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Data & Analytics•June 13, 2024•10 min read

Real-Time Data Streaming: Building Event-Driven Architectures

Real-time streaming enables immediate data processing for analytics, ML features, and operational systems.

#data-streaming#kafka#flink#event-driven

Batch processing introduces latency between data creation and availability. Real-time streaming processes data continuously, enabling immediate insights and actions. Event-driven architectures built on streaming platforms transform how organizations handle data.

Streaming Platforms

Apache Kafka provides the foundation for most streaming architectures. Apache Flink enables complex stream processing with state management. Kafka Streams offers simpler processing within Kafka ecosystem. Cloud services like Kinesis and Pub/Sub reduce operational burden.

  • Kafka provides durable, ordered event storage with replay capability
  • Flink handles complex event processing with sophisticated windowing
  • Kafka Streams suits simpler processing without separate cluster
  • Consider managed services reducing operational complexity
  • Design for exactly-once semantics when data accuracy is critical

Stream Processing Patterns

Windowing aggregates events over time periods for analytics. Joins combine streams for enriched processing. State management maintains context across events. Dead letter queues handle processing failures gracefully. Design patterns addressing these concerns enable robust streaming systems.

Tags

data-streamingkafkaflinkevent-drivenreal-time

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