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harvard-artifacts-etl-streamlit

Automate ETL workflows from the Harvard Art Museums API into SQL databases.

5|1|Updated May 16, 2026
Search Tags:#SQL#ETL#Streamlit#data engineering#Plotly#art museum
One-click install
npx skills add https://github.com/Aradotso/data-skills --skill harvard-artifacts-etl-streamlit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: harvard-artifacts-etl-streamlit
Source: https://github.com/Aradotso/data-skills/tree/main/skills/harvard-artifacts-etl-streamlit
Command: npx skills add https://github.com/Aradotso/data-skills --skill harvard-artifacts-etl-streamlit

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 extraction, transformation, and loading of museum artifact data, providing analytics and visualization capabilities through SQL queries and interactive dashboards.

Core Features & Use Cases

  • ETL Pipeline Automation: Streamlines the process of extracting data from APIs, transforming it, and loading it into a SQL database.
  • Interactive Dashboard: Offers real-time analytics and visualization of art museum data using Streamlit and Plotly.
  • Use Case: Imagine you need to analyze the color distribution of art pieces from the Harvard Art Museums. Use this Skill to extract data, analyze it, and visualize the results in a dashboard.

Quick Start

Install the skill and run the command 'harvard-artifacts-etl-streamlit run-etl-pipeline' to start the ETL process.

Frequently Asked Questions about harvard-artifacts-etl-streamlit

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

FAQPage Schema
How do I extract data from the Harvard Art Museums API and load it into a SQL database?▼

You can automate the ETL pipeline for Harvard Art Museums API data by running the 'harvard-artifacts-etl-streamlit run-etl-pipeline' command, which extracts, transforms, and loads the artifact records into a SQL database using Python and pandas.

What is the best way to visualize art museum data stored in a SQL database?▼

You can build an interactive Streamlit dashboard using Plotly to visualize and analyze the art museum data queried from your SQL database, providing real-time analytics of the extracted artifact records.

Do I need to set up my own SQL database before running the ETL pipeline?▼

Yes, you must have an accessible SQL database configured beforehand. The ETL pipeline requires a database connection using mysql-connector-python to load the transformed Harvard Art Museums data.

Can I use this ETL pipeline to analyze specific attributes like color distribution of art pieces?▼

Yes, the pipeline extracts artifact data from the Harvard Art Museums API and supports analytics such as analyzing the color distribution of art pieces, with results visualized directly in the Streamlit dashboard.

Does the Streamlit dashboard work with MySQL for real-time artifact analytics?▼

Yes, the Streamlit dashboard connects to your SQL database via mysql-connector-python and uses Plotly to render interactive visualizations, enabling real-time analytics of the stored museum artifact data.

What Python dependencies are required to run the ETL pipeline and Streamlit dashboard?▼

The ETL pipeline and dashboard require streamlit, pandas, requests, mysql-connector-python, plotly, and python-dotenv to handle API extraction, data transformation, SQL storage, and interactive visualization.

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