Numeric Overflow and Precision Loss at Scale

Concepts covered: sqlDecimalType

Domain types are custom types that encode business invariants. Money is not a float; it's a precise decimal with a currency. An identifier is not an integer; it's a typed token that should not accidentally be added to another integer. Most warehouses don't support custom types directly, but the platform pattern is to enforce the invariants through wrapper functions, validation rules, and code conventions. Money handling The discipline: money columns are NUMERIC with explicit precision (not FLOAT); the currency is a separate column; cross-currency aggregates require a conversion step. The platform's reporting layer enforces these conventions; queries that SUM amounts across currencies without conversion are caught at code review or by dbt tests. State this when designing: 'every money colum

About This Interactive Section

This section is part of the Type Casting: 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.

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.