The FLOAT Money Bug

FLOAT and DOUBLE store numbers in binary scientific notation. They cannot represent 0.1 exactly, just like 1/3 cannot be represented exactly in decimal. This is not a bug. It is how IEEE 754 floating-point works. The problem is that financial calculations accumulate these tiny errors across millions of rows until someone notices that the monthly revenue report is off by $4,700. The $0.01 rounding error does not matter on one row. On 50 million payment transactions per month, it compounds to thousands of dollars that cannot be reconciled with the bank statement. When FLOAT Is Actually Fine FLOAT is not universally bad. It is the wrong choice for money, but the right choice for sensor readings, GPS coordinates, scientific measurements, and ML feature values where approximate representation i

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.