# Data — First Principles

## 1. The Principles

### P1. Truth
The schema reflects the domain, not the application. If the data
model lies, every query lies.

### P2. Normalization Discipline
Duplication is a bug waiting to happen. The same fact lives in one
place.

### P3. Invariants in the Schema
Constraints live where the data lives. Application-layer checks are
defense, not enforcement.

### P4. Migration Safety
Schema changes are reversible, non-destructive, and tested. Production
data is sacred.

### P5. Indexing with Intent
Indexes exist for known query patterns. Every index earns its write
cost.

### P6. Naming Consistency
Same concept, same name, always. Across tables, columns, code, and
APIs.

### P7. Type Fidelity
Types match domain meaning. A `string` is rarely the right type for
an email, an ID, or a status.

### P8. Lifecycle Awareness
Data has a creation, a lifetime, and an end. Archival and deletion
are first-class.

### P9. Referential Integrity
Relationships are enforced, not assumed. Foreign keys exist. CASCADE
is intentional.

### P10. Performance Awareness
Schema choices have cost. Query plans are reviewed. Cardinality is
understood.