SELECT
product_name,
SUM(monthly_sales) AS total_monthly_sales,
RANK() OVER (PARTITION BY category ORDER BY SUM(monthly_sales) DESC) AS sales_rank
FROM
`sales_data`
WHERE
EXTRACT(YEAR FROM sale_date) = 2023
GROUP BY
product_name, category, EXTRACT(MONTH FROM sale_date)But it’s still a far cry from being able to turn something like “/* give me a list of products by category, ranked by monthly sales in 2023 */” into a working query that does what the user wants.
Minimizing friction in day to day tasks
Robert Kramer, principal analyst at Moor Insights and Strategy, said those working with data tend to think in terms of questions and outcomes, not syntax. “Translating intent into accurate and efficient SQL still takes time, especially with joins, time logic, and repetitive patterns. By allowing natural language expressions inside SQL comments, Google is trying to speed up that translation while keeping SQL as the execution layer,” he said.
With the new feature, teams could spend more time interpreting results and less time writing and rewriting queries, creating more automated analytics processes down the road while speeding up insights, minimizing team handoffs, and saving time on query setup, he added.
