Most messaging migrations don’t fail in a single dramatic moment. They fail across months of scope creep, unexpected dependencies, and production behaviour that didn’t show up in testing. By the time the problem is visible, significant time and confidence has already been lost.
YOU MAY BE CONSIDERING MIGRATION FOR ANY OF THESE REASONS:
Your current messaging technology can’t support where your product is going
You’re consolidating a hybrid estate that’s grown through acquisition or organic complexity
You’re moving from a legacy or self-hosted setup to a managed solution
You are evaluating a new technology and you need to understand the real cost of switching
โThe teams that struggle most with migrations are the ones who underestimated how much the existing system had drifted from its original design, and how many other systems depended on behaviour that was never formally documented.โ Gabor | Engineering Lead
What We Do
Migration planning and execution that accounts for production reality.
We approach every migration as a production problem, not a greenfield project. That means starting with a thorough understanding of the existing system, its actual behaviour under load, its undocumented dependencies, and the failure modes that have been quietly managed around, before planning a single step of the migration.
What this looks like in practice:
MIGRATION ASSESSMENT
Understanding what you’re moving from, what you’re moving to, and what the real risks are
DEPENDENCY MAPPING
Identifying every system, team, and process that touches your current messaging layer
MIGRATION SEQUENCING
Planning the order of changes to minimise risk and maintain production stability throughout
PARALLEL RUNNING
Managing the period where both systems need to operate simultaneously
CUTOVER PLANNING
Designing the final transition to minimise downtime and provide clear rollback options
POST MIGRATION VALIDATION
Confirming the new system is performing as expected under real production load
Why Seventh State?
We’ve seen what breaks during migrations
We’ve seen the dependencies nobody documented, the consumer behaviour that only appears under production load, and the configuration differences between environments that cause failures at the worst moment. That pattern recognition is what you’re buying.
We stay in the room
We don’t hand over a migration plan and leave. We’re present through planning, execution, and post-migration validation, and we’re accountable for the outcome.
Honest about complexity
We’ll tell you which migrations are straightforward and which are genuinely complex. If a migration is higher risk than it appears, you’ll know before you start.
โThe most important thing we do before any migration is map what the system is actually doing , not what it was designed to do. Those two things are rarely the same.“ Lajos | Senior Engineering Consultant
Let’s talk…
Whether you have a migration planned or you’re still working out if it’s the right call, we can help you make that decision with full information.
How long does a messaging migration typically take?
It depends significantly on the complexity of the existing system, the number of downstream dependencies, and the risk tolerance of the organisation. Simple migrations between similar technologies in well-documented environments can take weeks. Complex migrations involving undocumented dependencies, multiple downstream consumers, or strict uptime requirements typically take months. The planning phase is rarely the bottleneck, it’s the dependency mapping and parallel running that take the most time.
What’s the biggest risk in a messaging migration?
Undocumented consumer behaviour. Most messaging systems accumulate consumers that depend on specific message ordering, timing, or format behaviour that was never formally specified. These dependencies are invisible until production load reveals them which is why thorough dependency mapping before migration is more valuable than any technical migration plan.
Should we migrate to Kafka or stay on RabbitMQ?
This depends on your workload characteristics, team expertise, and the direction your product is going. RabbitMQ and Kafka solve different problems. RabbitMQ excels at complex routing, task queues, and request-reply patterns. Kafka is better suited to high-throughput event streaming and log-based architectures. The right answer depends on what you’re actually building, not on which technology is more fashionable.
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