When two systems need to talk, the fastest solution is a direct API call. System A calls System B, gets a response, moves on. It works — until you have 15 systems and 210 possible connections.
The point-to-point trap
Every new integration adds complexity exponentially, not linearly. With 5 systems you have 10 connections. With 10 systems, 45. With 20, 190. Each connection is a contract two teams have to maintain, version, monitor and debug independently.
We see this pattern in every systems integration engagement. The company started with two or three tools, connected them with direct API calls, and now has a web of fragile dependencies where changing one system breaks three others.
Events change the model
In an event-driven architecture, systems publish facts — "order placed", "payment received", "item shipped" — and other systems subscribe to the events they care about. The publisher does not know or care who is listening.
This means: - Adding a new consumer costs zero changes to the publisher. When marketing wants to trigger an email on every new order, they subscribe to the event. The order system never knows. - Systems can fail independently. If the email service goes down, the order still processes. The email event sits in a queue until the service recovers. - You get a full audit trail for free. Every event is a fact with a timestamp, which means debugging becomes reading a log, not reconstructing state across five databases.
When to use it
Event-driven architecture is not always the right choice. For simple request-response patterns — "give me the current price of this product" — a direct API call is simpler and faster.
Use events when: - Multiple systems need to react to the same thing - You need resilience against downstream failures - You want to add new consumers without changing existing systems - Audit trails and replay capability matter
The tech stack we recommend
For most teams, Apache Kafka or RabbitMQ covers the messaging layer. Kafka is better for high-throughput, ordered event streams. RabbitMQ is simpler for task queues and routing patterns.
On top of that, a schema registry (Confluent or custom) ensures producers and consumers agree on event shapes, and a dead-letter queue catches anything that fails processing after retries.
The investment is real — event-driven systems take longer to design up front. But the maintenance cost at year two and beyond is a fraction of the point-to-point alternative.