Dimensional Data Model Design & Documentation
Design and document dimensional data warehouses with templates and best practices
What You Can Do
You can design production-ready dimensional data models (star schemas, snowflake schemas) from business requirements, generate complete technical documentation including DDL scripts, and validate your schema designs for cardinality, grain alignment, and slowly changing dimension strategies. Claude helps you plan fact and dimension tables, identify conformed dimensions, and create comprehensive ERD descriptions and SQL implementations.
Features
plan fact tables, dimensions, and hierarchies from requirements
validate fact table grain, measure additivity, and dimension join scenarios
implement Type 1, Type 2, and Type 3 with surrogate keys
map reusable dimensions across multiple fact tables and subject areas
create ERD descriptions, data dictionaries, and lineage diagrams
produce CREATE TABLE statements, indexes, and constraints ready for your database
define drill-down paths, aggregate levels, and fact table joins
design bridge tables and effective-dated rows for Type 2 implementation
Example Output
Fact & Dimension Design:
FACT_SALES (Grain: Order Line Item)
├─ DIMENSION_DATE (SCD Type 1)
├─ DIMENSION_CUSTOMER (SCD Type 2 with effective dates)
├─ DIMENSION_PRODUCT (SCD Type 2 with versioning)
└─ DIMENSION_CHANNEL (SCD Type 1)
Measures:
- sales_amount (additive)
- quantity (additive)
- discount_percent (semi-additive)
Generated DDL:
CREATE TABLE dim_customer (
customer_key BIGINT PRIMARY KEY,
customer_id VARCHAR(50),
effective_date DATE,
end_date DATE,
is_current BOOLEAN
);
Documentation Output: Dimension definitions, SCD handling strategy, conformed dimension lineage, and SQL implementation guide.
What's Included
- SKILL.md: Complete dimensional modeling methodology with workflows, validation checklists, and decision trees
- Data Model Templates: Blank star schema canvas, fact table grain worksheet, dimension design template
- SCD Implementation Guides: Type 1, Type 2, and Type 3 slowly changing dimension patterns with SQL examples
- Documentation Templates: Data dictionary template, ERD description format, lineage diagram structure
- Validation Checklists: Grain alignment verification, measure additivity rules, conformed dimension mapping
- SQL Generation Snippets: DDL templates, surrogate key patterns, effective dating logic
Who It's For
- Data warehouse architects designing new enterprise data warehouses
- BI and analytics engineers building dimensional schemas for reporting
- Data engineers implementing fact and dimension tables
- Database designers planning schema structures for analytics
- Solutions architects documenting warehouse designs for client delivery
Best For
- Designing star schemas and snowflake schemas from business requirements
- Planning and validating fact table grain and measure additivity
- Implementing slowly changing dimensions (Type 1, 2, and 3)
- Mapping conformed dimensions across multiple subject areas
- Generating comprehensive schema documentation and DDL scripts
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