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data-warehouse

Design scalable data warehouse architectures with star schemas and ETL patterns.

1|Updated Nov 18, 2025
Search Tags:#etl#data-quality#data-warehouse#dimensional-modeling#star-schema#data-architecture#slowly-changing-dimensions
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-sql --skill data-warehouse-pluginagentmarketplace
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-warehouse
Source: https://github.com/pluginagentmarketplace/custom-plugin-sql/tree/main/skills/data-engineer
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-sql --skill data-warehouse-pluginagentmarketplace

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Designing scalable, reliable data warehouses using star schemas and dimensional modeling to support analytics at scale.

Core Features & Use Cases

  • Star schema basics and fact/dimension table design for consistent analytics.
  • Slowly changing dimensions (SCD) handling, conformed dimensions, and bridge tables for many-to-many relationships.
  • Materialized views, aggregates, and ETL/ELT patterns to optimize performance and data quality.

Quick Start

Follow the included SQL schemas and ETL patterns to start building a production-ready data warehouse.

Frequently Asked Questions about data-warehouse

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a scalable data warehouse using dimensional modeling?▼

Slowly Changing Dimensions (SCDs) handle attribute changes in dimension tables over time. They preserve historical accuracy by tracking updates, using specific patterns to manage data quality and maintain reliable analytics across ETL pipelines.

What is the difference between ETL and ELT pipelines for data warehousing?▼

ETL transforms data before loading it into the warehouse, while ELT loads raw data first and transforms it within the warehouse. Both pipeline patterns support data quality checks, governance, and materialized views for performance optimization.

How do I model many-to-many relationships in a star schema?▼

Materialized views and aggregates pre-calculate frequently queried data, significantly reducing query latency. They optimize data warehouse performance by minimizing real-time computation needs during complex analytics and ETL processing.

When do I need conformed dimensions in a data warehouse architecture?▼

You need conformed dimensions when multiple fact tables or subject areas must share consistent dimensional attributes. They ensure standardized reporting and reliable analytics across different data marts within the same data warehouse architecture.

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