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harvard-artifacts-data-engineering-analytics

Extract Harvard Art Museums data into a SQL database and visualize with Streamlit.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, pandas, sqlalchemy, streamlit, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables users to build comprehensive data engineering pipelines and interactive analytics dashboards for the Harvard Art Museums API.

Core Features & Use Cases

  • ETL Pipeline Construction: Automate the extraction, transformation, and loading of museum artifact data.
  • SQL Database Integration: Design and execute SQL queries for detailed analysis of artifact metadata.
  • Interactive Visualization: Create Streamlit-based dashboards with visualizations for easy data exploration.
  • Use Case: With this Skill, a data engineer can quickly set up a complete pipeline that fetches data from the Harvard Art Museums API, transforms it into a SQL database, and visualizes key metrics on a dashboard.

Quick Start

Clone the repository, set up the environment variables, and run the ETL pipeline using the command: pip install -r requirements.txt && ./run-etl.sh.

Frequently Asked Questions about harvard-artifacts-data-engineering-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?▼

To build an ETL pipeline for the Harvard Art Museums API, you can use this Skill to automate data extraction, transform it into a relational format, and load it into a SQL database using Python libraries like pandas and SQLAlchemy.

What do I need to visualize SQL data with Streamlit?▼

To visualize SQL data with Streamlit, you need a Python environment with Streamlit and SQLAlchemy installed, along with access to a MySQL or TiDB Cloud database where your transformed artifact data is stored for interactive dashboarding.

Can I use pandas and SQLAlchemy to load museum artifact data into a SQL database?▼

Yes, you can use pandas and SQLAlchemy to load museum artifact data into a SQL database, transforming raw API responses into structured relational tables for detailed SQL queries and analysis.

Does this ETL pipeline require a specific database setup?▼

The ETL pipeline requires MySQL or TiDB Cloud access to store the transformed data, along with a valid Harvard Art Museums API key to authenticate and extract the source artifact records.

How do I run the Harvard Art Museums data engineering pipeline?▼

You run the Harvard Art Museums data engineering pipeline by cloning the repository, installing the required Python dependencies with pip, setting up environment variables for API access, and executing the provided run-etl shell script.

What is the best way to analyze Harvard Art Museums artifacts?▼

The best way to analyze Harvard Art Museums artifacts is to extract records via their API, transform the metadata into a relational SQL database, and build interactive Streamlit dashboards to explore key metrics visually.

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