data-analytics-sql-reviewlisted

# Analytical SQL review ## Purpose Analytical SQL fails quietly. A join fans out and the revenue total doubles; an inner join drops the rows with no matching dimension and the count is 4% low; a filter in the WHERE clause turns a left join into an inner one. None of these raise an error, and all of them produce a number someone will act on. This skill is the ordered checklist for those failure classes, plus the cost review that stops a correct query from being unaffordable. ## Prerequisites - **Inputs:** the query; the grain each source table is at (one row per what?); expected row counts or an order of magnitude for the result; the metric definition it implements. - **Access:** ability to run the query's plan and row counts against a representative dataset. Reviewing SQL by reading alone catches syntax and obvious logic errors, but not fan-out — that needs counts. If nobody can state the grain of each source table, establish that first. Almost every fan-out and row-loss bug is a grain misunderstanding, and no amount of reading the SQL surfaces it. ## Procedure ### Pass 1 — Correctness 1. **State the grain of every source and the intended grain of the output.** Write it down as a sentence per table: "one row per order line", "one row per customer per day". Then check every join: joining a one-row-per-customer table to a one-row-per-order table produces one row per order — if an aggregate downstream sums a customer-level column, it is now multiplied