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.

#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.