A data warehouse team uses BigQuery and has noticed that their monthly compute costs are highly variable due to fluctuating query volume. They want…

The core requirements are to stabilize highly variable costs and gain predictable query capacity for a high-usage data warehouse. BigQuery's default on-demand pricing model charges per terabyte of data processed, which directly causes cost variability with fluctuating query volume. Switching to the flat-rate pricing model decouples compute costs from the amount of data scanned. By purchasing a dedicated capacity of "slots" (units of computational power), the organization pays a fixed, predictable price. An annual commitment provides a significant discount on this flat-rate price, making it the most effective solution for achieving cost stability and predictable performance for sustained, high-volume workloads.

A. Migrating from BigQuery, an analytical data warehouse, to Cloud SQL, a transactional RDBMS, is architecturally incorrect for this use case and would lead to severe performance degradation for analytical queries.

B. Table partitioning and clustering are important cost-optimization techniques that reduce data scanned per query. However, they do not change the on-demand pricing model, so costs would still be variable.

C. Scheduled queries only automate the execution of queries; they do not change the underlying pricing model or the amount of data processed, so costs would remain variable.

1. Google Cloud Documentation, "Introduction to BigQuery reservations": This document explicitly contrasts the two pricing models. It states, "Flat-rate pricing is best for customers who prefer a stable monthly cost for queries rather than paying the on-demand price per TB of data processed." This directly supports the choice of flat-rate pricing for cost stabilization.

2. Google Cloud Documentation, "Reservations commitments": This page details the benefits of commitments for flat-rate pricing. It notes, "A capacity commitment is a purchase of a fixed amount of BigQuery compute capacity for a fixed period of time... You can purchase commitments in 1-year or 3-year terms." This validates the "annual commitment" part of the answer for further cost savings.

3. Google Cloud Documentation, "Control BigQuery costs": In the section "Choose a pricing model," this guide advises, "For predictable workloads, consider using flat-rate pricing." This reinforces that flat-rate pricing is the recommended solution for achieving predictable costs, which is a key requirement in the question.

4. Google Cloud Documentation, "Cloud SQL" and "BigQuery" Product Pages: The official product pages clearly define Cloud SQL as a service for OLTP (Online Transaction Processing) workloads (MySQL, PostgreSQL, etc.) and BigQuery as a petabyte-scale data warehouse for OLAP (Online Analytical Processing). This distinction confirms that migrating a data warehouse to Cloud SQL (Option A) is an inappropriate architectural decision.