patterns/data/
Data
Patterns
Architecture for systems where data outlives the applications that created it — the patterns for ownership, evolution, consistency, and lineage that keep data debuggable, evolvable, and trusted across years and stores.
2 topics in this hub
patterns/data/cqrs/
CQRS — Command Query Responsibility Segregation
CQRS separates the model used to update data (commands) from the model used to read data (queries). The write side is optimised for consistency and business rule enforcement. The read side is optimised for the query patterns the application actually performs. They are different models, potentially different databases, and they evolve independently.
→
patterns/data/event-sourcing/
Event Sourcing
Event sourcing stores the state of a system as an ordered sequence of immutable events rather than as the current snapshot. The current state is derived by replaying the events in order. Every change to application state is captured as an event that is appended to the event log. The log is the source of truth — not the current state table.
→