- name
- data-model
- description
- Deep data modeling workflow—grain, facts and dimensions, keys, slowly changing dimensions, normalization trade-offs, and analytics query patterns. Use when designing warehouse/analytics models or reviewing star/snowflake schemas.
Data Model
Analytics models succeed when grain is explicit, keys are stable, and slowly changing dimensions are chosen deliberately—not “star schema by default.”
When to Offer This Workflow
Trigger conditions:
- Designing a warehouse, lakehouse, or BI layer
- Confusion on one row per what; duplicate counts in reports
- Refactoring dimensional models for performance or clarity
Initial offer:
Use six stages: (1) business questions & grain, (2) conformed dimensions, (3) facts & measures, (4) dimensions & SCD types, (5) keys & integrity, (6) performance & evolution). Confirm tooling (dbt, dimensional DW, BigQuery, etc.).
Stage 1: Business Questions & Grain
Goal: Grain = the atomic row: e.g., “one line item per order per day” not “sort of per order.”
Practices
- List questions the model must answer; derive grain from smallest needed detail
Exit condition: One sentence grain per fact table.
Stage 2: Conformed Dimensions
Goal: Same customer/product definitions across facts—shared dimension tables or SCD policy aligned.
Stage 3: Facts & Measures
Goal: Additive vs semi-additive vs non-additive measures documented (balances, distinct counts).
Practices
- Degenerate dimensions vs junk dimensions—avoid wide fact sprawl without reason
Stage 4: Dimensions & SCD Types
Goal: SCD1 overwrite vs SCD2 history with valid_from/valid_to vs SCD3 limited history—match compliance and reporting needs.
Stage 5: Keys & Integrity
Goal: Surrogate keys in facts; natural keys preserved as attributes; referential integrity strategy in the warehouse layer.
Stage 6: Performance & Evolution
Goal: Partition and cluster keys for large facts; late-arriving facts policy; version dims when schema evolves.
Final Review Checklist
- [ ] Grain explicit per fact table
- [ ] Conformed dimensions planned
- [ ] Measure additivity documented
- [ ] SCD strategy per critical dimension
- [ ] Keys and late-arriving data handled
Tips for Effective Guidance
- Fan traps and chasm traps in BI—flag when joining across facts incorrectly.
- Snapshot fact tables for point-in-time balances vs transaction facts.
Handling Deviations
- Event-only pipelines: still model curated dimensions for analysis, not only raw JSON.