Correction and Reversal Patterns

Concepts covered: dmImmutableLogs

The interviewer asks: 'The source system sent incorrect data yesterday and just sent a correction. How does your model handle it?' They are testing whether you destructively update fact rows (no-hire signal) or use reversal patterns that preserve the audit trail (strong-hire signal). In financial data modeling interviews, this question is worth 20% of the scorecard. Why 'Just UPDATE It' Is a No-Hire Answer The Reversal Pattern: The Strong-Hire Answer Your reversal answer: 'Two new rows: a reversal that negates the original (-$1,000) and a correction with the right value ($1,100). The original row is untouched. SUM produces $1,100: correct. The audit trail shows exactly what happened and when.' Walk through the three rows on the whiteboard. The interviewer is checking whether you preserve t

About This Interactive Section

This section is part of the Late-Arriving Data: Advanced 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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