Harvard Art Museums Etl Analytics

This is a complete data engineering starter for working with Harvard Art Museums' API. It walks you through the full pipeline: pagination and rate limiting on the API calls, relational schema design across three tables (metadata, media, colors), and a Streamlit dashboard with 20+ analytics queries already written. Good for learning ETL patterns with real cultural data, or if you need museum artifact analytics fast. The code is explicit about things like foreign keys and rate limits, so you can actually see how the pieces connect. Uses TiDB Cloud or MySQL for storage and Plotly for visualizations. More教学-oriented than production-hardened, but the API integration logic is solid.

skills.sh installs: 1.5K
GitHub stars: 2
last updated: Jul 14, 2026

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Install

$ npx -y skills add aradotso/data-skills --skill harvard-art-museums-etl-analytics --agent claude-code

Installs into .claude/skills of the current project.

Files

Harvard Art Museums ETL Analytics

Skill by ara.so — Data Skills collection.

This skill enables AI coding agents to build end-to-end data engineering and analytics applications using the Harvard Art Museums API. The project demonstrates real-world ETL pipelines, SQL analytics, and interactive data visualization using Streamlit.

What This Project Does

The Harvard-Artifacts-Collection-Data-Engineering-Analytics-App provides:

API Integration: Collect artifact data from Harvard Art Museums API with pagination and rate limiting
ETL Pipeline: Extract, transform, and load artifact metadata, media details, and color data
SQL Storage: Store structured data in MySQL/TiDB Cloud with proper relational schema
Analytics Engine: Execute 20+ predefined analytical SQL queries
Interactive Dashboards: Visualize query results using Streamlit and Plotly

The architecture follows: API → ETL → SQL → Analytics → Visualization

Installation

# Clone the repository
git clone https://github.com/Manali0711/Harvard-Artifacts-Collection-Data-Engineering-Analytics-App.git
cd Harvard-Artifacts-Collection-Data-Engineering-Analytics-App

SKILL.md on GitHub