ROW_NUMBER Deduplication

Concepts covered: sqlDistinct

Three design decisions this lesson covers First: write-time dedup. MERGE or INSERT ... ON CONFLICT patterns that dedupe as rows land in the target, so the target is always in a deduped state. Idempotency is the key property: rerunning the ingestion produces the same target rows, not duplicate target rows. Second: CDC-aware dedup. CDC streams emit the same row multiple times during failovers, retries, and reconnections; the dedup discipline catches the duplicates at the ingestion boundary and emits a metric so the platform team sees the volume. Third: data quality metrics. The dedup operation produces a count of removed duplicates; that count is itself a metric, alerted on when it drifts beyond historical norms. Why these decisions matter in production Real pipelines run repeatedly. A reing

About This Interactive Section

This section is part of the Deduplication: 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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