nw-data-architecture-patterns

Map analytics needs to data warehouse, lake, lakehouse, or mesh patterns.

Updated Apr 15, 2026

Search Tags:#governance #etl #architecture-patterns #data-warehouse #data-lake #data-architecture #data-lakehouse

Ready to run

npx skills add https://github.com/StudentCristian/nWave-github --skill nw-data-architecture-patterns

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nw-data-architecture-patterns
Source: https://github.com/StudentCristian/nWave-github/tree/main/.github/skills/nw-data-architecture-patterns
Command: npx skills add https://github.com/StudentCristian/nWave-github --skill nw-data-architecture-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Clarifies how to choose among data architecture patterns (data warehouse, data lake, data lakehouse, and data mesh) and explains how each pattern supports governance, ETL/ELT pipelines, and scaling strategies.

Core Features & Use Cases

Decision guidance on selecting the appropriate architecture for structured analytics, data science workloads, and autonomous domain data products.
Architectural patterns with explanations of schemas, governance considerations, and technology touchpoints (e.g., Snowflake, AWS/GCP/Azure options, and orchestration).
Use Case: For a large organization needing centralized reporting with domain-owned data products, align with a data mesh or lakehouse approach to balance governance and flexibility.

Quick Start

Apply the pattern selection guidance to map your current data needs to one of: Data Warehouse, Data Lake, Data Lakehouse, or Data Mesh.

Frequently Asked Questions about nw-data-architecture-patterns

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

FAQPage Schema

How do I choose between a data warehouse, data lake, data lakehouse, and data mesh?▼

Choosing a data architecture pattern depends on your analytics needs, data product autonomy, and multi-domain governance requirements. This Skill maps your specific context to the most suitable pattern by evaluating structured analytics, data science workloads, and domain ownership.

Does this data architecture guidance apply to enterprise-scale BI and multi-domain governance?▼

Yes, this data architecture guidance applies to enterprise-scale BI and multi-domain governance scenarios. It specifies criteria and pattern definitions to balance domain autonomy with varying data quality requirements across large organizations.

What technology considerations are included for data architecture patterns?▼

Technology considerations include schema definitions, ETL/ELT pipeline integration, and platform touchpoints like Snowflake, AWS, GCP, and Azure. The guidance aligns these technologies with governance and orchestration strategies for scalable analytics.

When should I not use a single centralized data warehouse for my analytics?▼

You should not use a single centralized data warehouse when your organization needs autonomous domain data products and high flexibility for data science workloads. This Skill helps identify when a data mesh or lakehouse approach better balances governance and domain autonomy.