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Event-Driven Architecture

Systems communicate through events, enabling loose coupling, async workflows, and reactive behavior.

Architecture Diagram

Event-Driven ArchitectureEvent-driven systems showing messaging patterns (pub/sub) and streaming patterns (event logs) with producers, brokers, and consumers.Event-Driven Architecture📨 Message-Based Events🌊 Stream-Based EventsProducer AOrder ServiceProducer BUser ServiceProducer CPayment ServiceMessage BrokerTopics & QueuesAt-least-onceConsumer 1Email ServiceConsumer 2AnalyticsConsumer 3Audit Service✓ Decoupled producers/consumers✓ Load balancing & competing consumers✓ Guaranteed delivery with retries & DLQProducer AClick EventsProducer BIoT SensorsProducer CCDC EventsEvent StreamPartitioned LogRetention & ReplayConsumer AReal-time MLConsumer BData LakeConsumer CDashboard✓ High throughput & low latency✓ Event replay & time-travel queries✓ Partitioning & parallel processingKey Benefits: Loose coupling • Scalability • Resilience • Real-time processing

Enabled By Platform Building Blocks

  • Messaging & Streaming Platform: Provides secure, observable messaging and streaming infrastructure to enable event-driven patterns, decouple services, support async workflows, and ensure enterprise-grade reliability.
  • Observability & Operations: Provides visibility, SLOs, and resilience validation across architectures.

What it is

Producers emit events describing facts; consumers react asynchronously. Brokers or streaming platforms handle delivery and retention.

Patterns

  • Pub/sub via topics
  • Event sourcing + CQRS
  • Choreography vs. orchestration
  • CDC for legacy integration

Distinctions (pub/sub vs. event sourcing vs. CQRS + events)

  • Pub/Sub: fire-and-forget notifications or data distribution; consumers act independently; minimal state in the broker
  • Event Sourcing: events are the source of truth for state; rebuild state by replaying the event log
  • CQRS + Events: separate write and read models; events propagate changes to build optimized read projections

Trade-offs

  • Complexity in reasoning about flow
  • Event contract governance is critical
  • Eventual consistency impacts UX and design

When to use

  • High throughput, decoupled workloads
  • Real-time analytics and streaming ETL
  • Integrating heterogeneous systems asynchronously