Gerard Klijs

From event streaming to event sourcing 101

Stop inferring state changes between microservices. Learn to capture the 'why' behind every event by moving from event streaming to event sourcing.

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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