• One service going down is taking other services with it because your microservices call each other directly?

  • Your async processing is running on a makeshift queue that loses messages when the worker crashes?

Event-Driven Architecture Development

RaftLabs designs and builds event-driven systems using Apache Kafka, RabbitMQ, and event bus patterns for microservices communication, asynchronous processing, and real-time event propagation across services. We replace direct service-to-service API calls with loosely coupled event flows that scale independently and recover from failure without cascading downtime.
Every engagement starts with an architecture review: we map your current service dependencies, identify where tight coupling is creating bottlenecks or fragility, and design the event topology that solves those problems without introducing unnecessary operational complexity.

  • Kafka and RabbitMQ design for your specific throughput and ordering requirements

  • Event sourcing and CQRS patterns for systems that need a reliable audit trail

  • Microservices decoupling -- services communicate through events, not direct API calls

  • Designed for failure: dead letter queues, retry policies, and poison message handling included

RaftLabs designs event-driven architectures using Apache Kafka and RabbitMQ for microservices communication, async processing, and real-time event propagation. We deliver event sourcing, CQRS patterns, and message broker infrastructure for systems that need to scale independently and recover gracefully from failure.

Vodafone
Aldi
Nike
Microsoft
Heineken
Cisco
Calorgas
Energia Rewards
GE
Bank of America
T-Mobile
Valero
Techstars
East Ventures

When microservices call each other directly, they create a dependency graph that looks fine in the diagram and becomes a reliability problem in production. Service A calls Service B synchronously; Service B is slow or down; Service A times out; the user sees an error. Multiply that pattern across a dozen services and you have a system where any single failure propagates to the users of every service that depends on it, directly or transitively.

Event-driven architecture breaks that dependency graph. Services publish events to a broker -- something happened -- and other services subscribe to the events they care about and react asynchronously. The publishing service does not know or care which services are listening. A subscriber going down does not affect the publisher or any other subscriber. And the event log becomes an audit trail, a replay mechanism, and a foundation for the kind of event sourcing and CQRS patterns that make complex business logic testable and auditable. RaftLabs designs these systems from the topology down: broker selection, partition strategy, schema design, consumer group layout, and the failure handling that makes them production-grade.

What we build

Event streaming with Apache Kafka

Kafka cluster design, topic and partition strategy, producer and consumer implementation, and consumer group topology for your specific throughput and ordering requirements. We define your event schema format -- Avro, Protobuf, or JSON Schema with a schema registry -- and design the retention policy and compaction strategy that balances storage cost with your replay requirements.

RabbitMQ message queue architecture

Exchange, queue, and binding topology for RabbitMQ deployments. Direct, fanout, topic, and header exchange configurations for complex routing scenarios. Dead letter queue setup, message TTL policies, priority queue configuration, and consumer concurrency design. We configure the broker for your reliability requirements and document the routing logic so your team can extend it without reintroducing tight coupling.

Event sourcing and CQRS patterns

Event sourcing architecture where every state change is stored as an immutable event in an append-only log, giving you a complete, replayable audit trail of everything that has ever happened. CQRS pattern implementation separating the write model (commands and events) from the read model (projections optimised for query), so your read performance scales independently from your write throughput.

Microservices event bus design

Event bus topology for systems where multiple services need to react to the same business events without knowing about each other. We define the event contract, design the producer and consumer interfaces, and establish the schema evolution policy so services can be updated independently without breaking the contract. Domain event design that maps your business processes to a clean, versioned event schema.

Async workflow orchestration

Long-running business process orchestration using event-driven state machines. Order fulfilment, approval workflows, data processing pipelines, and any multi-step process that spans multiple services and must recover gracefully from partial failures. Saga pattern implementation with compensating transactions so your system can roll back consistently when a step fails mid-process.

Event-driven integration with third-party systems

Event-driven integration layers for external APIs, SaaS platforms, and legacy systems that do not speak your internal event protocol. Webhook ingestion with idempotency and retry handling, change data capture (CDC) from relational databases to Kafka, and outbox pattern implementation so your database writes and event publications are always consistent with each other.

Tight coupling between your services is a reliability risk.

Tell us how your services currently communicate and where failures propagate. We will assess whether event-driven architecture is the right fix and what it would take to introduce it without a full rewrite.

Talk about your event-driven architecture project

Frequently asked questions

Direct synchronous API calls are appropriate when the calling service genuinely needs an immediate response before it can continue -- a payment authorisation check, a stock availability lookup at checkout. Event-driven architecture is the right choice when services need to react to something that happened without the originating service needing to wait for those reactions: an order placed triggers fulfilment, email, and analytics processes that can all run independently. The key signal is whether you are asking a question (synchronous) or announcing a fact (event). If you have services calling each other in chains, or one service causing another to fail when it goes down, that is a signal your synchronous calls should be events.

Kafka is a distributed event log: events are written to a durable, ordered, partitioned log and consumers read from any point in that log at their own pace. It excels at high-throughput event streaming, event replay, and use cases where multiple independent consumer groups need to process the same event stream differently. RabbitMQ is a message broker built around queues and routing: it excels at work distribution, complex routing rules, and use cases where a message should be processed by exactly one consumer and then deleted. Kafka is the right choice for event sourcing, audit trails, and stream processing at scale. RabbitMQ is the right choice for task queues, dead letter handling, and complex message routing between services.

Exactly-once delivery is a guarantee that most message brokers cannot provide without trade-offs -- the standard target is at-least-once delivery combined with idempotent consumers that handle duplicate events correctly. We design your event consumers to be idempotent from the start: each event carries a unique ID, consumers check whether they have already processed that ID before acting, and the idempotency key is stored in the same transaction as the resulting state change. For ordering, Kafka provides per-partition ordering guarantees, and we design the partition key strategy so that events that must be ordered relative to each other land in the same partition.

Introducing event infrastructure for a specific integration -- replacing a synchronous call between two services with an event-driven pattern, including the broker setup, schema definitions, producer, and consumer -- typically runs $25,000 to $80,000. Re-architecting a full system from synchronous microservices to an event-driven model, including event sourcing, CQRS, and multi-service broker topology, ranges from $80,000 to $200,000. We scope the engagement with a clear event topology diagram before providing a fixed-cost quote.