harvard-artifacts-etl-analytics

To build an ETL pipeline for the Harvard Art Museums API, this Skill automates data extraction via requests, transforms it using pandas, and loads the structured datasets into a MySQL database. It provides predefined scripts to handle the entire extraction and transformation workflow.

Yes, you can visualize SQL database data from the Harvard Art Museums API using Streamlit. This Skill provides predefined SQL queries to extract relational data, which is then rendered into interactive analytics dashboards using Streamlit and Plotly for actionable insights.

Yes, MySQL is required to load and analyze the extracted Harvard Art Museums data. The ETL pipeline transforms the raw API responses into a relational database format, relying on the mysql-connector-python dependency to establish the necessary database connection for SQL queries.

The best way to analyze large datasets of museum artifacts is to use an automated ETL pipeline with pandas for data transformation and Streamlit for visualization. This approach extracts raw API data, structures it in MySQL, and enables interactive analysis without manual processing.

Data transformation from the Harvard Art Museums API works by extracting raw JSON responses with requests, reshaping the collections data using pandas, and mapping the fields into a structured relational database format. This process prepares the unstructured API data for SQL loading.

When using Plotly and Streamlit for ETL pipeline data visualization, a limitation is the prerequisite environment setup. Users must configure a local Python environment with specific dependencies and manage a concurrent MySQL database connection to render the interactive analytics dashboards correctly.