数据与研究

harvard-art-museums-data-pipeline

reason-machines/data-skills

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

  • 2029 次安装
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安装命令

npx skills add https://github.com/reason-machines/data-skills --skill harvard-art-museums-data-pipeline

SKILL.md

Harvard Art Museums Data Pipeline

Skill by ara.so — Data Skills collection.

This skill enables you to build production-ready data engineering pipelines using the Harvard Art Museums API. It covers ETL workflows, relational database design, SQL analytics, and interactive Streamlit dashboards for artifact data visualization.

What This Project Does

The Harvard-Artifacts-Collection-Data-Engineering-Analytics-App demonstrates a complete data pipeline:

  1. Extract: Fetches artifact data from Harvard Art Museums API with pagination and rate limiting
  2. Transform: Converts nested JSON into normalized relational tables
  3. Load: Batch inserts data into MySQL/TiDB Cloud databases
  4. Analyze: Executes analytical SQL queries for insights
  5. Visualize: Renders interactive dashboards with Plotly and Streamlit

The architecture follows: API → ETL → SQL → Analytics → Visualization

Installation

Harvard Art Museums Data Pipeline

Skill by ara.so — Data Skills collection.

This skill enables you to build production-ready data engineering pipelines using the Harvard Art Museums API. It covers ETL workflows, relational database design, SQL analytics, and interactive Streamlit dashboards for artifact data visualization.

What This Project Does

The Harvard-Artifacts-Collection-Data-Engineering-Analytics-App demonstrates a complete data pipeline:

  1. Extract: Fetches artifact data from Harvard Art Museums API with pagination and rate limiting
  2. Transform: Converts nested JSON into normalized relational tables
  3. Load: Batch inserts data into MySQL/TiDB Cloud databases
  4. Analyze: Executes analytical SQL queries for insights
  5. Visualize: Renders interactive dashboards with Plotly and Streamlit

The architecture follows: API → ETL → SQL → Analytics → Visualization

Installation