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Event-driven architecture (EDA) lets software components communicate by publishing facts about changes and reacting to them asynchronously. It can decouple services and send one change to several independent consumers, but it also introduces delivery, ordering, recovery, observability, and eventual-consistency work. Use it when those trade-offs fit the problem—not as an automatic replacement for request-response.
How does event-driven architecture work?
A producer publishes an event when something changes. A broker or router may accept the event, filter it, buffer it, and forward it to subscribed consumers. Each consumer handles its work independently and may publish another event. The producer does not need to call every consumer directly, so components can be deployed independently and can use different technology stacks.
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An event describes something that has happened, such as a resource being updated. It is not the same as a command, which asks a recipient to do something. An event may carry relevant state, or it may carry an identifier that lets a consumer retrieve the state it needs. That choice affects how much consumers depend on the event’s contents and on the source system.
Because several systems may rely on the same event, its contract is an integration surface. Define who owns it and how changes remain compatible with existing consumers; otherwise a producer’s change can break subscribers it does not know about.
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When is EDA a good fit?
Use it when independent reactions matter
EDA is useful when one change should trigger work in several independent subsystems, when traffic varies, or when parallel processing and cross-system integration are important. For example, a resource-change event could be consumed by separate alerting and coordination systems. New consumers can be added without changing the producer to call each one directly.
Keep request-response for direct interactions
A straightforward request-response interaction is often simpler when a caller needs an immediate answer and the existing design meets its latency and throughput needs. EDA is also a poor fit when a business transaction requires all participating services to agree immediately, or when the team cannot support asynchronous debugging, monitoring, and recovery.
Ask whether decoupling or fan-out solves a real problem. If not, the extra broker, event contracts, failure paths, and operational responsibilities may add cost without a corresponding benefit.
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Consumers may see stale state
Consumers process events asynchronously, so different systems can reflect a change at different times. A read model or downstream view may lag behind the system that accepted the original change. Make pending or stale states clear to users, and do not assume an asynchronous projection will support immediate read-after-write behavior.
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Delivery, duplicates, and ordering need explicit rules
Choose a delivery requirement for each event class. Where losing an event is unacceptable, use a durable source and, where supported, retain in-transit events until the next component acknowledges receipt. Acknowledgment and durability can reduce loss, but retries may still result in duplicate delivery. Make handlers idempotent: processing the same event again should not repeat an irreversible business effect. Define a deduplication key or another business-level safeguard.
Decide where ordering matters—often within one entity or aggregate rather than across the entire system—and partition work accordingly. Parallel consumer instances can process events out of order. Do not assume a platform’s ordering guarantee is global or broader than the scope it actually supports.
Failures need a recovery path
Set a retry policy and decide what happens when an event repeatedly fails. Poison messages should have a deliberate error path, such as a dead-letter or quarantine workflow, with an owner responsible for investigation and recovery. Without that plan, retries can repeat harmful work or leave messages unprocessed without a clear signal.
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Asynchronous flows require end-to-end visibility
A normal request call stack cannot show a complete operation that crosses producers, brokers, and consumers. Include correlation IDs and consistent structured logs, metrics, and traces in the design from the start. Establish who owns broker operations and shared standards; distributed component ownership can coexist with centralized observability and common non-functional requirements.
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How do the main infrastructure choices differ?
These categories solve related but distinct problems. Compare them by durability and replay, ordering scope, throughput and latency, routing and filtering, transaction and dead-letter support, consumer independence, operational ownership, and the cost of running the broker and its observability stack. Choose guarantees to match the business requirement rather than assuming one service provides every guarantee.
| Category | Useful when | What distinguishes it |
|---|---|---|
| Queue | A sender needs to hand work to a consumer. | Commonly links one sender with a consumer; it is not by itself the same distribution model as pub/sub. |
| Pub/sub notifications | One event should notify several independent consumers. | Subscriptions provide a fan-out model so consumers can react independently. |
| Transactional messaging | Work needs messaging features such as transactions, ordering, sessions, or dead-letter queues. | These capabilities are platform-specific. Microsoft documents them in Azure Service Bus examples; that is an example, not a universal recommendation. |
| Event notifications | Changes should be delivered as push notifications, such as changes to cloud resources. | Azure Event Grid is documented for push-delivered notifications, particularly for Azure resource changes. |
| Event streaming | High-throughput telemetry or log aggregation needs a stream that independent consumers can read. | Azure Event Hubs is documented for these workloads and uses consumer groups. Its log-based streaming model differs from conventional pub/sub messaging. |
The Azure services in the table illustrate documented categories and capabilities; they are not a neutral comparative benchmark or a recommendation for every architecture. Match the service’s actual guarantees and operating model to the event’s requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do CQRS and event sourcing fit?
CQRS separates read and write responsibilities
Command Query Responsibility Segregation (CQRS) uses separate responsibilities for changing data and answering queries. It is useful when read and write needs justify the additional design and operational complexity; it is not required for EDA. The read and write models can share a store or use separate stores.
With separate stores, events can update read projections, but a database and broker normally do not participate in one distributed transaction. An outbox addresses this gap by persisting the business change and the event atomically in the source database; consumers should still be idempotent. Separate projections can lag, so plan how to synchronize and rebuild them.
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Event sourcing makes the event history the source of truth
Event sourcing stores state changes as a sequence of events, from which the current state or read projections can be reconstructed by replay. It preserves the history of changes, but adds responsibility for event evolution, replay, projection rebuilding, and lag. An event-driven system does not have to be event-sourced: EDA describes a communication style, while event sourcing describes how state is stored.
How should a multi-service workflow be coordinated?
Choreography
In choreography, services react to events independently and publish events that other services may consume. This supports decoupled reactions, but the end-to-end flow can be harder to follow when responsibility is spread across many event handlers.
Orchestrated saga
An orchestrated saga coordinates the workflow’s steps and compensating actions. It can make control and workflow visibility more explicit, while adding a coordinator and its failure handling. Choose between choreography and orchestration based on how much central control, visibility, and failure coordination the business process needs.
Quick Recap
What should an EDA implementation plan cover?
- Classify event requirements. For each event type, record whether loss is acceptable, where ordering matters, which consumers need it, and what state the event carries or identifies.
- Select infrastructure by semantics. Compare durability and replay, ordering scope, throughput and latency, filtering, transactions, dead-letter support, consumer independence, operational ownership, and observability cost against those requirements.
- Define the event contract. Establish ownership and compatibility rules so producers can evolve events without unexpectedly breaking existing consumers. Include correlation IDs so related work can be followed across asynchronous boundaries.
- Design safe processing. Expect retries and duplicate deliveries where applicable. Make handlers idempotent, specify ordering and partitioning needs, and decide how failed or poison events are retried, quarantined, investigated, and recovered.
- Make asynchronous state visible. Set expectations for projection lag and expose pending or stale states where they affect users. Plan how projections will be synchronized or rebuilt if they fall behind.
- Assign operational ownership. Name the teams responsible for broker operations, consumer health, shared standards, and end-to-end logs, metrics, and traces before the system depends on the event flow.
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