Schema Evolution

Concepts covered: paTableFormats

What They Want to Hear 'Schema changes are inevitable. My approach: adding a nullable column is always safe. Widening a type (int to long) is safe. Removing a column or narrowing a type is a breaking change that requires a migration plan with dual-write during the transition.' Then mention table formats: 'Iceberg and Delta handle safe schema evolution natively. For breaking changes, I use a dual-write period where both old and new schemas are produced simultaneously.'

About This Interactive Section

This section is part of the Where Data Lives: Intermediate 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.

How DataDriven Lessons Work

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.