Regional Revenue with GROUP BY SUM and AVG

What the solution is:

This SQL query combines standard aggregate functions (SUM and AVG) with a GROUP BY clause in GoogleSQL to compute descriptive statistics categorized across distinct attribute values.

How it meets the requirements:

• Total revenue calculation (✓): The SUM(order_amount) function aggregates the numerical values within order_amount by adding them together for each group, producing the total monetary revenue.
• Central tendency calculation (✓): The AVG(order_amount) function calculates the arithmetic mean of order_amount for each group, determining the average transaction size.
• Regional segmentation (✓): The GROUP BY region clause partitions the rows into subsets based on unique values in the region column, ensuring both summary metrics are computed independently per region.

Key advantages and capabilities:

• Native BigQuery optimization: Standard aggregate functions like SUM and AVG run efficiently across distributed data partitions in BigQuery.
• Deterministic segmentation: Grouping by categorical dimensions allows multidimensional slicing of large transactional tables into compact summary records.
• Direct business answers: Producing both aggregate sum totals and central tendencies provides immediate insight into regional volume versus individual purchasing behavior.

Why this is optimal:

In standard SQL syntax used by BigQuery, whenever non-aggregated columns such as region appear in the SELECT list alongside aggregation functions like SUM() and AVG(), they must be specified in the GROUP BY clause. This query adheres strictly to standard SQL semantics while utilizing the correct, built-in aggregation functions.