A normalized schema organizes information into smaller, focused tables, each centered on one subject or entity. Separating repeated details reduces redundancy and improves data integrity, scalability, performance, and maintenance. In practice, the required predictors and target may be scattered across warehouse tables, files, or data streams, so analysis begins by assembling them. section 2.3.1
A typical warehouse dataset may combine:
- Event tables record business events at particular moments, such as orders, banking transactions, medical visits, or clicks. section 2.3.1
- Item tables describe products or event details, including descriptions, prices, stock levels, or prescriptions. section 2.3.1
- Dimension tables add descriptive context, such as a person’s location or a product’s category; changing values require care. section 2.3.1
SELECT df1.product_id, df1.product_name, df1.price, df2.product_description, df2.category, df3.manufacturer, df3.weight FROM df1 JOIN df2 ON df1.product_id = df2.product_id JOIN df3 ON df1.product_id = df3.product_id;
Join product tables on product_id to assemble names, prices, descriptions, categories, manufacturers, and weights into an analysis-ready view. section 2.3.1
