Core Problem & SQL Analysis
When reviewing SQL query logic, aggregations like SUM, AVG, and COUNT are the most frequent sources of silent data discrepancies. A query might run successfully and return a result, but the calculated numbers may not represent the intended business logic. To prevent these downstream errors, code reviewers must systematically verify the relationship between grouping granularity and joined dimensions.
The main issue usually stems from many-to-many or one-to-many relationships that multiply rows before the aggregation step occurs. For example, joining an order table with an order items table before calculating a sum of order amounts will duplicate order rows, leading to artificially inflated revenue metrics. To verify query intent, ask whether the COUNT is counting unique entities or raw event rows. Similarly, check if AVG is calculated across all records equally or needs to be weighted by group size.
Every aggregation must have a clearly defined physical meaning. Reviewing query execution plans and testing against small, known datasets helps identify these granularity mismatches before they impact production reports.
Why This Query Intent Fails
Often in production data environments, developers default to simple joins without reviewing the row multiplicity. When duplicate keys exist or table joins do not represent direct dependencies, metrics like revenue and active user count become inflated.
Key Takeaway
Always verify the primary keys of joined datasets and execute aggregate checks before using nested layouts. Improperly scoped aggregation levels will distort downstream BI dashboard reports.
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