Samples are using CQRS architecture. They're sliced based on the business modules and operations. Read more about the assumptions in "How to slice the codebase effectively?".

• Simplest CQRS and Event Sourcing flow using Minimal API,
• Cutting the number of layers and boilerplate complex code to bare minimum,
• Using all Marten helpers like WriteToAggregate, AggregateStream to simplify the processing,
• Examples of all the typical Marten's projections,
• Example of how and where to use C# Records, Nullable Reference Types, etc,
• No Aggregates. Commands are handled in the domain service as pure functions.

• typical Event Sourcing and CQRS flow,
• DDD using Aggregates,
• microservices example,
• stores events to Marten,
• distributed processes coordinated by Saga (Order Saga),
• Kafka as a messaging platform to integrate microservices,
• example of the case when some services are event-sourced (Carts, Orders, Payments) and some are not (Shipments using EntityFramework as ORM)

• typical Event Sourcing and CQRS flow,
• functional composition, no aggregates, just data and functions,
• stores events to EventStoreDB,
• Builds read models using Subscription to $all,
• Read models are stored as Postgres tables using EntityFramework.

• typical Event Sourcing and CQRS flow,
• DDD using Aggregates,
• stores events to EventStoreDB,
• Builds read models using Subscription to $all.
• Read models are stored as Marten documents.

• simplest CQRS flow using .NET 5 Endpoints,
• example of how and where to use C# Records, Nullable Reference Types, etc,
• No Event Sourcing! Using Entity Framework to show that CQRS is not bounded to Event Sourcing or any type of storage,
• No Aggregates! CQRS do not need DDD. Business logic can be handled in handlers.

Variation of the previous example, but:

• using Minimal API,
• example how to inject handlers in MediatR like style to decouple API from handlers.
• 📝 Read more CQRS is simpler than you think with .NET 6 and C# 10

Shows how to handle basic event schema versioning scenarios using event and stream transformations (e.g. upcasting):

• Simple mapping
  • New not required property
  • New required property
  • Renamed property
• Upcasting
  • Changed Structure
  • New required property
• Downcasters
• Events Transformations
• Stream Transformation
• Summary

Shows how to compose event handlers in the processing pipelines to:

• filter events,
• transform them,
• NOT requiring marker interfaces for events,
• NOT requiring marker interfaces for handlers,
• enables composition through regular functions,
• allows using interfaces and classes if you want to,
• can be used with Dependency Injection, but also without through builder,
• integrates with MediatR if you want to.

• typical Event Sourcing and CQRS flow,
• DDD using Aggregates,
• microservices example,
• stores events to Marten,
• Kafka as a messaging platform to integrate microservices,
• read models handled in separate microservice and stored to other database (ElasticSearch)

• typical Event Sourcing and CQRS flow,
• DDD using Aggregates,
• stores events to Marten.

• typical Event Sourcing and CQRS flow,
• DDD using Aggregates,
• stores events to Marten,
• asynchronous projections rebuild using AsynDaemon feature.