Course Objective & Scope
As synchronous service calls multiply, systems become fragile and harder to scale. Event-driven architecture reduces this coupling by decoupling components through events, but it introduces new problems such as delivery guarantees, ordering and consistency.
The course covers pub/sub and event streaming models, Apache Kafka (topics, partitions, consumer groups) compared with RabbitMQ, the transactional outbox, idempotent consumers, dead letter queues, choreography- and orchestration-based sagas, event sourcing with CQRS, and schema evolution. Participants model a business flow from their organisation with event storming and produce an event catalogue and an architecture decision record (ADR).
Who Should Attend?
- Backend developers building distributed systems
- Software and solution architects
- Integration and platform teams
- Prerequisite: experience building service-based applications
Key Takeaways
- Decide which interactions should be synchronous and which asynchronous
- Design Kafka topics and partitions for ordering and scale requirements
- Preserve data consistency with the outbox pattern and idempotent consumers
- Design distributed workflows and compensating steps with sagas
- Evolve event schemas in a backward-compatible way with a schema registry
Curriculum
Event Thinking and Event Storming
- Distinguishing events, commands and queries
- Discovering business flows with event storming
- Domain events vs integration events
Messaging Infrastructure
- Kafka: topics, partitions, offsets, consumer groups
- The RabbitMQ exchange and queue model
- Cloud options: Azure Service Bus, Amazon SNS/SQS
Delivery Guarantees and Consistency
- At-least-once, exactly-once and ordering
- Transactional outbox and idempotent consumers
- Retries, dead letter queues and poison messages
Sagas, Event Sourcing and CQRS
- Choreography- and orchestration-based sagas
- Rebuilding state with event sourcing
- Read models with CQRS
Schema Management and Traceability
- Avro/Protobuf and schema registry
- Correlation IDs and distributed tracing
- Monitoring consumer lag
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