dedupe

DE//AGENT currently lists 2 resources under dedupe: 2 tutorials. Everything below is derived from the live registry — counts, capabilities, verification state and security posture come from the curated records, never from marketing copy.

Documented learning objectives across these resources:

• Recognize inflated row counts and double-counted metrics as the fan-out signature — via Incident — the numbers doubled and no alarm fired
• Count duplicates per key and correlate the first duplicate date with a deploy — via Incident — the numbers doubled and no alarm fired
• Rewrite affected partitions instead of the whole table — via Incident — the numbers doubled and no alarm fired
• Keep the result grain intact — a window computes over a set without collapsing rows — via Window functions — keep the grain, rank the rows
• Find duplicate keys with one GROUP BY query before deduplicating — via Window functions — keep the grain, rank the rows
• Deduplicate on the business key, keeping the latest by event time — via Window functions — keep the grain, rank the rows

These resources map to stage: Python + SQL (2). Follow the four-phase roadmap to place this topic inside a progression instead of learning it in isolation.

Every entry, its shape and its trust state — sorted by the registry's composite score. This table is the honest version of a marketing comparison: no sponsored placements, no unverifiable claims.

How scores are computed: source quality score.

Highest composite score first:

1. Incident — the numbers doubled and no alarm fired score 34.15 — intermediate
2. Window functions — keep the grain, rank the rows score 34.15 — intermediate

Scores rank provenance, completeness and trust — read how verification works before adopting anything.

• Incident — the numbers doubled and no alarm fired — Row counts are inflated and metrics double-counted after a retry, a fan-out join or a backfill — the incident signature of a silent fan-out. You work the duplicate-records runbook … details
• Window functions — keep the grain, rank the rows — A window function computes over a set of rows and hands the answer back to every row, so the result keeps its grain — the property that makes a keep-latest dedupe safe and a bare G… details

Adjacent topics: tutorial (2 shared), intermediate (2 shared).

Study path: the roadmap places this topic in a progression · the scoring model explains the numbers · the trust rule catalog shows every security check these entries passed through.