数据与研究

harvard-art-museum-etl-analytics

reason-machines/data-skills

Build end-to-end data engineering pipelines with Harvard Art Museums API, ETL workflows, SQL analytics, and Streamlit visualization.

  • 2013 次安装
  • 5 星标
  • 收录于

安装命令

npx skills add https://github.com/reason-machines/data-skills --skill harvard-art-museum-etl-analytics

SKILL.md

Harvard Art Museum ETL Analytics Skill

Skill by ara.so — Data Skills collection.

This skill enables you to build end-to-end data engineering and analytics applications using the Harvard Art Museums API. The project demonstrates real-world ETL pipelines, SQL database design, analytical queries, and interactive Streamlit dashboards for artifact collections.

What This Project Does

The Harvard-Artifacts-Collection-Data-Engineering-Analytics-App provides:

  • API Integration: Fetch artifact data from Harvard Art Museums API with pagination and rate limiting
  • ETL Pipeline: Extract, transform, and load artifact metadata, media, and color data into relational databases
  • SQL Analytics: Pre-built analytical queries for insights on culture, century, media, colors, and departments
  • Visualization: Interactive Plotly charts and Streamlit dashboards for data exploration
  • Database Design: Properly structured relational schema with foreign key relationships

Architecture flow: API → ETL → SQL → Analytics → Visualization

Installation

Harvard Art Museum ETL Analytics Skill

Skill by ara.so — Data Skills collection.

This skill enables you to build end-to-end data engineering and analytics applications using the Harvard Art Museums API. The project demonstrates real-world ETL pipelines, SQL database design, analytical queries, and interactive Streamlit dashboards for artifact collections.

What This Project Does

The Harvard-Artifacts-Collection-Data-Engineering-Analytics-App provides:

  • API Integration: Fetch artifact data from Harvard Art Museums API with pagination and rate limiting
  • ETL Pipeline: Extract, transform, and load artifact metadata, media, and color data into relational databases
  • SQL Analytics: Pre-built analytical queries for insights on culture, century, media, colors, and departments
  • Visualization: Interactive Plotly charts and Streamlit dashboards for data exploration
  • Database Design: Properly structured relational schema with foreign key relationships

Architecture flow: API → ETL → SQL → Analytics → Visualization

Installation