Harvard Art Museum Etl Analytics

This is a full-stack data engineering tutorial wrapped as a skill. It walks you through building an ETL pipeline that pulls artifact data from Harvard's Art Museums API, loads it into MySQL or TiDB with a proper relational schema (metadata, media, and colors tables with foreign keys), then queries it with SQL and visualizes results in a Streamlit dashboard with Plotly charts. Good for learning real-world data pipeline patterns or if you actually need to analyze museum collections by culture, century, or color distribution. The code is thorough with pagination handling, rate limiting, and environment variable management. It's more educational scaffold than production-ready tool, but that's the point.

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-museum-etl-analytics --agent claude-code

Installs into .claude/skills of the current project.

Files

Harvard Art Museum ETL Analytics Skill

Skill by ara.so — Data Skills collection.

This skill enables you to build end-to-end data engineering and analytics applications using the Harvard Art Museums API. The project demonstrates real-world ETL pipelines, SQL database design, analytical queries, and interactive Streamlit dashboards for artifact collections.

What This Project Does

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

API Integration: Fetch artifact data from Harvard Art Museums API with pagination and rate limiting
ETL Pipeline: Extract, transform, and load artifact metadata, media, and color data into relational databases
SQL Analytics: Pre-built analytical queries for insights on culture, century, media, colors, and departments
Visualization: Interactive Plotly charts and Streamlit dashboards for data exploration
Database Design: Properly structured relational schema with foreign key relationships

Architecture flow: API → ETL → SQL → Analytics → Visualization