Event Modeling

Current-state rows explain what is true now, but not how the entity reached that state.

Updating one row in place removes the sequence of business changes that created the current value. Later, teams cannot reliably explain when a contract was created, changed or cancelled.

Event Modeling stores each meaningful business occurrence as an immutable event. The ordered event stream becomes the historical source from which state, timelines and analytical projections can be derived.

This only works when event identity, temporal meaning and ordering are explicit. Otherwise replay can produce different states from the same data.

An ordered contract event stream is replayed into one deterministic historical state timeline.

A trustworthy event stream separates identity, time and order.

Analyze this Event Modeling example

Use these sample event streams to test deterministic replay:

1. Choose one event stream below.
2. Start an investigation with the selected example.
3. Review identity, temporal semantics and event ordering.
4. Check whether replay yields one intended historical timeline.

event_id,aggregate_id,business_key,event_type,sequence
E1,A1,C-1001,Created,1
E2,A1,C-1001,PremiumChanged,2
E3,A1,C-1001,Deleted,3

event_id,aggregate_id,business_key,event_type,sequence
E1,A1,C-1001,Created,1
E2,A1,C-1001,PremiumChanged,2
E2,A1,C-1001,PremiumChanged,3
E3,A1,C-1001,Activated,4

Events describe transitions; projections derive reportable state.

Each event represents a business fact such as contract creation, a premium change or cancellation. It should have a stable identifier and belong to exactly one business entity.

entity_id | event_type         | effective_at
C-1001    | CONTRACT_CREATED   | 2024-01-05
C-1001    | PREMIUM_CHANGED    | 2024-04-10
C-1001    | CONTRACT_CANCELLED | 2024-09-20

Every replay must resolve events in one stable order.

Timestamps alone are often insufficient. Two events may share the same effective and recorded timestamp, particularly after batch ingestion or backfill.

order by
  effective_at,
  recorded_at,
  event_priority,
  event_sequence,
  event_id

The exact ordering contract can differ, but it must be explicit, stable and covered by tests. A physical input order is never a valid historical rule.

Event streams become unreliable when ingestion mechanics leak into business semantics.

Retries can create duplicates. Batch loads can assign identical timestamps. Late-arriving events can be recorded after later business events. Source systems may emit technical messages that are not independent business facts.

A robust model normalizes those conditions without rewriting historical facts. It defines stable event identity, idempotent ingestion, explicit priority and a reproducible projection rule.

Validate the stream before trusting any derived state.

Event Modeling is strongest when the business cares about transitions and auditability.

A deterministic event stream preserves explanation, not merely history.

A state table can answer what was valid on a date. An event stream can additionally explain which business facts produced that state.

When event identity, time and ordering are reliable, teams can replay history, rebuild projections, investigate corrections and prove why a report changed.

How Event Modeling relates to other historical patterns