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Ensure event ordering and delivery\n\n## When to use me\nWhen building loosely coupled, scalable systems, microservices, or real-time processing applications.\n\n## Core Concepts\n\n### Event-Driven Architecture\n- Producers emit events\n- Consumers react to events\n- Decoupled communication\n- Temporal coupling eliminated\n\n### Event Types\n- **Domain Events**: Business meaningful\n- **Integration Events**: Cross-service communication\n- **Change Data Capture**: Database changes\n- **System Events**: Infrastructure events\n\n### Message Patterns\n\n#### Point-to-Point\n- One producer → One consumer\n- Queue-based\n- Guaranteed processing\n\n#### Pub\u002FSub\n- One producer → Multiple consumers\n- Topic-based\n- Event notification\n\n## Message Brokers\n\n### Apache Kafka\n- Distributed event streaming\n- High throughput\n- Persistence\n- Exactly-once semantics\n- Use cases: Log aggregation, CDC, Event streaming\n\n### RabbitMQ\n- Traditional message broker\n- Complex routing\n- Multiple protocols (AMQP, MQTT, STOMP)\n- Use cases: Task queues, RPC\n\n### AWS SQS\n- Managed queue service\n- FIFO queues\n- Dead letter queues\n- Use cases: Microservices, Batch jobs\n\n### NATS\n- Lightweight\n- Pub\u002Fsub, request\u002Freply\n- JetStream for persistence\n\n## Event Sourcing\n\n### Concept\n- Store events, not state\n- Rebuild state by replaying events\n- Complete audit trail\n- Time travel debugging\n\n### Implementation\n```csharp\n\u002F\u002F Command\npublic class PlaceOrderCommand {\n    public Guid OrderId;\n    public List\u003COrderItem> Items;\n}\n\n\u002F\u002F Event\npublic class OrderPlacedEvent {\n    public Guid OrderId;\n    public List\u003COrderItem> Items;\n    public DateTime PlacedAt;\n}\n\n\u002F\u002F Aggregate\npublic class Order {\n    private List\u003COrderPlacedEvent> _events = new();\n    \n    public void PlaceOrder(PlaceOrderCommand cmd) {\n        \u002F\u002F Validate\n        \u002F\u002F Create event\n        var evt = new OrderPlacedEvent(cmd.OrderId, cmd.Items, DateTime.UtcNow);\n        _events.Add(evt);\n    }\n}\n```\n\n### Benefits\n- Complete history\n- Temporal queries\n- Event replay\n- Audit trail\n- Performance\n\n### Challenges\n- Event schema evolution\n- Eventual consistency\n- Performance with many events\n- CQRS required\n\n## CQRS (Command Query Responsibility Segregation)\n\n### Pattern\n- Separate read and write models\n- Different data structures\n- Synchronization via events\n\n### Implementation\n- Commands: Intent to change\n- Queries: Read data\n- Read models updated via events\n\n### Benefits\n- Optimized read\u002Fwrite\n- Scalability\n- Flexibility\n\n## Best Practices\n- Idempotent consumers\n- Handle duplicates\n- Order events appropriately\n- Use correlation IDs\n- Document event schemas (Avro, Protobuf)\n- Version events\n- Dead letter handling\n","## What I do\n- Design event-driven systems\n- Implement message queues and brokers\n- Use event sourcing patterns\n- Handle asynchronous messaging\n- Build event processing pipelines\n- Ensure event ordering and delivery\n\n## When to use me\nWhen building loosely coupled, scalable systems, microservices, or real-time processing applications.\n\n## Core Concepts\n\n### Event-Driven Architecture\n- Producers emit events\n- Consumers react to events\n- Decoupled communication\n- Temporal coupling eliminated\n\n### Event Types\n- **Domain Events**: Business meaningful\n- **Integration Events**: Cross-service communication\n- **Change Data Capture**: Database changes\n- **System Events**: Infrastructure events\n\n### Message Patterns\n\n#### Point-to-Point\n- One producer → One consumer\n- Queue-based\n- Guaranteed processing\n\n#### Pub\u002FSub\n- One producer → Multiple consumers\n- Topic-based\n- Event notification\n\n## Message Brokers\n\n### Apache Kafka\n- Distributed event streaming\n- High throughput\n- Persistence\n- Exactly-once semantics\n- Use cases: Log aggregation, CDC, Event streaming\n\n### RabbitMQ\n- Traditional message broker\n- Complex routing\n- Multiple protocols (AMQP, MQTT, STOMP)\n- Use cases: Task queues, RPC\n\n### AWS SQS\n- Managed queue service\n- FIFO queues\n- Dead letter queues\n- Use cases: Microservices, Batch jobs\n\n### NATS\n- Lightweight\n- Pub\u002Fsub, request\u002Freply\n- JetStream for persistence\n\n## Event Sourcing\n\n### Concept\n- Store events, not state\n- Rebuild state by replaying events\n- Complete audit trail\n- Time travel debugging\n\n### Implementation\n```csharp\n\u002F\u002F Command\npublic class PlaceOrderCommand {\n    public Guid OrderId;\n    public List\u003COrderItem> Items;\n}\n\n\u002F\u002F Event\npublic class OrderPlacedEvent {\n    public Guid OrderId;\n    public List\u003COrderItem> Items;\n    public DateTime PlacedAt;\n}\n\n\u002F\u002F Aggregate\npublic class Order {\n    private List\u003COrderPlacedEvent> _events = new();\n    \n    public void PlaceOrder(PlaceOrderCommand cmd) {\n        \u002F\u002F Validate\n        \u002F\u002F Create event\n        var evt = new OrderPlacedEvent(cmd.OrderId, cmd.Items, DateTime.UtcNow);\n        _events.Add(evt);\n    }\n}\n```\n\n### Benefits\n- Complete history\n- Temporal queries\n- Event replay\n- Audit trail\n- Performance\n\n### Challenges\n- Event schema evolution\n- Eventual consistency\n- Performance with many events\n- CQRS required\n\n## CQRS (Command Query Responsibility Segregation)\n\n### Pattern\n- Separate read and write models\n- Different data structures\n- Synchronization via events\n\n### Implementation\n- Commands: Intent to change\n- Queries: Read data\n- Read models updated via events\n\n### Benefits\n- Optimized read\u002Fwrite\n- Scalability\n- Flexibility\n\n## Best Practices\n- Idempotent consumers\n- Handle duplicates\n- Order events appropriately\n- Use correlation IDs\n- Document event schemas (Avro, Protobuf)\n- Version events\n- Dead letter 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