You added Kafka to decouple your services and now you have a different set of problems. Messages are processed out of order. A consumer crashed and when it came back, it reprocessed three hours of events. Your dead letter queue has 50,000 messages that nobody knows how to deal with. The promise of event-driven architecture was loose coupling and scalability — the reality is debugging invisible data flows and hoping nothing was lost.
Event-driven systems are powerful but they shift complexity from code to infrastructure and data flow. After building event-driven architectures for production systems handling millions of events daily, I have learned where the abstractions leak and how to design around them. I can help you choose the right messaging patterns, configure your brokers correctly, handle the edge cases that tutorials skip, and build observability into your event flows so you can actually see what is happening when things go wrong.
Common Event-Driven Pain Points
An order event was published but the notification service never received it. Or the payment service processed the same payment twice because the consumer committed the offset before completing the work. At-least-once delivery sounds simple until you realize your consumers are not idempotent.
A user updated their profile and then placed an order, but the order service processed the order event before the profile update arrived. Partition keys were not set correctly, consumer group rebalancing shuffled assignments, and now your downstream services see events in the wrong sequence.
Your consumer group's lag keeps increasing and you cannot figure out why. Is it slow processing, too few partitions, a downstream dependency that is throttling, or a poison message that keeps causing retries? The consumer metrics do not tell you enough to diagnose the bottleneck.
Your team is debating whether a new feature should use events or direct API calls. Some interactions naturally fit async patterns while others need synchronous responses. Making the wrong choice leads to unnecessary complexity or awkward request-reply patterns over a message broker.
You added a new field to an event schema and three consumers broke. Or you renamed a field and old events in the topic cannot be deserialized anymore. Without a schema evolution strategy, every schema change is a potential production incident.
Failed messages are routed to the DLQ but nobody looks at them. They accumulate for months until the queue is full and new failures start getting dropped silently. There is no process for analyzing, fixing, and replaying dead-lettered messages.
How We Help
We design your event-driven architecture from the ground up — event schemas, topic design, partitioning strategy, consumer groups, and data flow. Or we fix the existing design that is causing problems by identifying and correcting the patterns that are not working.
Kafka, RabbitMQ, or SQS — each broker has configuration knobs that dramatically affect reliability, throughput, and ordering guarantees. I help you configure your broker correctly for your specific workload instead of using defaults that work for demos but not production.
Event sourcing, CQRS, saga orchestration, outbox pattern, idempotent consumers — I help you implement these patterns correctly for your domain, with proper error handling and edge case coverage that tutorials leave out.
Debugging event-driven systems requires different observability than request-response. I help you build visibility into your event flows — consumer lag dashboards, dead letter monitoring, event tracing, and alerting that catches problems before they cascade.
Real Scenarios
You are building an order pipeline that spans inventory, payment, fulfillment, and notification services. We design the event flow, define the event schemas, handle compensation for failures, and make sure no orders are lost or double-processed.
Your Kafka cluster is running but you are not sure the topic design, partition count, and consumer group configuration are right for your workload. We review and optimize your Kafka setup for your actual throughput, ordering, and scaling requirements.
Your domain has complex state transitions and audit requirements that make event sourcing a natural fit. We design the event store, define aggregate boundaries, build the projection layer, and handle the operational complexity of an event-sourced system.
Your services are chained with synchronous REST calls and one slow service brings everything down. We identify which interactions should become async, design the event contracts, and plan the migration so you can move incrementally without breaking existing functionality.
Who This Is For
You are building event-driven features for the first time and want to get the foundational patterns right — idempotency, ordering, error handling, and schema management — before they become production problems.
Your event-driven system is in production but messages are being lost, processed out of order, or stuck in dead letter queues. You need someone who has seen these problems before to help diagnose and fix them.
You are considering event sourcing, CQRS, or a move from sync to async communication and want to understand the trade-offs, operational implications, and whether these patterns fit your actual needs.
You run the message broker infrastructure and need help with cluster sizing, topic governance, schema registry setup, consumer group management, and operational best practices for production.
Pricing
Single consultation sessions, multi-session packs, and engagement packages available. See all pricing options on our Work Assistance page.
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Describe the problem you are facing with your event-driven system. We will respond within 24 hours.
