regulatory and compliance demand
Where it all started?
• Low Cost Reads Project
5
Adoption Rate
6
0
20
40
60
80
2016 2017 2018
Number of Projects
Over 400% YTY
increase in demand
7
Event Hub
WealthRetail
Banking
Business &
Private Banking
Online
Brokerage
Institutional
Banking &
Markets
8
Use Cases
• Customer notifications
• Workflow initiation
• Account fulfilment
• Customer on boarding notifications
• Auditing and Fraud detection for online transactions
• Real time payment registration and maintenance
• Payment enrichment
• Task replication
9
New Payments Platform (NPP)
• Launch in February 2018
• Make or receive payments in real-time, 24/7
• Addressing Service (PayID)
› Initiate payments using mobile number, email address or Australian Business
Number (ABN)
10
NPP – PayID Registration
11
NPP – Payment Enrichment
12
Task Replication
13
Where we are now
• Event Driven Architecture
14
Top Lessons we have learnt (so far…..)
1. Governance and Self Service
2. Message duplication
15
Governance and Self Service
16
17
Topic
Creation
Topic
Discovery
Data
Lineage
Viewing
Message
Topic
Ownership
Topic
Monitoring
When supporting multiple teams
18
Topic management
19
Topic management
20
Topic Viewer
21
Topic Viewer
22
Data Lineage
• How do we keep track where
messages are coming from?
• How do we keep track how
messages are moving across
multiple topics?
• Multiple applications publish messages into Kafka
23
Data Lineage
Message duplication ( and message duplication…)
24
• Messages persist in Kafka
• Consuming Application might ‘accidentally’ reprocess all the messages again
› Default offsets retention (offsets.retention.minutes)!
• Consequences: Negative impacts on the Platform
› Reduce adoption
› Bad reputation
Message duplication
25
• Consider……..
• Topic retention period
• Change offsets.retention.minutes (and auto.offset.reset)
• Use Kafka ”exactly once” feature
• Ensure downstream applications are idempotent.
• Deduplicate messages before sending to downstream applications.
• Duplication needs to be considered in every use case end to end
• Understanding the impact if duplication occurs
• Mitigation procedure
• Testing, testing and testing!
Thank you
Questions?

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