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 and builds event-driven architectures using Apache Kafka and RabbitMQ. We deliver microservices event bus topology, event sourcing and CQRS patterns, async workflow orchestration with saga patterns, dead letter queue handling, and change data capture pipelines. A focused integration -- replacing synchronous calls between two services with event-driven patterns -- runs $25,000-$80,000. Full system re-architecture from synchronous microservices to event-driven ranges from $80,000-$200,000. Fixed cost, scoped before development starts.
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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.
Capabilities
What we build
Event streaming with Apache Kafka
Apache Kafka cluster design, topic architecture, and producer/consumer implementation for high-throughput event streaming at production scale. Partition strategy designed around your ordering requirements: the partition key is the entity ID (order ID, customer ID, device ID) so all events for the same entity land in the same partition and are processed in order by the consumer, while events for different entities are processed in parallel across partitions. Consumer group topology: each distinct processing concern (fulfilment, analytics, notifications) gets its own consumer group reading the same topic independently, with separate committed offsets so a slow analytics consumer doesn't block the real-time fulfilment consumer. Confluent Schema Registry (or AWS Glue Schema Registry) for schema management with Avro or Protobuf encoding: backward-compatible schema evolution lets producers add fields without breaking existing consumers, and forward-compatible evolution lets consumers handle events from future producers. Topic configuration: replication factor 3 with min.insync.replicas=2 for durability; retention period set to match your replay window (7 days for operational replay, 90 days for audit trails); log compaction for event-sourced entity topics that only need the latest event per key. Producer idempotency and transactional APIs for exactly-once semantics when the same event must not be produced twice even if the producer restarts. Managed Kafka on Confluent Cloud, AWS MSK, or Aiven, or self-managed on Kubernetes via Strimzi operator depending on your control requirements and operational budget.
RabbitMQ message queue architecture
RabbitMQ exchange, queue, and binding topology designed for your routing complexity and reliability requirements. Exchange type selection by use case: direct exchange for point-to-point task distribution (payment processing, email sending), fanout exchange for broadcasting the same message to multiple queues (new-order event that triggers fulfilment, inventory, and notification queues simultaneously), topic exchange for pattern-based routing (order.placed.US.enterprise routed to separate queues for regional and customer-tier processing). Dead letter queue configuration: messages that are rejected, expire, or exceed the maximum delivery attempt count are routed to a dead letter exchange and queue for inspection and replay without being silently discarded -- the observability that turns "that message never arrived" into a visible, recoverable failure. Message TTL policies per queue: time-sensitive messages (notifications, live data updates) expired after a configurable window so stale messages don't process after they lose meaning. Consumer prefetch count (QoS setting) calibrated to the consumer's processing speed to prevent a fast producer overwhelming a slow consumer with unbounded in-memory messages. Consumer acknowledgement modes: manual ack with requeue-on-failure for critical processing (the message stays in the queue until the consumer explicitly confirms it was processed), auto-ack for fire-and-forget notifications where duplicate delivery is acceptable. RabbitMQ Cluster with quorum queues for HA: quorum queues replicate messages across a majority of cluster nodes, surviving node loss without message loss. CloudAMQP or RabbitMQ on Kubernetes (via the RabbitMQ Cluster Operator) depending on operational preference.
Event sourcing and CQRS patterns
Event sourcing architecture where every state change is stored as an immutable event in an append-only log rather than updating a mutable row -- giving you a complete, replayable audit trail of everything that has ever happened to every entity in the system, with the ability to reconstruct any historical state by replaying events to a point in time. Event store implementation using EventStoreDB (purpose-built event store with built-in projections and subscriptions) or PostgreSQL with an events table (aggregate_id, event_type, sequence_number, payload, created_at) depending on scale and operational preference. Snapshot strategy for aggregates with long event histories: every N events, a snapshot of the current state is written so replaying a high-event-count aggregate starts from the nearest snapshot rather than replaying from the beginning each time. CQRS (Command Query Responsibility Segregation) implemented alongside event sourcing: the write side (command handlers) processes commands, raises events, and appends them to the event store; the read side (projections) subscribes to the event stream and maintains denormalised read models optimised for each query pattern -- an order status view, a customer history view, and an analytics aggregate each maintained independently without the join overhead that would be required from a normalised write model. Projection rebuild capability: when a new read model is needed or an existing projection requires a schema change, the projection is rebuilt by replaying the event stream from the start -- the ability to ask new questions of historical data without retroactive migration.
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 -- the architecture that lets you add a new service that reacts to order.placed without modifying the order service or any other existing subscriber. Domain event catalog: every business event named using past-tense domain language (OrderPlaced, PaymentCaptured, ShipmentDispatched), with the event type, version, schema, producing service, and consuming services documented in a contract registry. Event envelope structure: a standard wrapper (event_id, event_type, aggregate_id, aggregate_type, occurred_at, schema_version, payload) applied to every event regardless of domain -- making serialisation, routing, and consumer filtering consistent across the system. Schema evolution policy: v1 events remain supported until all consumers have migrated, new required fields added as optional with a defined deadline for consumer updates, breaking changes published as v2 events with a migration period where both versions are produced. CloudEvents standard specification used as the event envelope format where cross-organisation or cross-platform interoperability is required. Consumer contract testing with Pact: consumer publishes its expectations of the event schema, producer CI validates that the produced event satisfies all registered consumer contracts before merging -- preventing the producer from shipping a schema change that silently breaks a consumer in a separate repository.
Async workflow orchestration
Long-running business process orchestration for workflows that span multiple services, take minutes to hours to complete, and must recover gracefully when any step fails. Saga pattern selection by use case: choreography saga (each service publishes events and reacts to others without a central coordinator -- appropriate for simple linear workflows) or orchestration saga (a central orchestrator sends commands to services and waits for completion events -- appropriate for complex branching workflows where visibility matters). Compensating transaction design: each step in the saga has a defined compensation action that undoes the step's effect if a later step fails; an order saga that fails at the payment step cancels the inventory reservation it already applied, maintaining system consistency without a distributed transaction. Temporal workflow engine for complex long-running orchestration: Temporal provides durable execution (workflows survive application restarts and infrastructure failures), deterministic replay (the workflow's history can be replayed to diagnose failures), and built-in retry with configurable backoff per activity. Conductor (Netflix) as an alternative for organisations already invested in the Java ecosystem. State machine implementation for approval workflows and processing pipelines where each state is explicit, transitions are defined, and the current state of every in-flight process is queryable. Timeout and escalation handling: a step that doesn't complete within a configurable SLA triggers an escalation event (alert, reassignment, fallback action) rather than silently hanging.
Event-driven integration with third-party systems
Event-driven integration layers connecting external APIs, SaaS platforms, and legacy systems to your internal event infrastructure -- bridging the gap between systems that push webhooks, systems that require polling, and systems that expose database-level change streams. Webhook ingestion service: receives incoming webhooks from Stripe, GitHub, Shopify, or any HTTP-based external source; validates the HMAC signature; publishes to an internal Kafka topic; responds HTTP 200 immediately -- decoupling the external caller from internal processing latency, handling burst webhook delivery (Black Friday Stripe webhooks) by queuing rather than dropping, and providing automatic retry on downstream consumer failure. Change data capture (CDC) with Debezium: connects to your PostgreSQL, MySQL, or MongoDB database and streams every row-level INSERT, UPDATE, and DELETE to a Kafka topic in real time -- enabling downstream services to react to database changes without polling queries or application-level event publishing hooks. Debezium runs as a Kafka Connect connector, managed on Confluent Cloud or self-hosted. Transactional outbox pattern implementation for systems where application-level event publishing must be consistent with the database write: the application writes the event to an outbox table in the same database transaction as the domain state change; a separate relay process reads the outbox table and publishes to Kafka; the published event is acknowledged and deleted from the outbox -- eliminating the dual-write problem where a successful database write followed by a failed event publish produces inconsistent state.
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.
IoT Development -- IoT event pipelines using Kafka and message brokers for sensor data at scale
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.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Event-Driven Architecture Development in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
