A complete event-driven architecture with Kafka event bus, CQRS read/write separation, event store, and async consumers — ready to customize.
Copy this Mermaid code into Cybewave Studio or any Mermaid-compatible editor.
graph TD
Producer1[Order Service] -->|OrderCreated| EB[Event Bus - Kafka]
Producer2[User Service] -->|UserRegistered| EB
Producer3[Payment Service] -->|PaymentProcessed| EB
EB --> ES[(Event Store)]
EB --> Consumer1[Inventory Consumer]
EB --> Consumer2[Notification Consumer]
EB --> Consumer3[Analytics Consumer]
EB --> Consumer4[Search Indexer]
Consumer1 --> InvDB[(Inventory DB)]
Consumer2 --> Email[Email / Push]
Consumer3 --> DW[(Data Warehouse)]
Consumer4 --> Elastic[(Elasticsearch)]
subgraph CQRS
WriteModel[Write Model - Commands] --> EB
EB --> ReadModel[Read Model - Projections]
ReadModel --> ReadDB[(Read DB - Materialized Views)]
end
API[API Gateway] --> WriteModel
API --> ReadDBbattle-tested patterns for async, event-sourced systems.
Producers and consumers are decoupled through the event bus — services evolve independently.
Separate read and write models for optimal query performance and independent scaling.
Persistent event store captures every state change for auditing, replay, and debugging.
Add new consumers without modifying existing services — analytics, search, notifications.
Event-driven architecture (EDA) is a design pattern where services communicate by producing and consuming events through an event bus like Kafka. It enables loose coupling, scalability, and real-time processing.
Event sourcing stores every state change as an immutable event. CQRS separates read and write models. They are often used together — events are the write model, and projections build optimized read models.
Yes. Open it in Cybewave Studio and ask the AI to add saga orchestration, dead-letter queues, schema registry, or any other EDA pattern.
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