I’ve been building distributed systems for over a decade, and recently I faced a critical challenge where traditional database approaches fell short. Our team was struggling with data consistency across microservices, and auditing requirements were becoming increasingly complex. That’s when I rediscovered event sourcing—a pattern that fundamentally changed how we handle data persistence. Today, I want to share my practical experience building robust event sourcing systems using Spring Boot, Axon Framework, and Apache Kafka.

Have you ever considered what happens when you lose the history of your data changes? Event sourcing addresses this by storing every state change as an immutable event. Instead of just keeping the current balance of a bank account, for example, you store the complete sequence of deposits, withdrawals, and transfers. This approach gives you an audit trail by default and enables powerful features like temporal queries.

Let me show you a basic event structure. In a banking application, instead of just storing the current account balance, we might define events like this:

public class AccountCreatedEvent {
    private final String accountId;
    private final BigDecimal initialBalance;
    private final Instant timestamp;
}

public class MoneyDepositedEvent {
    private final String accountId;
    private final BigDecimal amount;
    private final Instant timestamp;
}

Each event represents something that happened in the past, and they’re stored in sequence. When you need the current state, you replay these events to rebuild it. This might sound inefficient, but modern frameworks optimize this process significantly.

Why would you choose this over traditional CRUD? Well, have you ever needed to debug why a particular data state occurred? With event sourcing, you have the complete history. You can reconstruct the system’s state at any point in time, which is invaluable for troubleshooting and compliance.

Setting up the project requires careful dependency management. Here’s a condensed version of my typical Maven configuration:

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.axonframework</groupId>
        <artifactId>axon-spring-boot-starter</artifactId>
        <version>4.9.0</version>
    </dependency>
    <dependency>
        <groupId>org.springframework.kafka</groupId>
        <artifactId>spring-kafka</artifactId>
    </dependency>
</dependencies>

Axon Framework handles much of the heavy lifting for event sourcing. It provides annotations and components that make implementing the pattern straightforward. For instance, defining an aggregate—the central domain object—becomes clean and declarative.

What about handling complex business workflows that span multiple services? That’s where sagas come in. Sagas manage long-running processes by coordinating multiple events and commands. In Axon, you can implement them using annotation-based handlers that respond to events and trigger subsequent actions.

Here’s a simple saga example that might handle a money transfer between accounts:

@Saga
public class MoneyTransferSaga {
    @StartSaga
    @SagaEventHandler(associationProperty = "transferId")
    public void handle(TransferInitiatedEvent event) {
        // Start the transfer process
    }
}

Integration with Apache Kafka enables distributed event streaming. This is crucial for scaling your system across multiple instances. Events published to Kafka can be consumed by various services, each building their own projections or reacting to changes.

But how do you ensure performance doesn’t suffer with all this event replay? Projections—read-optimized views of your data—are key. They’re built from events and stored separately, allowing fast queries without impacting the write path. You can use different databases for projections based on query patterns.

Testing event-sourced systems requires a different mindset. You need to verify not just the current state, but the entire event sequence. Axon provides testing utilities that help validate command handling and event emission.

Monitoring is another critical aspect. Since events are immutable, you need good observability into your event store and projection builders. Distributed tracing and metrics collection become essential for understanding system behavior.

In my implementations, I’ve found that proper error handling and idempotency are vital. Since events might be replayed or processed multiple times, your handlers must be resilient to duplicates. This is where idempotent operations save the day.

What challenges might you face? Event versioning is a common one. As your domain evolves, events might change structure. You need strategies for migrating old events or handling multiple versions gracefully.

Another consideration is snapshotting—periodically storing the current state to avoid replaying all events from the beginning. Axon supports this out of the box, significantly improving performance for aggregates with long event histories.

I’ve deployed these systems in production environments handling millions of events daily. The combination of Spring Boot’s simplicity, Axon’s powerful abstractions, and Kafka’s reliability creates a solid foundation. The initial learning curve pays off in maintainability and flexibility.

Remember that event sourcing isn’t a silver bullet. It adds complexity that might be overkill for simple CRUD applications. But for domains requiring strong audit trails, temporal queries, or complex business workflows, it’s transformative.

I’d love to hear about your experiences with event sourcing. What challenges have you faced, and how did you overcome them? If this article helped clarify the approach, please share it with your team and leave a comment below—your feedback helps me create better content for our community.