data-modeling
Guides warehouse data modeling with a Kimball/wide-table/Data Vault decision tree, medallion dbt layering, and fact table templates; use when designing a warehouse schema, choosing a modeling approach, or refactoring an existing model.
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- 2026年10月16日 19:08
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SKILL.md
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- data-modeling
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- Guides warehouse data modeling with a Kimball/wide-table/Data Vault decision tree, medallion dbt layering, and fact table templates; use when designing a warehouse schema, choosing a modeling approach, or refactoring an existing model.
# Skill: Data Modeling
## When to load
When designing a warehouse schema, choosing between modeling approaches, or refactoring an existing model.
## Approach Decision Tree
```
Primary consumer?
→ BI tools + analysts: Kimball (dimensional)
→ Data science + ML: Wide denormalized tables
→ Multiple teams: Data Vault
History tracking?
→ Never/rarely: Type 1 SCD (overwrite)
→ Track current + previous: Type 2 SCD (versioned rows)
Scale?
→ < 100 GB: Simple star schema
→ 100 GB – 10 TB: Partitioned star schema
→ > 10 TB: Data Vault or Lakehouse (Delta/Iceberg)
```
## dbt Layering (Medallion Architecture)
```
sources/ ← Raw data as-is from source systems
staging/ ← Cleaned, renamed, typed. 1:1 with source tables.
intermediate/ ← Business logic, joins. Not exposed directly.
marts/ ← Dimensional models for specific use cases
├── core/ ← fct_*, dim_*
└── finance/ ← rpt_*
```
## Fact Table Template
```sql
CREATE TABLE fct_orders (
order_key BIGINT PRIMARY KEY, -- surrogate key
order_id VARCHAR NOT NULL, -- natural key
user_key BIGINT REFERENCES dim_users(user_key),
date_key INT REFERENCES dim_date(date_key),
quantity INT NOT NULL,
total_amount NUMERIC(10,2) NOT NULL,
loaded_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
source_system VARCHAR DEFAULT 'orders-api'
);
```
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