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Business events trigger actions in other systems when a producer records a meaningful change, such as an order being placed, and publishes it to a channel or broker. The broker routes that event to one or more consumers, which independently decide what to do: reserve inventory, begin fulfillment, update a read model, or notify a customer. An event reports what has happened; a command asks a recipient to do something. This asynchronous pattern can reduce dependencies between systems, but it also introduces delivery, ordering, and consistency concerns.
What is event processing?
Google Cloud defines an event as “a record of something that has happened.” Salesforce describes one as “a change in state that is meaningful in a business process.” In practical terms, an event is a durable statement of a business fact: an order was accepted, a payment cleared, or a customer changed an address. It is not, by itself, an instruction for every recipient to follow.
The usual flow has three roles. A producer detects or commits a business change and publishes an event to a channel. A router or broker delivers it to the relevant subscribers. Each consumer applies its own rules and performs its own work. Google Cloud’s Eventarc Standard overview describes this producer-router-consumer pattern and its asynchronous flow; Salesforce’s Platform Events Developer Guide explains events, producers, channels, consumers, and event buses.
Event versus command
A command-oriented message commonly says, in effect, “perform this task.” An event says, “this change occurred.” A consumer may respond to an event by issuing a command to another service, so both styles can appear in one architecture. Product terminology is not perfectly uniform; examine what the message means and what the recipient is expected to do rather than relying on a label alone. Google Cloud discusses the distinction in its Pub/Sub overview.
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How an event reaches downstream work
Consider an order that has been accepted. The order service publishes an OrderAccepted event. Separate consumers can reserve inventory, initiate payment, and notify fulfillment. The producer does not need to know each consumer’s internal business rules. A new subscriber can be added to the event channel without embedding another downstream integration in the order service.
A queue can buffer incoming work when a consumer is slower than the producer, allowing the consumer to catch up as capacity becomes available. A publish-subscribe channel can fan out one event to multiple subscribers. Depending on the platform and its configuration, an event stream may also support filtering, retention, replay, or audit use cases. These capabilities are not automatic guarantees: check the selected system’s routing, retention, acknowledgment, and replay behavior. Google Cloud’s Eventarc documentation, Pub/Sub documentation, and AWS’s Lambda event-driven architecture guidance describe these patterns.
When to use event-driven processing
Events are a strong candidate when several independent systems need to react to the same business change, when near-real-time follow-up matters, or when bursts of incoming work should be buffered. They can also help keep a producer from depending on the availability of every downstream consumer. Mobile or device integrations may benefit from buffering work through periods of unreliable connectivity, where the platform supports it.
Asynchronous work is less suitable when a user-facing operation needs an immediate decision from another system, when multiple systems must show one agreed state immediately, or when a small number of simple integrations would be easier to operate synchronously. Consumers can lag, so two systems may temporarily show different states. A hybrid design is common: use a synchronous request-response call for the decision needed now, then publish events for notifications, reporting, or other follow-on work. Microsoft’s Event-Driven Architecture Style and Salesforce Architects’ Platform Decision Guides discuss these trade-offs and selective use of the pattern.
Compare the actual delivery requirements
Do not choose a queue, broker, or stream by name alone. Compare the guarantees and operating model that matter to the business process.
| Decision area | Questions to answer |
|---|---|
| Delivery and acknowledgment | When is a message considered handled? Can it be delivered more than once? What retry behavior applies? |
| Ordering | Is any order guaranteed, and is that guarantee global, per partition, per key, or absent? |
| Retention and replay | How long can events be retained, and how far back can a consumer recover or an operator replay? |
| Routing | Can the system filter events, fan them out to subscribers, and manage subscriptions as consumers change? |
| Load and back pressure | How are bursts buffered? How will teams see throughput, consumer lag, and an overloaded subscriber? |
| Failure handling | What are the retry limits, dead-letter or unprocessed-message path, manual repair process, and replay safeguards? |
| Contracts and governance | Who owns the event schema? How are changes made compatibly, and how are access, sensitive data, and correlation IDs controlled? |
| Operational fit | Does the deployment model fit the team’s skills, monitoring, availability needs, and acceptable provider coupling? |
At-least-once delivery and uncertain ordering can coexist with high scale and availability; they are not evidence that a business effect will happen exactly once or in the intended sequence. A highly decoupled broker topology gives consumers more independence, while a mediator can provide more centralized control over workflow. The right balance depends on the coordination, visibility, and recovery the process requires.
Make publishing reliable with an outbox
A producer can lose the connection between a committed business change and its event if it updates a database and publishes separately. If the database commit succeeds but publication fails, subscribers never learn about the change. If publication succeeds but the database transaction fails, subscribers may act on a change that did not persist.
The transactional outbox pattern addresses this dual-write problem. In the same database transaction, the application writes both the business change and an event record to an outbox table. A separate relay publishes committed outbox records to the broker. Change data capture (CDC) can be an alternative when the database provides a suitable change stream. AWS explains the pattern and its trade-offs in its Transactional outbox pattern guidance.
An outbox improves the link between database commit and event publication; it does not guarantee that a consumer receives an event only once. A relay can publish and then fail before recording that it completed, leading to another delivery. Give events stable identifiers and make consumers safe to run more than once.
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Design consumers for duplicates, ordering, and failure
Make repeated processing safe
With at-least-once delivery, a consumer may receive the same event multiple times. It can record stable event IDs it has already applied, or make the operation itself idempotent so repeating it has no additional effect. For an irreversible external action—such as issuing a refund or sending a one-time notice—define how a retry is recognized before enabling automatic replay. Avoid claiming “exactly once” for a business outcome unless the guarantee covers the complete boundary from publication through the external effect.
Decide which ordering matters
Scaling across processing units may limit ordering guarantees. Identify the event sequences that truly matter and the required scope: global, per customer, per order, per partition, or none. Sequence numbers or key-based partitioning can help where supported, but they add constraints and should be used for a real ordering requirement. Retries and dead-letter recovery can bring older work back after newer events have been processed; consumers may need to detect stale events or reconcile state. AWS’s outbox guidance, Google Cloud’s Pub/Sub overview, and Microsoft’s architecture guidance cover delivery and ordering considerations.
Set a recovery path
Where the platform supports it, keep a message available until it is acknowledged. Use bounded retries, then move messages that continue to fail to a reviewable dead-letter or unprocessed-message path. Operators need a way to inspect the cause, repair data or code, and replay safely. Define who owns that repair and how replay avoids repeating external effects that already succeeded. Include correlation IDs so one business flow can be followed across producers, brokers, and consumers.
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Choose event payloads and evolve schemas
Independent producer and consumer deployments make event contracts a shared boundary. Version schemas and establish compatibility rules so a producer change does not unexpectedly break older consumers. Assign ownership for the contract and establish how consumers learn about changes.
A payload with all attributes a consumer needs can reduce extra lookups, but larger payloads can carry stale copies of data and increase coupling to a particular representation. A payload containing only identifiers keeps the authoritative record elsewhere, but consumers must fetch that data and may see a later state than the one that caused the event. Choose according to latency, consistency, message size, and contract-management needs. Microsoft’s Event-Driven Architecture Style discusses payload and schema trade-offs.
Build the smallest architecture that meets the need
For one producer and one consumer, a direct synchronous call or a simple queue may be easier to understand and operate than a broad event platform. Add routing, fan-out, retention, replay, or stream processing when the actual requirements justify them. Asynchronous decoupling is useful, but it shifts work into operating contracts, monitoring lag, handling duplicates, and repairing failed messages. The architecture should make those responsibilities explicit rather than hide them behind the broker.
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