harvard-art-museums-data-engineering-analytics

To build an ETL pipeline for the Harvard Art Museums API, you can use this Skill to automate fetching data with pagination, transform it using Python and pandas, and load the results into a SQL database. It manages rate limiting and database connections automatically.

Running this data engineering workflow requires Python, a MySQL or TiDB database for data storage, and the Streamlit framework for interactive analytics dashboards. You also need to provide your own Harvard Art Museums API key and database settings.

Yes, you can visualize SQL query results using Streamlit. This Skill executes predefined SQL queries against your loaded data and generates interactive dashboards using the Plotly library within the Streamlit environment.

Pagination for the Harvard Art Museums API is handled automatically by the Skill's Python scripts. It manages API requests with built-in rate limiting to fetch large datasets efficiently before transforming and loading them into your SQL database.

You can analyze Harvard Art Museums artifacts without writing manual SQL by using the predefined queries included in the Skill. These queries execute automatically to extract insights, which are then visualized through the Streamlit dashboard interface.

The best way to store this API data for analytics is to load it into a SQL database like MySQL or TiDB. This Skill automates the extraction, transformation, and loading process, enabling structured storage for efficient querying and visualization.