The Nesting Decision

When to Nest vs When to Normalize The fundamental question: should related data live inside the parent row (nested) or in a separate table (normalized)? Both are valid. The choice depends on how the data is queried, how often it changes, and whether the nested data needs to be joined to independently. Nesting works well when the child data is always read alongside the parent. A user's shipping addresses are almost always retrieved when loading the user record. Normalizing this into a separate addresses table forces a JOIN on every user load. Nesting the addresses as an ARRAY<STRUCT> inside the user row eliminates that JOIN. Decision Framework A common mistake: nesting everything because the source data is JSON. Just because the API returns nested JSON does not mean your analytical table sh

About This Interactive Section

This section is part of the Nested Data 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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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.