Event Streaming in 2026: Kafka, Event Hubs, and What Actually Matters

At Truliacare, I helped build a distributed Kafka + Spring Boot system that processed over a billion events a year, then led the migration of that messaging backbone to Azure Event Hubs. That experience taught me that the choice between them matters far less than most people think — and that the things that do matter barely get discussed.

The tooling debate is mostly settled

Kafka and Event Hubs share the same core model: partitioned, ordered, replayable logs with consumer groups. If you can reason about offsets and partitions in one, you can operate the other. The migration was far less dramatic than the architecture diagrams suggest — the real work was in the operational details, not the concepts.

• Kafka gives you total control and a rich ecosystem, at the cost of running brokers, ZooKeeper/KRaft, and all the operations that come with them.
• Event Hubs gives you the same log model as a managed PaaS, trading control for far less operational burden.

If your team is small and your differentiator isn't "we run brokers better than a cloud provider," managed is often the right call.

What actually matters

1. Your event schema is a contract

The biggest source of pain in an event-driven system isn't the broker — it's schema evolution. When twelve microservices consume the same event, adding a field is a negotiation. We standardised on a shared event codec module precisely because duplication across services was where drift and bugs crept in.

Rule: version your events, and treat the schema as a public API.

2. Idempotent consumers are non-negotiable

Streaming systems deliver at least once. That means your consumers will see duplicates. If your consumer isn't idempotent, your system is wrong, regardless of which broker you chose.

if (processedStore.contains(event.getId())) {
    return; // already handled — safe to skip
}
process(event);
processedStore.add(event.getId());

3. Backpressure beats brute force

High-volume streams will overwhelm a weak consumer. We added a WebSocket filtering layer that dropped irrelevant events before they reached the UI, cutting downstream load by ~30%. The lesson: filter as early as possible, and let consumers signal when they're saturated.

4. Observability is the whole game

You cannot debug a distributed stream by reading code. You need consumer lag metrics, per-partition offsets, dead-letter queues, and traces that follow an event across service boundaries. We invested heavily here, and it paid for itself every single incident.

A decision checklist for 2026

The mindset

The broker is a commodity. What separates a resilient event-driven system from a fragile one is schema discipline, idempotency, backpressure, and observability — none of which come free with any tool.

Choose the broker you can operate. Then spend your energy on the four things that actually decide whether the system holds under load.

A billion events a year taught me that the technology choice is the easy part. The engineering is everything after.