Four Cheap Quality Checks

Concepts covered: paFourQualityChecks, paFreshnessCheck

Quality engineering has a 90/10 rule. Roughly ninety percent of silent failures are caught by ten percent of the possible checks. The four cheap checks below cover that ninety percent. They run in seconds, they need only basic SQL, and they catch the most common production incidents. The point of starting with these four is that any of them is better than none, and arguments about more sophisticated checks are arguments about edge cases until the basics are in place. The four checks are also the easiest to explain to a non-technical stakeholder. A finance partner can understand 'the row count was outside its band'; the same partner cannot reasonably be asked to interpret a Kolmogorov-Smirnov statistic. Explainability is not a courtesy; it is what allows the alert to translate into a correc

About This Interactive Section

This section is part of the Data Quality and Contracts: Beginner 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.