Harvard Artifacts Collection Analytics App
This is a complete ETL reference implementation for pulling artifacts from Harvard Art Museums' API into a relational database with a Streamlit frontend. You get the full pipeline: API pagination handling, JSON to normalized SQL transformation across three tables (metadata, media, colors), and Plotly visualizations for exploring the collection. It's built for TiDB Cloud or MySQL and includes practical patterns like rate limiting and environment-based config. Good starting point if you're learning data engineering fundamentals or need to adapt a similar museum/cultural API project. The code examples in the docs are thorough enough to understand the architecture before running it.
- skills.sh installs
- 907
- GitHub stars
- 2
- last updated
- Jul 14, 2026
Install
$ npx -y skills add aradotso/data-skills --skill harvard-artifacts-collection-analytics-app --agent claude-codeInstalls into .claude/skills of the current project.
Files
Harvard Artifacts Collection Analytics App
Skill by ara.so — Data Skills collection
What This Project Does
The Harvard-Artifacts-Collection-Data-Engineering-Analytics-App is an end-to-end data engineering application that demonstrates real-world ETL pipelines, SQL analytics, and interactive visualization. It extracts artifact data from the Harvard Art Museums API, transforms it into relational database structures, and provides interactive analytics dashboards using Streamlit.
Key capabilities:
- Extract artifact metadata, media, and color data from Harvard Art Museums API
- Transform nested JSON into normalized relational tables
- Load data into MySQL/TiDB Cloud databases
- Execute analytical SQL queries
- Visualize results with interactive Plotly charts
- Handle API pagination and rate limiting
Installation
Prerequisites
# Python 3.7+
python --version
# Install required packages
pip install streamlit pandas requests mysql-connector-python plotly python-dotenv
Project Setup
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- Repository
- github.com/aradotso/data-skills
- Categories
- Data Science & ML, Marketing & SEO
- First seen
- Jul 14, 2026
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