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harvard-art-museum-etl-analytics

Extract Harvard Art Museums API data into a relational database and visualize with Streamlit.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires 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 end-to-end data engineering pipelines for analyzing Harvard Art Museums data, streamlining the process of data extraction, transformation, and visualization.

Core Features & Use Cases

  • ETL Pipeline Creation: Build ETL pipelines to extract data from the Harvard Art Museums API, transform it into relational databases, and load it for analysis.
  • Data Analytics: Perform SQL analytics on the extracted data, including queries on culture, century, media, colors, and departments.
  • Visualization: Generate interactive visualizations and dashboards using Streamlit for data exploration.
  • Use Case: For a museum curator, this Skill could be used to analyze the collection's color distribution, media usage, or artifact distribution by culture over time.

Quick Start

To get started, run the ETL pipeline with the provided script and connect your database.

Frequently Asked Questions about harvard-art-museum-etl-analytics

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?▼

To build an ETL pipeline for Harvard Art Museums API data, you automate data extraction, transform it into a relational format, and load it into a database using Python scripts with pandas and requests. This process prepares the cultural heritage dataset for SQL analytics.

What is the process for analyzing cultural heritage data with SQL and Streamlit?▼

Analyzing cultural heritage data with SQL and Streamlit involves loading extracted API data into a relational database, running SQL queries on attributes like culture or century, and generating interactive dashboards with Streamlit and Plotly for data exploration.

Do I need a MySQL database to run SQL analytics on Harvard Art Museums data?▼

Yes, you need a MySQL database to run the SQL analytics on Harvard Art Museums data. The Skill uses the mysql-connector-python dependency to establish the connection and load the transformed pandas DataFrames into your relational database environment.

Can I use pandas and Plotly to visualize museum collection distributions?▼

Yes, you can use pandas and Plotly to visualize museum collection distributions. The Skill leverages pandas for data transformation and Plotly within a Streamlit application to generate interactive visualizations of artifact distributions by media, colors, or culture.

What's the best way to automate extraction from the Harvard Art Museums API into a relational database?▼

The best way to automate extraction from the Harvard Art Museums API into a relational database is using Python requests to fetch the data, pandas to transform the JSON responses into structured tables, and mysql-connector-python to load the records for analysis.

What types of SQL analytics can I perform on Harvard Art Museums collection data?▼

You can perform SQL analytics on Harvard Art Museums collection data by querying attributes such as culture, century, media, colors, and departments. This allows you to analyze artifact distributions and color usage across the dataset directly within your relational database.

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