harvard-artifacts-etl-pipeline

Automate ETL pipelines for Harvard Art Museums data into SQL databases.

5|1|Updated May 16, 2026

Search Tags:#SQL #ETL #Streamlit #data engineering #Plotly

Ready to run

npx skills add https://github.com/Aradotso/data-skills --skill harvard-artifacts-etl-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

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

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 addresses the challenge of building comprehensive ETL pipelines and analytics dashboards for the Harvard Art Museums API data, enabling efficient data processing and visualization.

Core Features & Use Cases

ETL Pipeline Creation: Automate the Extract, Transform, and Load process for Harvard Art Museums data.
Data Visualization: Generate interactive dashboards using Streamlit and Plotly.
SQL Database Interaction: Execute SQL queries on the Harvard Art Museums data stored in MySQL/TiDB Cloud databases.
Use Case: Develop a complete data engineering workflow to fetch, transform, and visualize museum artifact data.

Quick Start

Run the ETL pipeline using the command: python run_etl_pipeline.py

Frequently Asked Questions about harvard-artifacts-etl-pipeline

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

FAQPage Schema

How do I build an ETL pipeline for Harvard Art Museums API data?▼

You can build an ETL pipeline for Harvard Art Museums data by running `python run_etl_pipeline.py`, which automates extracting, transforming, and loading artifact information into a SQL database.

What is the best way to visualize SQL data using Streamlit and Plotly?▼

Visualizing SQL data with Streamlit and Plotly is achieved through generated interactive analytics dashboards that query your MySQL database to display Harvard Art Museums artifact data visually.

Do I need a MySQL database to run this ETL pipeline?▼

Yes, a MySQL or TiDB Cloud database is required. The ETL pipeline loads transformed Harvard Art Museums artifact information into a SQL database before you can execute queries and visualize data in the Streamlit dashboard.

Can I use Python to automate data engineering workflows for museum APIs?▼

You can use Python to automate data engineering workflows for museum APIs. This Skill leverages Python dependencies like pandas and requests to fetch, transform, and load Harvard Art Museums data automatically.

What dependencies are required to create Streamlit dashboards for SQL data?▼

Creating Streamlit dashboards for SQL data requires streamlit, pandas, requests, mysql-connector-python, plotly, and python-dotenv to support data extraction, database connection, and interactive visualization.