Master CQRS & Event Sourcing in .NET 8 with AWS & Kafka - Guide

In modern cloud-native applications, handling complex business logic with scalability, fault tolerance, and maintainability is crucial. Traditional CRUD-based architectures often lead to performance bottlenecks and difficulties in managing data consistency across distributed systems.

The CQRS (Command Query Responsibility Segregation) and Event Sourcing patterns are powerful techniques to address these challenges.

This article will deep dive into:

• Implementing CQRS in .NET 8 using MediatR and Minimal APIs.
• Event Sourcing with Kafka for distributed event processing.
• Using AWS DynamoDB for event storage.
• Applying AWS Lambda to handle domain events.

By the end, you’ll have a robust CQRS and Event Sourcing-based system built using .NET 8 and AWS services. 🚀

---

1. Why Use CQRS and Event Sourcing?

✅ Separation of Concerns – Commands (writes) and Queries (reads) are handled separately.
✅ Performance Optimization – Read models are optimized for fast queries.
✅ Event Replayability – Can reconstruct state at any point in time.
✅ Scalability – Reads and writes can scale independently.
✅ Auditability – Complete history of all state changes.

Traditional architectures struggle with event-driven microservices, but CQRS + Event Sourcing makes it easy!

---

2. Setting Up CQRS in .NET 8

Step 1: Install Dependencies

We use MediatR to handle command and query segregation:

dotnet add package MediatR
dotnet add package MediatR.Extensions.Microsoft.DependencyInjection

Step 2: Define a Command and Query Model

Command Model (Writing Data)

public record CreateOrderCommand(string ProductId, int Quantity) : IRequest<string>;

Query Model (Reading Data)

public record GetOrderByIdQuery(string OrderId) : IRequest<OrderDto>;

---

3. Implementing CQRS Handlers

Step 1: Implement the Command Handler

Handles write operations and publishes events.

public class CreateOrderHandler : IRequestHandler<CreateOrderCommand, string>
{
    private readonly IEventStore _eventStore;
    
    public CreateOrderHandler(IEventStore eventStore)
    {
        _eventStore = eventStore;
    }

    public async Task<string> Handle(CreateOrderCommand request, CancellationToken cancellationToken)
    {
        var orderId = Guid.NewGuid().ToString();
        var orderCreatedEvent = new OrderCreatedEvent(orderId, request.ProductId, request.Quantity);

        await _eventStore.SaveEventAsync(orderCreatedEvent);
        return orderId;
    }
}

Step 2: Implement the Query Handler

Handles read operations efficiently.

public class GetOrderByIdHandler : IRequestHandler<GetOrderByIdQuery, OrderDto>
{
    private readonly IReadOnlyRepository _readRepository;

    public GetOrderByIdHandler(IReadOnlyRepository readRepository)
    {
        _readRepository = readRepository;
    }

    public async Task<OrderDto> Handle(GetOrderByIdQuery request, CancellationToken cancellationToken)
    {
        return await _readRepository.GetOrderByIdAsync(request.OrderId);
    }
}

✅ Writes are handled separately from reads!
✅ Reads fetch precomputed data for fast performance!

---

4. Implementing Event Sourcing with Apache Kafka

To ensure event-driven consistency, we use Kafka as an Event Store.

Step 1: Install Kafka Dependencies

dotnet add package Confluent.Kafka

Step 2: Define an Event Model

public record OrderCreatedEvent(string OrderId, string ProductId, int Quantity);

Step 3: Implement Kafka Producer for Publishing Events

public class KafkaEventProducer
{
    private readonly IProducer<string, string> _producer;

    public KafkaEventProducer()
    {
        var config = new ProducerConfig { BootstrapServers = "localhost:9092" };
        _producer = new ProducerBuilder<string, string>(config).Build();
    }

    public async Task PublishAsync(OrderCreatedEvent @event)
    {
        var message = JsonSerializer.Serialize(@event);
        await _producer.ProduceAsync("OrderEvents", new Message<string, string> { Key = @event.OrderId, Value = message });
    }
}

✅ All state changes are stored as immutable events in Kafka!
✅ Replayable and scalable event-driven architecture!

---

5. Storing Events in AWS DynamoDB

DynamoDB is ideal for event storage, as it supports fast, scalable, NoSQL data access.

Step 1: Create a DynamoDB Table for Events

aws dynamodb create-table --table-name OrderEvents \
--attribute-definitions AttributeName=OrderId,AttributeType=S \
--key-schema AttributeName=OrderId,KeyType=HASH \
--billing-mode PAY_PER_REQUEST

Step 2: Save Events to DynamoDB

Modify KafkaEventProducer to persist events:

public class DynamoDbEventStore : IEventStore
{
    private readonly DynamoDBContext _dbContext;

    public DynamoDbEventStore()
    {
        var client = new AmazonDynamoDBClient();
        _dbContext = new DynamoDBContext(client);
    }

    public async Task SaveEventAsync(OrderCreatedEvent @event)
    {
        await _dbContext.SaveAsync(@event);
    }
}

✅ Each event is stored, allowing for full history reconstruction!

---

6. Event Handling with AWS Lambda

AWS Lambda can react to new events and update read models.

Step 1: Create a Lambda Event Consumer

public class OrderEventHandler
{
    public async Task HandleEvent(KafkaEvent kafkaEvent)
    {
        var eventData = JsonSerializer.Deserialize<OrderCreatedEvent>(kafkaEvent.Value);
        
        // Update Read Model
        await UpdateReadModel(eventData);
    }

    private async Task UpdateReadModel(OrderCreatedEvent eventData)
    {
        // Save in optimized Read DB (e.g., Redis, SQL, or ElasticSearch)
    }
}

✅ Read models update asynchronously for faster performance!
✅ Only read from the optimized store, reducing DB load!

---

7. Securing and Monitoring the System

A. AWS Cognito for Authentication

aws cognito-idp create-user-pool --pool-name CQRSUserPool

Secure API endpoints with JWT authentication.

B. AWS CloudWatch for Monitoring

aws cloudwatch put-metric-alarm --alarm-name "HighEventProcessingTime" --metric-name "KafkaProcessingTime"

Set up alerts for latency or failures.

✅ Secured API endpoints with Cognito!
✅ Automatic monitoring for event processing health!

---

8. Deployment with AWS CDK

Define the entire CQRS infrastructure in AWS CDK.

var kafkaCluster = new MskCluster(this, "KafkaCluster");
var eventStore = new Table(this, "EventStore", new TableProps { TableName = "OrderEvents" });
var lambdaFunction = new Function(this, "OrderProcessor", new FunctionProps { Runtime = Runtime.DOTNET_8 });

Deploy everything in one command:

cdk deploy

✅ Fully automated, repeatable deployments!
✅ Infrastructure as Code (IaC) for reliability!

---

Conclusion

In this article, we built a scalable, event-driven .NET 8 application using:
✅ CQRS for separating writes and reads.
✅ Kafka for event streaming.
✅ DynamoDB for event persistence.
✅ AWS Lambda for asynchronous event processing.
✅ AWS CDK for automated deployment.

Next, we’ll explore real-time analytics with AWS Kinesis and .NET 8! 🚀