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Publishing and Consuming Events

Once events are written to the event store, they shouldn't remain locked away. Publishing makes them available to other systems and teams, while consuming is the act of reacting to those events in operational workflows, analytics pipelines, or AI/ML processes.

Events become far more valuable once they leave the store and reach their consumers. This is what turns a historical record into a living stream of information.

Why Publishing Matters

If events are only stored but never published, their potential remains untapped. By making them accessible, you allow:

  • Operational systems to respond to changes in real time
  • Analytics pipelines to build projections and datasets
  • AI/ML pipelines to update models or trigger predictions immediately

The faster and more reliably events reach their consumers, the greater their value. Timely delivery is especially important for real-time analytics and online machine learning, where delays can reduce accuracy and impact.

Consumption Patterns

Not every consumer works the same way. Some require real-time processing, consuming events the moment they happen to:

  • Update dashboards or live reports
  • Detect anomalies as they emerge
  • Provide instant recommendations

Others prefer batch processing, periodically fetching events to:

  • Update projections
  • Recalculate statistics
  • Prepare training datasets

Many architectures use a hybrid approach – streaming for immediate reactions while maintaining batch jobs for complex aggregations or historical backfills. This combination ensures both responsiveness and completeness.

Example: Real-Time Consumption with EventSourcingDB

Certain use cases demand immediate action. Fraud detection, anomaly monitoring, and dynamic recommendations all depend on reacting to events the moment they are recorded.

EventSourcingDB supports this with its observe endpoint:

  • Clients can subscribe to events as they are written
  • Events are delivered in the exact sequence they occurred
  • Ordering and delivery are guaranteed

This enables scenarios such as feeding new events directly into a real-time AI model, continuously updating a feature store, or triggering alerts when specific event patterns occur.

Reliable Delivery

Publishing is only useful if delivery is dependable. Robust event distribution addresses:

  • Ordering guarantees – events arrive exactly in sequence
  • At-least-once delivery – consumers must handle duplicates
  • Backpressure – slower consumers can process at their own pace without losing events

With the right infrastructure, these challenges become predictable and manageable, ensuring every consumer gets the data it needs.

AI/ML Example: Triggering Model Updates

In the library domain, events such as LoanExtended or LateFeeIncurred might feed a model that predicts the likelihood of overdue returns. With a real-time subscription, you could:

  • Update a member's risk score immediately
  • Send a personalized reminder while the due date is still in the future
  • Continuously enrich your model with fresh examples for incremental learning

This responsiveness means AI-driven systems can act while there is still a chance to change the outcome – not just explain it afterward.

Next up: Ensuring Point-in-Time Correctness – learn how to guarantee that analytics and AI always work with data as it truly was at any point in the past.