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harvard-art-museums-data-engineering

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

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

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 end-to-end process of working with Harvard Art Museums data, providing an efficient way to create pipelines, conduct SQL analytics, and visualize collections with Streamlit dashboards.

Core Features & Use Cases

  • API Integration: Connects to the Harvard Art Museums API for fetching artifact data.
  • ETL Pipeline: Automates the process of Extracting, Transforming, and Loading (ETL) data into relational databases.
  • Database Design: Constructs structured MySQL/TiDB tables with proper schema.
  • SQL Analytics: Executes analytical queries for deep insights.
  • Visualization: Creates interactive dashboards using Streamlit and Plotly charts.
  • Use Case: Build a robust data engineering pipeline to process cultural heritage data from the Harvard Art Museums.

Quick Start

Run the 'app.py' script to launch the Streamlit dashboard.

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

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 data, you automate API integration for data retrieval, transform the artifacts, and load them into structured MySQL tables. This skill streamlines that entire extract, transform, and load process.

What is the best way to visualize SQL analytics from cultural heritage data?▼

The best way to visualize SQL analytics from cultural heritage data is by creating interactive dashboards. This skill uses Streamlit and Plotly charts to generate visual reports directly from your structured MySQL database queries.

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

Yes, you need a relational database like MySQL or TiDB to run SQL analytics on Harvard Art Museums data. The skill constructs proper database schemas and requires the mysql-connector-python library to execute analytical queries.

Can I use Streamlit and Plotly to build dashboards for API fetched collections?▼

Yes, you can use Streamlit and Plotly to build dashboards for API fetched collections. This skill leverages both libraries to create interactive visualizations, allowing you to explore artifact data extracted from the Harvard Art Museums API.

What Python dependencies are required to automate data engineering pipelines for cultural heritage institutions?▼

Required Python dependencies include streamlit, pandas, requests, mysql-connector-python, plotly, and python-dotenv. You also need a valid API key for the Harvard Art Museums API to automate the data engineering pipeline.

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