Your company wants to be able to retrieve large result sets of medical information from your current system, which has over 10 TBs in the database, and store the data in new tables for further…

B is correct. BigQuery is Google Cloud's fully managed, serverless data warehouse built specifically for large-scale analytics - it handles 10+ TB datasets efficiently, requires virtually no infrastructure maintenance, is queried via standard SQL, and supports writing query…

Submitted by salim_om· Mar 30, 2026Designing data processing systems

Question

Your company wants to be able to retrieve large result sets of medical information from your current system, which has over 10 TBs in the database, and store the data in new tables for further query. The database must have a low-maintenance architecture and be accessible via SQL. You need to implement a cost-effective solution that can support data analytics for large result sets. What should you do?

Options

AUse Cloud SQL, but first organize the data into tables. Use JOIN in queries to retrieve data.
BUse BigQuery as a data warehouse. Set output destinations for caching large queries.

Explanation

B is correct. BigQuery is Google Cloud's fully managed, serverless data warehouse built specifically for large-scale analytics - it handles 10+ TB datasets efficiently, requires virtually no infrastructure maintenance, is queried via standard SQL, and supports writing query results to destination tables (which directly addresses the "store data in new tables for further query" requirement). Setting output destinations allows BigQuery to handle results that exceed in-memory limits, making it ideal for large result sets.

Why A is wrong: Cloud SQL is a transactional (OLTP) relational database service designed for operational workloads like web apps, not analytics. It does not scale cost-effectively or performantly to 10+ TB analytics workloads, and heavy JOINs on that volume would be slow and expensive - it's simply the wrong tool for a data warehouse use case.

Memory tip: Think of the acronym BAD - BigQuery = Analytics + Data warehouse. Whenever an exam question mentions large-scale analytics, petabyte-scale data, low-maintenance, or SQL-based data warehousing on GCP, BigQuery is almost always the answer. Cloud SQL = small/medium transactional databases; BigQuery = massive analytical queries.

Topics

#BigQuery#Data Warehousing#Scalable Analytics#Cost Optimization

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