Traditional message queues (RabbitMQ, ActiveMQ) are designed for point-to-point messaging message is consumed and gone. Kafka is a distributed log  messages persist, multiple consumers can read independently, you can replay history. Kafka excels at high-throughput event streaming; traditional queues are better for task distribution. Most modern data architectures use Kafka for the event backbone.

Managed services reduce operational burden no cluster management, automatic scaling, built-in monitoring. Self-hosted gives more control and can be cheaper at scale. For most organisations, we recommend managed services unless you have specific security or customisation requirements. The engineering time saved is worth the premium.

Exactly-once semantics require careful design: idempotent producers, transactional consumers, and application-level deduplication. Kafka supports exactly-once within transactions, but end-to-end exactly-once requires your application logic to handle it. We design for at-least-once with idempotent processing  more reliable in practice than trying to guarantee exactly-once.

Absolutely. We never recommend big-bang migrations. Typical approach: start with one high-value use case (e.g., one partner feed), prove it works, expand. You can run batch and streaming in parallel during transition. Most batch processes can be replaced incrementally over 3-6 months.

We set up comprehensive monitoring from day one  Prometheus/Grafana or Datadog for metrics, alerting for lag and failures, runbooks for common issues. If you want ongoing operational support, we can provide that. If you want to bring operations in-house, we document everything and train your team.

7–10 business days from signed agreement to engineer embedded in your team.