Delivery and product
Break the chain of synchronous calls
When one slow service can stall a whole request path, the system is only as reliable as its weakest link. We move the right flows to events, with schemas, guarantees and replay built in.
- Duration
- 6 to 16 weeks
- Model
- Design and build
- Output
- Event backbone and migrated flows
Signs the call chain has become the risk
Synchronous calls are simple until the chain gets long. Then every dependency’s bad day becomes yours.
- A timeout in one downstream service turns into errors at checkout, at login or on the main screen.
- Retries multiply the load during an incident, so a small slowdown becomes an outage.
- Adding a new consumer of order or payment data means changing the service that produces it.
- Background jobs poll the database every few seconds to find out what changed.
- Events are already published, but consumers process some of them twice and miss others.
- Nobody can replay what happened yesterday to rebuild a projection or fix a bug in a consumer.
- Kafka is running, but topics, keys and retention were chosen ad hoc and nobody owns the schemas.
Events where they help, calls where they do not
Moving everything to events is as risky as keeping everything synchronous. Some interactions need an immediate answer; others only need to know that something happened. The work starts by sorting the flows: which calls can become events, which must stay requests, and which need a saga to coordinate several steps.
For the flows that move, the rules are made explicit: event schemas and how they evolve, keys and ordering, delivery guarantees, idempotent consumers, and the outbox pattern so that a database change and its event never disagree.
Operability is built in from the first topic. Dead-letter handling, replay, consumer lag monitoring and tracing across asynchronous hops let your team see and fix a flow without guessing where a message went.
Scope
Included
- Flow inventory: which calls become events and which stay synchronous
- Event schemas, versioning and a schema registry
- Topic, key, partition and retention design
- Outbox pattern for reliable publishing
- Idempotent consumers and deduplication rules
- Saga coordination for multi-step processes
- Dead-letter handling and replay procedures
- Tracing and lag monitoring across asynchronous flows
Not included
- Moving every flow to events
- Stream analytics and real-time dashboards
- Running the messaging cluster day to day after handover
- Rewriting services unrelated to the migrated flows
How the work runs
- Weeks 1 to 3Sort the flows. Request paths, failure history and data dependencies are mapped. Each flow is marked as event, request or saga, with the reason written down.
- Weeks 4 to 6Lay the backbone. Topics, schemas, registry, outbox and consumer conventions are set up and proven end to end on one real flow.
- Week 7 onwardMove flows in order. Flows are moved one at a time, running in parallel with the old path until their results match.
- Final weeksOperate and hand over. Replay, dead-letter handling and monitoring are exercised with your team in a practice drill before handover.
What you receive
The backbone runs in your infrastructure, and the conventions live in your repositories.
Who this service is not for
- Systems with a handful of services and short call chains. Events add moving parts that only pay off once coupling hurts.
- Teams looking only for a Kafka installation. The value is in the flow design and the conventions, not in the cluster.
- Projects that want every interaction turned into an event. We keep requests where an immediate answer is needed.
Frequently asked questions
Do we have to use Kafka?
No. Kafka fits high-volume, replayable streams well, but the managed queues and brokers of AWS, Azure and Google Cloud are often enough. The choice follows your volumes, your ordering needs and your team.
How do you prevent duplicate processing?
Consumers are made idempotent: each event carries an identifier, and processing records what has already been handled. Together with the outbox pattern on the producer side, duplicates stop causing wrong results.
What happens to data consistency?
Flows that move to events become eventually consistent, and we design where that is acceptable. Where it is not, the flow stays synchronous or is coordinated by a saga with explicit compensation steps.
How is this different from Distributed Systems Design?
Distributed Systems Design decides the consistency model and proves it with reference code. This service builds the event backbone and moves your production flows onto it.
Can we see where a message is at any time?
Yes. Tracing across asynchronous hops and consumer lag monitoring are part of the handover, so a stuck flow is visible without searching through logs.
Start with a short technical call
Thirty minutes. You describe the call chain that worries you most, and we tell you which parts of it should become events.