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Streaming Data With Apache Kafka

An introduction to Kafka's concepts — topics, partitions, producers and consumers — and when event streaming fits.

Editorial team 1 min read

Apache Kafka is a widely used platform for streaming events between systems.

Core Concepts

  • Event: a record of something that happened — an order placed, a sensor reading.
  • Topic: a named stream of events.
  • Partition: topics are split into partitions for parallelism; order is guaranteed within a partition.
  • Producer: writes events.
  • Consumer: reads events; consumer groups share partitions to scale.
  • Retention: events are stored for a configured period, so consumers can replay them.

Uses

  • Real-time analytics and dashboards.
  • Feeding data warehouses and lakes.
  • Event-driven microservices.
  • Change data capture from databases.
  • Real-time features for machine learning.

Considerations

  • Operational complexity; managed services reduce it.
  • Schema management with registries to prevent breaking changes.
  • Delivery guarantees: at-least-once is common, so consumers should handle duplicates.

When Not to Use It

If data is only needed daily, batch processing is simpler and cheaper.

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