When you order something online, a lot happens — order created, payment confirmed, address updated, shipped, delivered. But if the system only stores the final state, you’d know it was delivered but lose the story of how it got there. That’s the limitation of traditional CRUD (Create, Read, Update, Delete): it shows the present but erases the past.
Event Sourcing flips that model. Every change is stored as an immutable event, giving you a complete, replayable history — like a bank ledger instead of just today’s balance. This means better auditing, debugging, analytics, and the ability to evolve your system without rewriting history.
And with AWS services like DynamoDB, EventBridge, and Lambda, event sourcing fits naturally into the serverless, event-driven world, where scalability and cost-efficiency are built in.
In this article, we’ll explore how to move beyond basic CRUD and design serverless event-sourced systems on AWS — unlocking capabilities that CRUD can’t deliver.
At its core, Event Sourcing is about storing events instead of just state. Instead of updating a record to reflect the latest value, every change is captured as a new, immutable event.
Think of it like this:
status = "Shipped"OrderCreatedOrderPaidOrderShippedThe current state can always be reconstructed by replaying these events, but the real power lies in the history. You can answer questions like:
This approach turns your database into a source of truth for the past, present, and future. You can not only see what happened, but also replay events to rebuild projections, feed analytics pipelines, or even drive new features without touching existing data.
Event Sourcing is not just about data storage — it’s about preserving the story of your system.
Designing event sourcing comes down to two core questions: how do you store events, and how do you use them?
On AWS, a common approach is to use DynamoDB as the event store. Since events are usually unstructured or vary over time, DynamoDB’s schemaless design makes it a natural fit. Every new change in the system is appended to this table as an immutable event.
From there, AWS Lambda acts as the consumer. By enabling DynamoDB Streams, you can automatically trigger a Lambda whenever new events are written. This means your application can react to changes in near real time — for example, updating projections, sending notifications, or publishing to an event bus.
In the diagram below, events arrive through a REST API exposed via API Gateway. The API writes them into the DynamoDB event store. Each new entry then flows through the DynamoDB Stream → Lambda consumer, which processes the event.
But there’s a challenge. In an event-sourced system, the database doesn’t store the final state — only the sequence of events. To answer a simple question like “What’s the current status of this order?”, you’d have to replay all past events for that aggregate. Doing this repeatedly is both inefficient and expensive.
This is where the CQRS (Command Query Responsibility Segregation) pattern comes in. With CQRS, you separate writes from reads:
This read model looks like a traditional transactional database — fast, queryable, and easy to consume. Clients now query this projection for the latest state, while the event store continues to capture the full history.
In short:
By combining Event Sourcing + CQRS, you get the best of both worlds: complete historical traceability and efficient real-time reads.
Event sourcing isn’t just about storing data differently — it unlocks capabilities that traditional CRUD models struggle with.
Every event is preserved, giving you a full history of what happened, when, and by whom (ex. finance domain). This makes compliance, debugging, and forensics far easier than with systems that only store the latest state.
Since events are immutable, you can replay them at any time to rebuild projections or recover from failures. If a read model gets corrupted, just clear it and replay the event stream to restore it.
CRUD can only answer “What is the state now?”. Event sourcing lets you ask “What was the state at this point in time?”. For example, you can see what an order looked like yesterday at 3 PM or trace how it evolved over time.
New features often require new ways of looking at data. With event sourcing, you don’t have to redesign your schema — just build a new projection by replaying existing events. This allows you to evolve the system without rewriting history.
Event sourcing aligns perfectly with modern, serverless, event-driven systems. Events can be published to an event bus (like Amazon EventBridge) and consumed by multiple independent services without tight coupling.
While event sourcing is powerful, it’s not without its complexities. Before adopting it, teams should carefully weigh the trade-offs:
In short, event sourcing unlocks powerful capabilities like auditing, traceability, and replay — but comes with operational overhead and design challenges that need to be managed with care.
Event Sourcing is powerful, but it’s not always the right tool. You should consider it when:
When to avoid it:
In short: use Event Sourcing when auditability, scalability, and flexibility outweigh the complexity.
Event Sourcing brings a powerful way of modeling applications by treating state changes as a sequence of immutable events. It enables auditability, flexibility, and scalability, making it a natural fit for modern cloud-native architectures on AWS. However, it also comes with challenges such as event ordering, schema evolution, and operational complexity that require careful design.
When applied in the right context — such as systems needing strong audit trails, historical state reconstruction, or real-time insights — Event Sourcing can significantly elevate the reliability and adaptability of your applications.
In short, Event Sourcing isn’t about replacing CRUD everywhere. It’s about choosing the right paradigm for the right problem. By combining Event Sourcing with supporting patterns like CQRS, and leveraging AWS services like DynamoDB, EventBridge, Kinesis, and Lambda, teams can build event-driven systems that are both resilient and future-ready.