harvard-art-museum-data-engineering

Automate ETL from the Harvard Art Museums API into SQL databases with Streamlit dashboards.

5|1|Updated May 16, 2026

Search Tags:#ETL #Streamlit #data engineering #data analytics #Harvard Art Museums

Ready to run

npx skills add https://github.com/Aradotso/data-skills --skill harvard-art-museum-data-engineering

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: harvard-art-museum-data-engineering
Source: https://github.com/Aradotso/data-skills/tree/main/skills/harvard-art-museum-data-engineering
Command: npx skills add https://github.com/Aradotso/data-skills --skill harvard-art-museum-data-engineering

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires streamlit, pandas, requests, mysql-connector-python, plotly, python-dotenv, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the Extract, Transform, Load (ETL) process and provides analytics for the Harvard Art Museums API, streamlining data engineering tasks.

Core Features & Use Cases

ETL Pipeline: Extracts artifact data from the API, transforms it into relational tables, and loads it into SQL databases.
Analytics Dashboard: Offers interactive analytics dashboards using Streamlit for data visualization.
Use Case: Ideal for data engineers and analysts looking to automate the processing of museum collection data and create insightful visualizations.

Quick Start

Use the harvard-art-museum-data-engineering skill to load and analyze the museum artifact data from the Harvard Art Museums API.

Frequently Asked Questions about harvard-art-museum-data-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate the ETL pipeline for Harvard Art Museums API data?▼

The skill uses Python, pandas, and requests to extract data from the Harvard Art Museums API, transform it into relational tables, and load it into a MySQL database using mysql-connector-python.

How do I build a Streamlit analytics dashboard for museum collection data?▼

To build a Streamlit dashboard for museum collection data, the skill uses Streamlit and Plotly to render interactive analytics visualizations from SQL database records loaded via pandas.

What do I need to set up to connect to the Harvard Art Museums API and MySQL database?▼

You need Python with the requests and mysql-connector-python libraries installed, plus a python-dotenv file to manage your Harvard Art Museums API key and MySQL database credentials securely.

How does the ETL process transform API records into relational tables?▼

The ETL process uses pandas to flatten nested JSON responses from the Harvard Art Museums API into structured DataFrames, which are then mapped and loaded into SQL database schemas.

What is the best way to visualize Harvard Art Museums artifact data in Python?▼

The best way to visualize Harvard Art Museums artifact data in Python is by using Streamlit and Plotly to create interactive dashboards that read transformed data directly from SQL databases.