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harvard-artifacts-collection-analytics-pipeline

Extract, transform, and load Harvard Art Museums API data for SQL analysis and Streamlit visualization.

5|1|Updated May 16, 2026
Search Tags:#ETL#Streamlit#data engineering#SQL analysis#art collection
One-click install
npx skills add https://github.com/Aradotso/data-skills --skill harvard-artifacts-collection-analytics-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: harvard-artifacts-collection-analytics-pipeline
Source: https://github.com/Aradotso/data-skills/tree/main/skills/harvard-artifacts-collection-analytics-pipeline
Command: npx skills add https://github.com/Aradotso/data-skills --skill harvard-artifacts-collection-analytics-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 offers a comprehensive data engineering solution to analyze Harvard Art Museums data, including ETL, SQL analytics, and interactive visualization.

Core Features & Use Cases

  • ETL Pipeline: Extract, transform, and load data from the Harvard Art Museums API.
  • SQL Analytics: Perform detailed SQL analytics on art artifacts.
  • Streamlit Dashboard: Create interactive dashboards for art collection data visualization.
  • Use Case: For a museum curator, this Skill can be used to analyze the popularity of different art artifacts and generate insights about the collection.

Quick Start

Clone the repository and install dependencies. Run the ETL pipeline to load data into the database.

Frequently Asked Questions about harvard-artifacts-collection-analytics-pipeline

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 extract collection records using the requests library, transform them with pandas, and load the structured data into a MySQL database for downstream SQL analysis.

What is the best way to visualize SQL analysis results from an art collection database?▼

The best way to visualize SQL analysis results from an art collection database is to build an interactive Streamlit dashboard, using the plotly library to render exploratory data plots directly from the queried pandas DataFrames.

Can I use Streamlit and pandas to analyze museum artifact popularity?▼

Yes, you can use Streamlit and pandas to analyze museum artifact popularity by running SQL queries against your MySQL database, loading the results into a DataFrame, and rendering the metrics on an interactive dashboard.

Do I need a MySQL database to run the Harvard Art Museums data engineering pipeline?▼

Yes, you need a MySQL database because the pipeline requires the mysql-connector-python library to establish database connections and load the extracted API records for persistent SQL analytics.

How does python-dotenv manage API credentials in a data engineering project?▼

Python-dotenv manages API credentials in a data engineering project by loading environment variables from a local configuration file, ensuring that sensitive Harvard Art Museums API keys remain secure and separate from the ETL scripts.

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