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Apache Kafka

A distributed event streaming platform used as the backbone for real-time data movement and event-driven architecture.

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How data reaches a decision

Sources, ingest, store, model, serve — over definitions, lineage, access and quality that apply at every step.

What it is

Understanding Apache Kafka

Kafka is a distributed log. Producers append events to topics; consumers read them independently at their own pace, and events are retained for a configured period rather than deleted on consumption. That retention model is what distinguishes it from a traditional message queue and what makes replay possible.

Why it matters

Decoupling producers from consumers means new systems can subscribe to existing event streams without changing the producer. In estates where every integration is currently point-to-point, this is the difference between integration cost growing quadratically and growing linearly.

Problems it addresses

  • “Every new integration requires changing the source system.”
  • “Our data is only available in overnight batches.”
  • “Point-to-point integrations have become unmaintainable.”
  • “We cannot replay data after a downstream failure.”

Capabilities

  • Event streaming architecture

    Topic design, partitioning strategy, retention and schema governance.

  • Stream processing

    Transformation, aggregation and enrichment in flight.

  • Change data capture

    Streaming database changes into the event backbone.

  • Operations

    Monitoring, capacity planning, and consumer lag management.

Use cases

  • Integration backbone

    Replace point-to-point integration with a shared event layer.

  • Real-time analytics

    Feed operational dashboards and detection with current data.

  • Change data capture

    Propagate database changes to downstream systems without batch extracts.

Before you commit

Prerequisites, governance and risks

The questions that determine whether this will work in your environment, rather than in a demonstration.

What has to be true first

  • Schema registry and evolution policy from the start
  • Operational capability — Kafka is a distributed system that requires real operational maturity
  • Topic ownership and naming standards

Governance

Schema governance is the discipline that determines whether an event backbone stays usable. Without a registry and a compatibility policy, producers make breaking changes and consumers fail unpredictably. Topic ownership must also be explicit.

Risks

  • Operational complexity — self-managed Kafka needs genuine expertise
  • Schema drift without a registry and compatibility rules
  • Treating it as a message queue and losing the benefit of retention and replay

Where Apache Kafka leads

Every connection below is recorded in the content itself, and each one says why it is here.

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