Schema Validation Basics

Concepts covered: paSchemaValidation, paSchemaDrift

Schema validation is the second-most-common failure mode after row count, and it is the easiest to express. A schema check asserts that every row in a table conforms to a declared shape: column names exist, types match, nullability matches, and values fall in declared ranges. A row that fails a schema check is, by definition, a row the pipeline should not have produced. Schema validation is also where the conversation with the producer starts, because the schema is the producer's commitment to the consumer. The conversation is uncomfortable when it happens for the first time after a production incident; it is much easier when it happens at integration time, before any consumer has built anything against the data. The discipline of writing the schema check first surfaces every ambiguity in

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.

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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.