Warn vs Block Authorities

Concepts covered: paWarnVsBlock, paAlertSeverity

Not every quality check should stop the pipeline. Some failures are catastrophic and demand a halt; others are advisory and demand a notification. Treating every check as a blocker creates an over-protective pipeline that halts on minor anomalies and wakes engineers up at 3am for problems that could have waited. Treating every check as a warning creates a pipeline that ignores its own alarms. The classification is per-check, not per-pipeline, and the rule is simple: block when running is worse than not running, warn when running is better than not running but still imperfect. The classification has to be made at the time the check is authored, not later. A check that ships as 'temporarily a warning until the team gets used to it' tends to stay a warning forever, because nothing forces the

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.

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