Distributional Checks

Concepts covered: paDistributionalCheck, paStatisticalQuality

Schema and row-level checks miss a class of failures where every individual row is structurally valid and the population has shifted. A column whose mean used to be 42.3 and is now 67.8 may signal a real change in the world, or a producer-side bug, or an upstream filter regression. None of the rows are individually wrong. The distribution is wrong. Distributional checks compare summary statistics of the current run against a historical baseline and fire when the comparison crosses a threshold. The class of failures these checks catch is exactly the class that escapes every other check. Row counts are normal; nulls are absent; uniqueness is preserved; schema is intact; freshness is on time. The only signal that anything is amiss is that the distribution of values has shifted in a way that t

About This Interactive Section

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