data-modeling

# Data Modeling

Comprehensive guide to data modeling techniques for operational databases, data warehouses, and analytical systems.

## When to Use This Skill

- Designing database schemas
- Choosing between modeling approaches
- Building data warehouses
- Planning schema evolution
- Understanding trade-offs in data models
- Designing for analytics vs operations

## Data Modeling Fundamentals

### Types of Data Models

```text
Data Model Categories:

1. Conceptual Model
   Purpose: Business understanding
   Audience: Business stakeholders
   Content: Entities, relationships, business rules
   Detail: High-level, no implementation details

2. Logical Model
   Purpose: Structure definition
   Audience: Data architects
   Content: Tables, columns, keys, relationships
   Detail: Database-agnostic design

3. Physical Model
   Purpose: Implementation
   Audience: Database engineers
   Content: Indexes, partitions, storage
   Detail: Database-specific optimization

Model Evolution:
┌─────────────────────────────────────────────────────────────┐
│                                                              │
│  Business         Conceptual        Logical       Physical   │
│  Requirements ──► Model        ──► Model     ──► Model      │
│                                                              │
│  "Customers       Customer ─────►  customers    customers    │
│   make orders"    Order           ├── id        ├── id PK   │
│                   └──< makes      ├── name      ├── name    │
│                                   └── email     ├── email   │
│                                                 └── idx_*   │
│                                                              │
└─────────────────────────────────────────────────────────────┘
```

### Operational vs Analytical

```text
Operational (OLTP) vs Analytical (OLAP):

┌────────────────────────────────────────────────────────────┐
│                      OLTP                                   │
│            (Online Tr