Harvard Artifacts Etl Analytics

This is a practical teaching project that walks you through building a complete data pipeline from the Harvard Art Museums API. You're extracting artifact metadata with pagination, normalizing nested JSON into three relational tables (metadata, media, colors), loading it into MySQL, and visualizing 20+ predefined queries in a Streamlit dashboard. It's solid for learning ETL fundamentals with a real API, though the analytical queries are prebuilt rather than dynamic. The code patterns are clear and the museum data is interesting to work with. Good starter project if you're moving from scripts to structured data engineering workflows, less useful if you need production-grade error handling or streaming updates.

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-artifacts-etl-analytics --agent claude-code

Installs into .claude/skills of the current project.

Files

Harvard Artifacts ETL Analytics Skill

Skill by ara.so — Data Skills collection.

This skill enables AI coding agents to help developers build end-to-end ETL pipelines and analytics applications using the Harvard Art Museums API. The project demonstrates real-world data engineering patterns including API integration, data transformation, SQL database design, and interactive visualization with Streamlit.

What This Project Does

The Harvard Artifacts Collection application:

Extracts artifact data from Harvard Art Museums API with pagination and rate limiting
Transforms nested JSON into normalized relational tables
Loads data into MySQL/TiDB Cloud databases
Provides 20+ predefined analytical SQL queries
Visualizes results with interactive Plotly charts in Streamlit

Architecture Flow: 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

# Install dependencies
pip install -r requirements.txt

SKILL.md on GitHub