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Writing and Storing Events

Once you have identified your domain events, the next step is to persist them so their accuracy, integrity, and long-term usefulness are guaranteed – both for running your system and for powering analytics, statistics, and AI models.

A specialized event store such as EventSourcingDB is designed for exactly this purpose: storing immutable events in the correct order, along with the metadata needed for operational use and advanced analytics.

Why Storage Matters for AI

AI and machine learning depend on reliable, reproducible datasets. If events are:

  • Lost
  • Altered
  • Recorded inconsistently

…then any projections, features, or model outputs built from them will be unreliable.

An event store that enforces strict ordering and immutability preserves every fact exactly as it happened. This ensures you can always:

  • Rebuild any dataset for analysis or retraining
  • Work with a history that is identical today and tomorrow

Principles for Storing Events

Events are historical facts. Once stored, they:

  • Must never be changed or deleted
  • Record corrections as new events
  • Are stored in the exact sequence in which they occurred

Ordering is critical for:

  • Accurate history replays
  • Building reliable time-series datasets

Every event should:

  • Be self-contained – understandable on its own
  • Include all details needed without relying on mutable state
  • Carry metadata such as timestamps, unique IDs, and references to related events

Such metadata is invaluable for:

  • Feature engineering
  • Explainable AI

The Event Store as the Single Source of Truth

Your event store is the authoritative history of your domain.

With EventSourcingDB:

  • All events are stored immutably, in order
  • The same history can serve operational systems, reporting tools, and AI pipelines
  • History can be replayed to produce exactly the same datasets as before

This separation between immutable storage and downstream processing ensures analytics and ML always run on a complete and consistent history.

AI/ML Example: Rebuilding Datasets

Imagine you trained a model last year to predict overdue loans. Now you want to improve it. Because your events are immutable and complete, you can:

  1. Replay the exact same training dataset to reproduce old results
  2. Generate new feature sets from the same history
  3. Train updated models without losing the ability to explain past predictions

Next up: Publishing and Consuming Events – see how to distribute events to analytical pipelines and real-time AI consumers.