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Data Architecture Design Guide

A strategic skill for choosing between data lake/warehouse/lakehouse, modeling styles, medallion layers, table formats, and data mesh readiness.

Data & AnalyticsAdvanced★ 513⑂ 73AI score 8/10Last updated: Dec 11, 2025

What it does

Structures the strategic decisions behind modern cloud-native data platforms.

  • Storage paradigm selection: lake vs warehouse vs lakehouse, weighed by use case, budget, and org size
  • Modeling approach: decision tree across dimensional (Kimball), 3NF, Data Vault 2.0, and wide tables
  • Open table formats: Iceberg vs Delta Lake vs Hudi, with Iceberg recommended for greenfield work
  • Medallion architecture: Bronze→Silver→Gold responsibilities, quality gates per hop, and SQL patterns
  • Data mesh readiness: six-factor 1–5 scoring with thresholds for centralized, hybrid, or mesh
  • Modern stack & governance: ingestion/transformation/orchestration/BI tool picks plus catalog, lineage, quality, and access-control patterns

Who it's for

  • Data architects designing a new platform or modernizing a legacy warehouse
  • Platform engineers who must pick a tool stack and estimate monthly cost
  • Data leaders who need a defensible answer on whether to adopt data mesh
  • Analytics engineers standardizing layers in dbt, Snowflake, or Databricks

Example uses

  1. "50-person startup with PostgreSQL, MongoDB, and Stripe" → a simple warehouse plan on BigQuery + Airbyte + dbt under $1K/month
  2. "Plan our Oracle warehouse migration to the cloud" → incremental CDC migration into a medallion lakehouse with cost-savings estimates
  3. "200-person company, 5-person central data team — should we go data mesh?" → a "not yet" verdict from the six-factor score, plus a self-serve platform and governance roadmap

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/ancoleman/ai-design-components/HEAD/skills/architecting-data/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/architecting-data folder from the GitHub repo ancoleman/ai-design-components into my ~/.claude/skills/architecting-data/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/ancoleman/ai-design-components.git && mkdir -p ~/.claude/skills && cp -r ai-design-components/skills/architecting-data ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and move to the folder where you keep repos.
  2. Clone the repository: git clone https://github.com/ancoleman/ai-design-components.git
  3. Create the skills folder if needed: mkdir -p ~/.claude/skills
  4. Copy the skill: cp -r ai-design-components/skills/architecting-data ~/.claude/skills/
  5. Verify the references/ and examples/ subfolders came along: ls ~/.claude/skills/architecting-data
  6. Restart Claude Code, then ask something like "design a lakehouse architecture for us" and the skill will be picked up automatically.
View source on GitHub ↗License: MIT