Google Associate Cloud EngineerPlanning and configuring a cloud solutionMedium
A data analytics team needs to process large datasets (tens of terabytes) using SQL queries for business intelligence reporting. The data is structured and requires transactional consistency, but the primary use case is analytical querying, not high-volume transactional writes. They need a fully managed, serverless service that can scale to petabytes if needed. Which Google Cloud data storage option is most suitable?
- ACloud SQL
- BBigQuery
- CCloud Spanner
- DCloud Bigtable
Show answer & explanationAnswer & explanation
Correct answer: B. BigQuery
BigQuery is a fully managed, serverless data warehouse designed for petabyte-scale analytics using SQL. It is optimized for large-scale analytical queries and is ideal for business intelligence reporting on structured data, offering high scalability and transactional consistency where needed for batch loads.
Why the other options are wrong
- A. Cloud SQL is a relational database for transactional workloads, typically up to a few terabytes, and is not optimized for petabyte-scale analytical queries.
- C. Cloud Spanner is a globally distributed, strongly consistent relational database, overkill and more expensive for purely analytical, non-transactional reporting on structured data.
- D. Cloud Bigtable is a NoSQL wide-column store for high-throughput, low-latency reads/writes, often used for operational analytics, not primarily for SQL-based business intelligence on structured data.
BigQuery
A serverless, highly scalable, and cost-effective enterprise data warehouse designed for petabyte-scale analytics. It allows users to run SQL queries on massive datasets.
- Fully managed and serverless
- Petabyte-scale analytics
- SQL query engine
- Optimized for analytical workloads (OLAP)
- Separates compute and storage
Memory trick: BigQuery is the big brain for big data questions.