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architecting-data

Guide data platform design across storage, modeling, and architecture decisions.

503|73|Updated Nov 13, 2025
Search Tags:#data modeling#data architecture#medallion architecture#lakehouse#data mesh#data platform
One-click install
npx skills add https://github.com/ancoleman/ai-design-components --skill architecting-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: architecting-data
Source: https://github.com/ancoleman/ai-design-components/tree/main/skills/architecting-data
Command: npx skills add https://github.com/ancoleman/ai-design-components --skill architecting-data

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides strategic guidance for designing robust and scalable data platforms, helping you navigate complex architectural choices.

Core Features & Use Cases

  • Storage Paradigms: Choose between Data Lake, Data Warehouse, or Data Lakehouse.
  • Data Modeling: Select appropriate approaches like Dimensional, Data Vault, or Wide Tables.
  • Data Mesh: Evaluate readiness and principles for decentralized data architectures.
  • Medallion Architecture: Implement Bronze, Silver, and Gold layers for data quality.
  • Use Case: You are tasked with designing a new data platform for your company. Use this Skill to understand the trade-offs between different storage paradigms, choose the best data modeling approach for your BI and ML needs, and decide if a data mesh is suitable for your organization.

Quick Start

Use the architecting-data skill to get guidance on choosing a storage paradigm for a new data platform.

Frequently Asked Questions about architecting-data

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

FAQPage Schema
What is the difference between a data lakehouse and a data warehouse for modern data platform design?▼

A data warehouse stores structured relational data, while a data lakehouse combines data lake flexibility with warehouse analytics. This Skill helps you choose between these storage paradigms by evaluating trade-offs based on your specific BI, ML, and organizational use cases.

How do I implement medallion architecture with Bronze, Silver, and Gold layers?▼

Implement medallion architecture by structuring data through Bronze for raw ingestion, Silver for cleansed data, and Gold for business-level aggregates. This Skill provides strategic guidance to establish these layers, ensuring progressive data quality improvement.

When should I use dimensional modeling versus data vault for data architecture?▼

Use dimensional modeling for straightforward BI reporting and data vault when handling complex, evolving source systems with historical tracking needs. This Skill evaluates data modeling approaches to select the best fit for your BI and ML requirements.

How do I evaluate organizational readiness for a decentralized data mesh architecture?▼

Evaluate data mesh readiness by assessing organizational maturity, data ownership boundaries, and self-serve infrastructure capabilities. This Skill offers decision frameworks to determine if decentralized data architectures suit your operational context.

What are the trade-offs of choosing Iceberg versus Delta Lake table formats?▼

Choosing between Iceberg and Delta Lake involves evaluating ecosystem compatibility, query engine support, and transaction consistency. This Skill assists in selecting appropriate table formats based on your storage paradigm and architecture.

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