Apache Kafka Fundamentals

Apache Kafka is a distributed event-streaming platform that stores and delivers high-throughput streams of records. Producers write events to topics partitioned across a cluster, and consumers read them independently, enabling decoupled real-time data pipelines and event-driven architectures.

itDistributed systems, messaging, and integration

Apache Kafka Fundamentals

Apache Kafka moves event records between systems and retains them for later reading. It combines messaging with a distributed, replayable log.

That combination changes how you design data flows. A consumer does not remove a record when it reads it. Kafka keeps the record according to the topic's retention policy. Consumers track their own position and can reread retained records.

The problem Kafka solves

Applications often need the same event for different purposes. An order event might feed fulfillment, fraud detection, analytics, and customer notifications.

Direct point-to-point integrations couple every producer to every consumer. Each new destination adds another connection, failure mode, and deployment dependency.

Kafka puts a durable event stream between those systems. Producers write records without knowing every consumer. Consumers read records at their own pace and keep independent positions.

This model supports four common purposes:

Publish and subscribe to event streams.
Store event streams for a configured period.
Process event streams as they arrive or after replay.
Connect databases and services through reusable source and sink connectors.

The core path

Keep this path in mind:

Producer → topic → partition → broker → consumer group

Apache Kafka Fundamentals

The problem Kafka solves

The core path

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