Type Review Framework

When reviewing a schema, apply the same checklist to every column. This catches type bugs before they reach production. The cost of fixing a type in design is a one-line change. The cost of fixing a type in production is a migration, a backfill, and a data reconciliation. The Checklist Common Type Smells The Operational Cost of Getting It Wrong Type changes on large tables are expensive. Changing a column from INT to BIGINT on a 1-billion-row table in PostgreSQL requires a full table rewrite. In Snowflake, type changes are metadata-only for some types (VARCHAR widening) but require a rewrite for others (INT to BIGINT). Always check your platform's ALTER TABLE behavior before declaring a type in production.

About This Interactive Section

This section is part of the Schema Types 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.