Message Brokers – Kafka, RabbitMQ, SQS, EventBridge

Modern E-commerce systems are inherently distributed. A single customer action—like placing an order—can trigger dozens of behind-the-scenes workflows: inventory updates, payment authorization, fraud evaluation, recommendation updates, analytics ingestion, warehouse routing, email notifications, and loyalty point calculation.

Executing all of this synchronously would slow down the customer experience and overload backend services.

To solve this, organizations rely on message brokers and event-driven architectures. These systems enable services to communicate asynchronously, decoupling producers and consumers and allowing workloads to scale independently.

Among the most widely adopted brokers are Kafka, RabbitMQ, SQS, and EventBridge. Each offers unique trade-offs and operational models suited to different needs.

Why Message Brokers Matter

A message broker acts as a durable buffer and routing layer between services. Instead of direct synchronous communication, services publish messages or events that are later processed by one or more consumers. This enables loose coupling, fault tolerance, high throughput, scalability, and event replayability.

In E-commerce, this pattern ensures that heavy back-office operations never block the customer's interaction. The user sees "Order Successful" instantly, while dozens of downstream processes continue asynchronously.

Example: Order Placement Workflow The moment an order is placed, the Order Service publishes an event like:

ORDER_PLACED { orderId: "O123", userId: "U55", amount: 2999.0 }

This event flows through a broker and is consumed by:

email service (send confirmation mail),
analytics service (record purchase),
warehouse service (start fulfillment),
fraud detection service (risk scoring),
loyalty service (points calculation),
recommendation engine (update signals).

Message brokers absorb unpredictable spikes—such as during festive sales—ensuring downstream systems aren't overwhelmed.

With context established, let's explore the major message brokers powering this architecture.

Kafka – The Distributed Commit Log for High-Throughput Event Streams

Apache Kafka is designed as a distributed, horizontally scalable, append-only commit log. It is the backbone of many high-throughput, event-driven architectures.

Kafka excels in environments where:

millions of events per second must be processed,
consumers need to read streams at their own pace,
replayability and durability are critical,
distributed microservices need real-time pipelines.

In E-commerce, Kafka is typically the central nervous system for:

order lifecycle events,
inventory movement across warehouses,
clickstream analytics,
real-time fraud scoring,
search index updates,
personalization and recommendation modeling.

Kafka's design emphasizes stream processing rather than simple queueing. It is ideal for architectures that treat events as immutable facts that must be replayed or processed in sequence.

RabbitMQ – The Traditional Message Queue for Transactional Workflows

RabbitMQ is a broker built around the AMQP protocol and excels at message delivery semantics like acknowledgments, routing, retries, and dead-letter queues. It supports flexible routing patterns (direct, topic, fanout) and excels at reliable delivery for individual tasks.

In E-commerce, RabbitMQ shines in:

email notifications,
order PDF generation,
payment receipt creation,
one-off tasks requiring guaranteed delivery,
workflows that depend on complex routing logic.

While Kafka focuses on streaming and replayability, RabbitMQ focuses on message routing and guaranteed task execution. It's a strong choice when you need immediate worker consumption rather than long-lived streams.

Amazon SQS – A Fully Managed, Serverless Queue

Amazon SQS is a serverless, fully managed queueing service that abstracts away infrastructure. It is known for its simplicity: no cluster to manage, no brokers to tune, no partitions to balance.

SQS fits perfectly into E-commerce platforms running on AWS because:

it scales automatically,
it guarantees at-least-once delivery,
costs grow with usage but remain predictable,
it integrates seamlessly with Lambda, EC2, ECS, and SQS-based worker pools.

SQS is ideal for:

asynchronous order tasks,
background catalog updates,
image resizing pipelines,
retry and dead-letter flows,
transactional email offloading.

SQS doesn't provide streaming or replayability like Kafka, but it offers exceptional reliability for discrete message workloads.

Amazon EventBridge – Event Bus for SaaS + Microservice Integrations

EventBridge is an event bus designed for system-level integration rather than raw throughput. Unlike Kafka or RabbitMQ, EventBridge shines when:

multiple microservices must react to the same event,
events must be routed automatically based on rules,
integration with AWS services or SaaS platforms (Shopify, Zendesk, Auth0) is needed.

EventBridge is widely used in E-commerce for:

propagating order events across internal and external services,
triggering serverless workflows,
auditing user activity events,
automating back-office operations,
decoupling 3rd-party services (payment gateways, CRM, ERP).

Where Kafka is a high-speed highway, EventBridge is an event router optimized for orchestrating distributed event flows.

Understanding Delivery Guarantees

Message brokers differ in how they deliver messages to consumers. Delivery guarantees determine whether a message might be lost, duplicated, or delivered exactly once.

Let's break down the three guarantees:

1. At Least Once Delivery

The broker guarantees that a message will be delivered at least one time, but it may be delivered more than once if retries occur.

In other words, no message is lost, but duplicates may appear. This is the most common guarantee across message brokers because it prioritizes durability and reliability.

Why does duplication happen?

- A consumer reads a message but crashes before acknowledging.
- A network partition causes the broker to resend.
- The broker retries because it didn't receive an ACK in time.

Example: If ORDER_PLACED is delivered twice to an analytics service, it may record the order twice unless duplicates are handled. For critical flows (payments, order confirmation), the service must be idempotent.

Almost all brokers deliver at least once by default:

Kafka
RabbitMQ
Amazon SQS
EventBridge

This ensures durability but shifts duplicate handling to the consumer.

2. Exactly Once Delivery (Kafka Streams)

A message is processed once and only once, without duplication and without loss. This is extremely hard in distributed systems because:

- Networks fail
- Consumers crash
- Retries cause duplicates
- Side effects cannot always be rolled back

Kafka achieves exactly-once semantics (EOS) only under specific controlled conditions:

- Using Kafka Streams or Kafka transactions
- Writing to Kafka topics (not external DBs)
- Atomic read-process-write cycles

Kafka's EOS works because:

- Offsets, state, and outputs are committed transactionally
- Kafka controls the entire pipeline end-to-end

Example: A fraud detection service processing clickstream events must not process the same event twice or risk false alerts. Kafka Streams provides this by maintaining coordinated state stores.

Important: No distributed system offers "global" exactly-once across all external systems. I's exactly-once within Kafka.

3. Exactly Once (RabbitMQ with Plugins)

RabbitMQ supports exactly-once delivery only through:

- Specialized plugins
- Careful storage configuration
- Transactional semantics

But in practice, RabbitMQ's default is at least once, and achieving EOS is tricky due to:

- Message acknowledgment timing
- Consumer/producer crashes
- Side-effect handling in downstream systems

Most teams use idempotency at the consumer layer rather than relying on RabbitMQ's EOS.

4. FIFO & Strict Ordering (SQS FIFO)

Amazon SQS FIFO queues guarantee:

- Exactly-once processing (per message group)
- Strict ordering within each group

This works because AWS:

- Deduplicates based on message IDs
- Ensures messages of the same group are delivered sequentially
- This is ideal for workloads needing strict sequence processing.

AWS SQS FIFO prevents duplicate messages by using a MessageDeduplicationId (or a hash of the message body). If the same ID appears again within a short window, SQS drops the duplicate instead of enqueueing it. This ensures producers don’t accidentally send the same message twice during retries.

Example: Consider updating a product's inventory count:

+5 restock
-2 order placed
-1 return processed

The order must remain consistent. SQS FIFO ensures these operations are executed in the right order without duplication.

Conclusion

Message brokers are not interchangeable—they reflect architectural intent.

Each broker plays a role in building a platform that remains resilient, scalable, and adaptable—even under the chaotic load spikes of a holiday sale.