data-modeling

name	data-modeling
description	Designs and validates normalized or dimensional data models. Assesses schema design for analytics readiness, identifies grain, cardinality, and fact vs. dimension patterns. Outputs entity-relationship diagram and modeling documentation.
allowed-tools	Read, Write, WebFetch
effort	high

When to activate

When designing a new analytics data warehouse, evaluating existing schema for analytics use cases, or refactoring data models to support new metrics. Run before building dbt models or star schema implementations.

When NOT to use

Not for SQL optimization — use sql-optimizer skill instead. Not for business logic implementation without understanding the underlying data structure first. Not without clear requirements about analytical grain and fact tables.

Modeling Checklist

1. Define analytical grain — Identify the atomic level of measurement (transaction, event, daily user, etc.)
2. Identify fact tables — Tables that measure business events with foreign keys to dimensions
3. Identify dimension tables — Tables that describe entities (customers, products, dates, etc.)
4. Check cardinality — Understand primary key relationships and fan-out patterns
5. Verify normalization — Assess if 3NF, star schema, or denormalized design is appropriate
6. Flag snowflaking — Detect unnecessary dimension hierarchy levels that could be flattened
7. Map conformed dimensions — Identify dimensions reused across fact tables to ensure consistency
8. Document lineage — Trace data from source systems through transformations to final tables

Modeling Patterns

Star schema — Central fact table surrounded by denormalized dimension tables. Fast for analytical queries; simple joins; easiest for BI tools.

Snowflake schema — Fact table with normalized dimensions (dimensions broken into sub-dimensions). Saves storage; harder to query; more joins required.

3rd Normal Form (3NF) — Fully normalized relational schema. Optimized for transactional consistency; not optimized for analytics (requires many joins).

Data vault — Hubs (core entities), links (relationships), satellites (attributes over time). Scalable; supports slowly changing dimensions; complex queries.

Kimball methodology — Dimensional modeling focused on business processes; conformed dimensions; iterative design.

Design Template

# Data Model Design

**Project:** [Project name]  
**Purpose:** [Analytics use case — e.g., revenue attribution, churn prediction]  
**Grain:** [Atomic level of fact — e.g., transaction, daily_user, event]  
**Design Pattern:** [Star / Snowflake / 3NF / Data Vault]  
**Version:** 1.0  
**Last Updated:** [date]

---

## Business Requirements

- [Key metric 1]
- [Key metric 2]
- [Key metric 3]

---

## Fact Tables

### fact_transactions

| Column | Type | Description | Grain |
|--------|------|-------------|-------|
| transaction_id | INT | Primary key | transaction |
| customer_id | INT | Foreign key to dim_customers | |
| product_id | INT | Foreign key to dim_products | |
| order_date_id | INT | Foreign key to dim_date | |
| transaction_amount | DECIMAL(10,2) | Revenue in USD | |
| quantity | INT | Units sold | |

**Primary Key:** transaction_id  
**Grain:** One row per transaction  
**Volume:** ~100M rows; grows ~10M/month  

---

## Dimension Tables

### dim_customers

| Column | Type | Description | SCD Type |
|--------|------|-------------|----------|
| customer_id | INT | Primary key | 0 |
| customer_name | VARCHAR | | 1 |
| email | VARCHAR | | 1 |
| country | VARCHAR | | 1 |
| segment | VARCHAR | Premium / Standard / Trial | 1 |
| created_date | DATE | | 0 |

**Cardinality:** ~50K unique customers  
**Update Frequency:** Daily  
**SCD Type:** 1 (overwrite) for attributes; Type 2 would be needed for segment history  

### dim_products

| Column | Type | Description | SCD Type |
|--------|------|-------------|----------|
| product_id | INT | Primary key | 0 |
| product_name | VARCHAR | | 1 |
| category | VARCHAR | | 1 |
| price | DECIMAL(8,2) | | 1 |

**Cardinality:** ~5K products  
**Update Frequency:** Weekly  
**SCD Type:** 1  

### dim_date

| Column | Type | Description |
|--------|------|-------------|
| datedate | DATE | |
| year | INT | |
| month | INT | |
| dayweek | INT | 1=Monday, 7=Sunday |
| iscustomers

---



  — Used by: all fact tables
 Strategy:** SCD Type 1 (overwrite)  
 Current segment state is what matters for analytics; history not required  
 UPDATE dimid = X  



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 Customer IDs are stable (never reassigned)
 Order dates are in UTC; no timezone conversion needed
 Product catalog is complete (no missing products)
 No late-arriving facts expected (data arrives within 24h of event)

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 Phase 1: factcustomers, dimdate
 Phase 2: Add factcustomersupportmarketing

Example

See Design Template above — adapt for your business domain.

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