Orphan Attributes with No Natural Home

Concepts covered: dmFactTables

The Problem Set up the scenario: 'The fact table has is_gift, is_prime, is_expedited, payment_method, and invoice_number. Where do these go? Leaving all five on the fact adds columns that every scan reads even when unused. Creating a dimension for each one is dimension explosion. The answer: consolidate the flags into a junk dimension, keep invoice_number as a degenerate dimension.' Deliver this in 15 seconds. It shows you know both patterns. What They're Really Testing These patterns are rarely asked as standalone questions. They come up when you are designing a fact table and the interviewer watches what you do with the leftover attributes. Having the vocabulary ready is the difference between fumbling and flowing.

About This Interactive Section

This section is part of the Junk and Degenerate Dimensions: Advanced 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 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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DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.