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harvard-art-museums-data-pipeline

Builds end-to-end ETL pipelines analyzing Harvard Art Museums API data with SQL and Streamlit dashboards.

5|1|Updated May 16, 2026
Search Tags:#SQL#ETL#data analysis#Streamlit#data engineering
One-click install
npx skills add https://github.com/Aradotso/data-skills --skill harvard-art-museums-data-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: harvard-art-museums-data-pipeline
Source: https://github.com/Aradotso/data-skills/tree/main/skills/harvard-art-museums-data-pipeline
Command: npx skills add https://github.com/Aradotso/data-skills --skill harvard-art-museums-data-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 need for efficient data engineering in managing, analyzing, and visualizing art museum collections, leveraging the Harvard Art Museums API.

Core Features & Use Cases

  • ETL Workflow: Provides a comprehensive ETL (Extract, Transform, Load) workflow for processing data from the Harvard Art Museums API.
  • SQL Analytics: Employs SQL for deep data analysis, offering insights into artifact metadata and collections.
  • Data Visualization: Generates interactive Streamlit dashboards for artifact data visualization using Plotly and Streamlit.
  • Use Case: Suppose you're a museum curator or researcher interested in the cultural heritage of specific artifacts. This Skill can help you collect, process, and visualize the data, enabling a better understanding of the collection and its artifacts.

Quick Start

To start building your data pipeline, clone the repository, install dependencies, and run the provided Streamlit dashboard with your environment variables set up.

Frequently Asked Questions about harvard-art-museums-data-pipeline

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

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

To build an ETL data pipeline with the Harvard Art Museums API, you extract artifact data, transform it using pandas, load it into MySQL, and visualize the collections interactively using Streamlit and Plotly dashboards.

How do I visualize SQL analytics from art museum collections in Streamlit?▼

You can visualize SQL analytics from art museum collections in Streamlit by querying the processed artifact data from MySQL and rendering interactive Plotly dashboards to explore cultural heritage metadata and insights.

Can I use Python and MySQL for analyzing Harvard Art Museums API data?▼

Yes, you can use Python and MySQL for analyzing Harvard Art Museums API data by leveraging python-dotenv for API keys, mysql-connector-python for database loading, and pandas for transforming the extracted artifact metadata.

What is the best way to set up a data engineering project for art collection analysis?▼

The best way to set up a data engineering project for art collection analysis is implementing a structured ETL workflow that extracts API data, loads it into a SQL database, and outputs interactive Streamlit visualizations for artifact insights.

Do I need specific Python libraries to run the Streamlit dashboard for artifact data?▼

Yes, you need specific Python libraries including Streamlit, pandas, requests, mysql-connector-python, plotly, and python-dotenv to execute the ETL processes and render the artifact data visualizations.

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