Design dimensional models with star schemas and SQL DDL templates.
npx skills add https://github.com/timequity/plugins --skill data-modeling-timequity
Please help me install this Agent Skill. Skill: data-modeling Source: https://github.com/timequity/plugins/tree/main/craft-coder/data/data-modeling Command: npx skills add https://github.com/timequity/plugins --skill data-modeling-timequity
Data analytics often struggles with poor schema design. This Skill presents dimensional modeling, star schemas, and normalization concepts to build analytics-ready data models.
Create a simple star schema for a sales analytics dataset (date, store, product) and define a fact table with measures like quantity and total_amount.
High-intent search queries and answers about installing and using this skill.
Dimensional modeling organizes data into star schemas with fact tables (measurements) and dimension tables (context), enabling faster analytics queries and simpler reporting logic. It structures data specifically for analytical workloads rather than transactional systems.
Start by identifying your business process (e.g., sales), define fact tables with measurable events and foreign keys to dimensions, then create dimension tables with descriptive attributes. Use surrogate keys for all dimensions and maintain consistent naming conventions across tables.
Normalization minimizes data redundancy for transactional systems; dimensional modeling denormalizes data for analytical speed. Star schemas intentionally duplicate dimension data to reduce joins and accelerate reporting queries on large datasets.
Slowly changing dimensions track attribute changes over time using three strategies: Type 1 overwrites old values, Type 2 creates new rows with effective dates, and Type 3 stores current and previous values in separate columns. Choose based on whether you need historical analysis.
Yes, dimensional modeling applies to any analytics-focused data structure, from enterprise data warehouses to specialized analytics marts. Star schema principles scale efficiently regardless of data volume when designed with appropriate granularity and indexing.
Use prefixes like 'fct_' for fact tables and 'dim_' for dimensions, keep names descriptive and lowercase with underscores, use consistent suffixes for keys (e.g., '_id' for surrogate keys), and document audit fields standardly across all tables.
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