Catalog
concept#Data#Platform#Architecture#Integration

Event Streaming

Architectural paradigm for continuous transmission and processing of events as data streams.

Event streaming describes continuous production, delivery and processing of events as ordered data streams.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Apache KafkaCloudEvents-compatible gatewaysStream processing engines (Flink, Kafka Streams)

Principles & goals

Events as primary integration interfaceLoose coupling via asynchronous communicationDurable, immutable logs for traceability
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data loss with incorrect configuration
  • Eventual consistency leads to more complex failure modes
  • Lack of governance can lead to sprawl
  • Manage event schemas centrally (schema registry)
  • Implement idempotent consumers
  • Define retention/archiving strategies

I/O & resources

  • Source systems producing events (APIs, DB-CDC)
  • Event schema and versioning rules
  • Infrastructure for broker and storage
  • Consumer streams for applications and analytics
  • Durable event logs for audits
  • Monitoring metrics and SLAs

Description

Event streaming describes continuous production, delivery and processing of events as ordered data streams. It enables scalable, loosely coupled architectures for real-time analytics, integrations and asynchronous workflows. Typical platforms include Apache Kafka and CloudEvents-compliant infrastructures that reduce latency and simplify data flows between services.

✔

  • Scalability at high throughput
  • Lower latency for real-time use cases
  • Improved decoupling of services

✖

  • Operational and debugging complexity
  • Requires suitable monitoring and observability
  • Schema and versioning management required

  • End-to-end latency

    Time from event production to processing.

  • Throughput (events/s)

    Number of events processed per second.

  • Error rate / lost events

    Share of undelivered or lost messages.

Apache Kafka as backbone

Using Kafka for high throughput and durable logs in distributed systems.

CloudEvents for interoperability

Standardized event format for cross-platform integration and routing.

Stream processing with Apache Flink

Stateful stream processing for complex aggregations and windowing.

1

Define requirements and SLAs for latency and throughput

2

Select appropriate broker and storage architecture

3

Introduce event schemas and governance rules

4

Plan consumer topologies and repartitioning strategy

5

Test observability, backpressure and fault tolerance

⚠️ Technical debt & bottlenecks

  • Ad-hoc topics without governance
  • Outdated schemas without migration path
  • Missing automated tests for consumers
Network bandwidthPartition hotspotsStorage I/O
  • Using overly large payloads as standard
  • Short retention without archiving for compliance
  • Lack of source-consumer isolation leading to side effects
  • Underestimating observability requirements
  • Unaccounted data schema changes
  • Insufficient planning of partitioning strategies
Distributed systems understandingOperating and sizing messaging platformsSchema design and versioning
Real-time processingScalability and throughputData durability and traceability
  • • Limited storage for retention
  • • Network latency across regions
  • • Compliance requirements for event data