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harvard-art-museums-data-engineering-pipeline

Builds an ETL pipeline from the Harvard Art Museums API into SQL tables with Streamlit dashboards.

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-museums-data-engineering-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: harvard-art-museums-data-engineering-pipeline
Source: https://github.com/Aradotso/data-skills/tree/main/skills/harvard-art-museums-data-engineering-pipeline
Command: npx skills add https://github.com/Aradotso/data-skills --skill harvard-art-museums-data-engineering-pipeline

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 streamlines the process of building comprehensive data engineering pipelines for the Harvard Art Museums API, from data extraction to visualization.

Core Features & Use Cases

  • API Integration: Fetches and processes data from the Harvard Art Museums API.
  • ETL Pipeline: Extracts, transforms, and loads nested JSON into normalized SQL tables.
  • Database Design: Implements a multi-table relational schema with proper foreign keys.
  • SQL Analytics: Offers 20+ predefined analytical queries for insights.
  • Interactive Visualization: Provides Streamlit dashboards with Plotly charts.
  • Use Case: Develop a robust data engineering pipeline to analyze the art collection data from the Harvard Art Museums.

Quick Start

Run the ETL pipeline and start the Streamlit dashboard to explore Harvard Art Museums data.

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

You can build an ETL pipeline for Harvard Art Museums API data by extracting nested JSON, transforming it with pandas, and loading it into normalized SQL tables for analysis.

How do I visualize SQL analytics data in a Streamlit dashboard?▼

To visualize SQL analytics data in a Streamlit dashboard, you can connect your MySQL database, execute predefined analytical queries, and render the results using interactive Plotly charts.

Can I normalize nested JSON API responses into relational SQL tables using pandas?▼

Yes, you can normalize nested JSON API responses into relational SQL tables using pandas by transforming the flattened data structures and loading them into a multi-table schema with proper foreign keys.

What Python dependencies do I need for API integration and SQL analytics?▼

For API integration and SQL analytics, you need the Python dependencies requests, pandas, mysql-connector-python, plotly, and python-dotenv to manage data fetching, processing, database connection, and visualization.

Does this data engineering pipeline include predefined analytical queries for art collection data?▼

Yes, this data engineering pipeline includes over 20 predefined SQL analytical queries designed to extract insights from the processed Harvard Art Museums art collection data.

What is the best way to analyze Harvard Art Museums data end-to-end?▼

The best way to analyze Harvard Art Museums data end-to-end is to use a pipeline that integrates API data extraction, ETL processing into a relational database, and interactive Streamlit visualization.

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