Complex Patterns

Concepts covered: dmJunctionTables

The Many-to-Many Fan-Out Problem When you join through a many-to-many relationship, your row count explodes. If a product belongs to 3 categories and has 10 orders, joining products to categories to orders produces 30 rows instead of 10. This is the fan-out trap, and it silently inflates every SUM, COUNT, and AVG downstream. The fix is to pre-aggregate before joining, or to use a bridge table with weighting factors that sum to 1.0 per entity. This is covered in depth in the Bridge Tables lesson. Ternary Relationships Some relationships involve three entities simultaneously. A supplier provides a specific part to a specific project. The relationship is not between any two entities alone. It is between all three at once. Modeling this requires a three-column junction table. The junction tabl

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This section is part of the Relationships lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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