Harvard Art Museums Data Engineering Analytics

This is a complete data engineering tutorial that walks you through building an ETL pipeline with the Harvard Art Museums API. You'll extract artifact data via paginated requests, transform nested JSON into three relational tables (metadata, media, colors), load everything into MySQL or TiDB, then build a Streamlit dashboard with Plotly visualizations. The code handles rate limiting, includes 20+ analytical queries, and covers the full workflow from API key setup to interactive dashboards. Good if you're learning data engineering fundamentals or need a museum data starter project. The repo includes actual implementation code, not just concepts, so you can run it immediately after setting up your API credentials and database connection.

skills.sh installs: 628
GitHub stars: 2
last updated: Jul 14, 2026

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Install

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

Installs into .claude/skills of the current project.

Files

Harvard Art Museums Data Engineering & Analytics

Skill by ara.so — Data Skills collection.

This project demonstrates a complete data engineering workflow: extracting data from the Harvard Art Museums API, transforming it into relational tables, loading into SQL databases, and building interactive analytics dashboards with Streamlit.

What This Project Does

API Integration: Fetches artifact data from Harvard Art Museums API with pagination and rate limiting
ETL Pipeline: Transforms nested JSON into structured relational tables (metadata, media, colors)
SQL Storage: Loads data into MySQL/TiDB Cloud with proper schema design
Analytics: Executes 20+ predefined analytical queries
Visualization: Interactive Plotly dashboards for data exploration

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

# Install dependencies
pip install -r requirements.txt

SKILL.md on GitHub