Design dbt data mart layers with domain ownership, stable grain, and BI-ready structure.
npx skills add https://github.com/nrakow/ae-skills-dev --skill marts-design
Please help me install this Agent Skill. Skill: marts-design Source: https://github.com/nrakow/ae-skills-dev/tree/main/skills/marts-design Command: npx skills add https://github.com/nrakow/ae-skills-dev --skill marts-design
Designing the data mart layer — the business-facing output of your dbt project — with clear domain ownership, consistent grain, and BI-ready structure.
Start by outlining domain ownership and grain, then draft the mart models and a documentation plan.
High-intent search queries and answers about installing and using this skill.
To design a data mart layer in dbt, define domain ownership, establish a stable grain, and apply BI-ready structures for fact and dimension tables. This approach reduces BI confusion by aligning models with specific business domains and naming conventions.
Structure staging-to-mart transformations by applying consistent grain, primary keys, and foreign keys. Use domain-based, fact/dimension, or subject-area mart patterns to ensure downstream analytics remain trusted and business-ready.
Enforce data mart governance by applying validation checks for grain consistency, primary keys, foreign keys, and model deprecation. These checks ensure trusted downstream analytics and maintain structure across multiple dbt domains.
Yes, you can model fact and dimension tables for different domains by defining domain ownership and applying subject-area mart patterns. This ensures the mart structure aligns with existing data-stack context and validation steps.
Without stable grain and naming conventions, dbt marts suffer from BI confusion and broken governance. Skipping validation checks on primary and foreign keys ultimately degrades trust in downstream analytics.
MGPowerlyticsDesign and maintain scalable data models with standardized schemas and governance.
masermediagroup-stackOrchestrate multi-widget dashboards with synchronized KPI cards, charts, and filters.
armanzeroeightGenerate schema, data quality, and freshness tests for dbt models.
borgheiModel data and build dbt pipelines from staging to marts.
