This is a complete ETL pipeline tutorial for pulling artifact data from Harvard Art Museums' API into MySQL, then visualizing it with Streamlit. It handles the full workflow: pagination and rate limiting on API calls, transforming nested JSON into three relational tables (metadata, media, colors), and running 20+ analytical queries with Plotly charts. The code examples are production-ready with proper error handling and batch inserts. You'd use this as a reference implementation if you're building similar museum or cultural data pipelines, or if you need a working example of API-to-database workflows with visualization. It's batteries-included with schema definitions, environment config, and transform functions that handle real-world messiness like field length limits.
npx -y skills add aradotso/data-skills --skill harvard-artifacts-collection-data-engineering-analytics --agent claude-codeInstalls into .claude/skills of the current project.
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