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harvard-art-museums-etl-analytics

Build ETL pipelines for Harvard Art Museums API data with SQL analytics and Streamlit dashboards.

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

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 simplifies building ETL pipelines and analytics dashboards using the Harvard Art Museums API, allowing users to easily manage and analyze museum data.

Core Features & Use Cases

  • API Integration: Extract data from the Harvard Art Museums API.
  • ETL Pipeline: Execute data extraction, transformation, and loading processes.
  • SQL Storage: Store data in MySQL/TiDB Cloud.
  • Analytics Engine: Perform over 20 predefined analytical SQL queries.
  • Interactive Dashboards: Visualize data with Streamlit and Plotly.

Quick Start

To begin, install the skill and set up the environment variables for database connection. Then, execute the ETL pipeline to collect data from the API.

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

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

FAQPage Schema
How do I build an ETL pipeline for the Harvard Art Museums API using Python?▼

To build an ETL pipeline for the Harvard Art Museums API, this solution provides Python scripts that extract artifact data, transform it, and load it into a MySQL database for downstream querying and analysis.

Can I visualize Harvard Art Museums data with Streamlit and Plotly?▼

Yes, you can visualize Harvard Art Museums data using Streamlit and Plotly. The solution generates interactive dashboards that connect directly to your SQL database to display analytical query results.

Do I need a MySQL database to store extracted Harvard Art Museums data?▼

Yes, you need a MySQL or TiDB Cloud database. The ETL pipeline loads the extracted and transformed Harvard Art Museums API data into SQL storage to support the predefined analytical queries.

What SQL analytics queries are available for Harvard Art Museums datasets?▼

The solution includes over 20 predefined analytical SQL queries designed to analyze Harvard Art Museums datasets, enabling you to derive insights directly from the structured artifact data without writing custom SQL.

What's the best way to set up environment variables for a Python ETL pipeline?▼

The best way to set up environment variables for this Python ETL pipeline is using the python-dotenv library, which securely configures your database connection details and API keys before executing the extraction scripts.

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