Catalog
concept#Architecture#Integration#Observability#Reliability

Event Driven Systems

An architectural paradigm where components interact via asynchronous events to enable loose coupling, scalability, and flexible integration.

Event-driven systems are an architectural paradigm where components communicate by emitting and reacting to asynchronous events.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Apache Kafka, RabbitMQ, PulsarCloudEvents / schema registriesStream processing platforms (Flink, Kafka Streams)

Principles & goals

Decouple producers and consumersClear event schemas and versioningIdempotency and resilient processing
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Hidden synchronous dependencies lead to failure scenarios
  • Data inconsistencies from incorrect consistency model
  • Uncontrolled event proliferation without governance
  • Clear, versioned schemas and compatibility rules
  • Idempotent consumers and deduplicating processing
  • Integrate observability from the start

I/O & resources

  • Event schemas (JSON/Avro/Protobuf)
  • Message broker or event backbone
  • Producer clients and consumer libraries
  • Asynchronous event streams
  • Materialized views and caches
  • Audit and replayable event logs

Description

Event-driven systems are an architectural paradigm where components communicate by emitting and reacting to asynchronous events. They enable loose coupling, scalable processing, and flexible integration across bounded contexts. Typical uses include microservices messaging, integration platforms, and event pipelines. Design decisions must balance consistency, latency, error handling, and observability.

✔

  • Improved scalability via asynchronous processing
  • Reduced coupling between components
  • More flexible integration of heterogeneous systems

✖

  • More complex error handling and recovery
  • Challenges for consistent read operations across boundaries
  • Higher operational overhead (monitoring, schema management)

  • Throughput (events/s)

    Number of processed events per second; measures capacity and scaling.

  • End-to-end latency

    Time between event emission and complete processing.

  • Processing error rate

    Proportion of failed event processings per time unit.

Stream-based user analytics

Real-time analysis of user events for personalization and monitoring.

Event sourcing for financial transactions

Using historical events as the source of truth to reconstruct state.

Decoupled integration platform

Central event-bus architecture connecting heterogeneous systems.

1

Create event models: define events, aggregates, boundaries

2

Choose infrastructure: broker, schema registry, processing engine

3

Implement producers/consumers, ensure idempotency and error handling

4

Introduce observability: tracing, metrics, alerts

5

Establish governance: schema versioning, event ownership

⚠️ Technical debt & bottlenecks

  • Unversioned event schemas
  • Tight coupling via structured payloads instead of contracts
  • Missing replay and backpressure strategies
Event throughputOrdering guaranteesState management
  • Using events as a direct replacement for API calls without decoupling
  • Unversioned payloads that force breaking changes
  • Publishing sensitive data unfiltered in events
  • Missing idempotency leads to duplicate processing
  • Hidden synchronous paths break decoupling
  • Insufficient observability hampers troubleshooting
Understanding of distributed systems and CAP conceptsExperience with messaging and streaming technologiesSkills in observability and incident response
Domain decouplingScalability and elasticityFault isolation and resilience
  • • Network latency and partitioning
  • • Limited broker capacities
  • • Regulatory requirements for data retention