Gerard Klijs
From event streaming to event sourcing 101
#1about 5 minutes
Understanding event streaming versus event sourcing
Event sourcing treats every state change as an immutable event, providing a full history, unlike traditional database-centric approaches.
#2about 4 minutes
Using change data capture for real-time alerts
A practical example shows how to stream database changes from a mainframe into Kafka to power a real-time customer alerting system.
#3about 4 minutes
Decoupling microservices with event streams
Kafka is used to break apart a monolith, enabling independent services for profiling and notifications, but highlights the challenge of understanding state changes from generic document updates.
#4about 2 minutes
Building a one-way data pipeline for analytics
An architecture for a data-sharing platform uses a one-way event stream to populate MongoDB and Elasticsearch, allowing indexes to be rebuilt from a single source of truth.
#5about 5 minutes
Implementing a CQRS banking demo with Kafka
A demo project illustrates a CQRS pattern using Kafka as an event store, revealing challenges with error handling, schema management, and event replayability.
#6about 5 minutes
Adopting the Axon framework for true event sourcing
The Axon framework provides a dedicated event store and battle-tested patterns that solve common event sourcing problems like error handling, command routing, and event replay.
#7about 1 minute
Key takeaways on adopting event sourcing
The primary advantage of adopting a full event sourcing model is the ability to trace every system change back to a specific command and its resulting events.
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