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A data analyst is tasked with generating a monthly revenue report by product category and geographic region from a multi-terabyte transactional dataset in BigQuery. The analyst needs to optimize the SQL query to minimize resource usage, reduce bytes scanned, and produce accurate summary aggregations.
Which SQL querying practice should the analyst apply to achieve efficient query execution and accurate reporting?
Execute a CROSS JOIN across all dimensions and use the HAVING clause to filter out mismatched records and unwanted time periods
Use SELECT * across all underlying tables and apply filtering conditions in a separate subquery wrapper after computing aggregations
Omit WHERE filtering clauses entirely and rely on a LIMIT clause to restrict the amount of data processed during aggregation
Specify only required columns in the SELECT list and apply selective filtering in the WHERE clause before aggregating with GROUP BY
Execute a CROSS JOIN across all dimensions and use the HAVING clause to filter out mismatched records and unwanted time periods
Use SELECT * across all underlying tables and apply filtering conditions in a separate subquery wrapper after computing aggregations
Omit WHERE filtering clauses entirely and rely on a LIMIT clause to restrict the amount of data processed during aggregation
Specify only required columns in the SELECT list and apply selective filtering in the WHERE clause before aggregating with GROUP BY
Column projection and predicate pushdown (early filtering) are foundational SQL optimization practices in columnar analytical databases like BigQuery. Column projection involves listing only specific needed attributes (such as region, category, and revenue) in the SELECT statement rather than querying all columns. Early filtering applies conditions within the WHERE clause to eliminate non-matching records prior to resource-intensive data processing operations such as joins and aggregations.
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StudyCertsWHERE reduces the row count before the data enters the GROUP BY execution stage, minimizing shuffle overhead and memory consumption.SUM(revenue)) reflect only the target time window and categories.Analytical query engines are engineered around columnar architectures. Selecting only specific dimensions and pruning rows early via WHERE conditions aligns with how the query optimizer schedules execution stages, outperforming queries that pass unfiltered row sets into memory-intensive aggregation steps.