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Answer: BigQuery, because it is designed for large-scale processing of tabular data
The question specifies building an OLAP (Online Analytical Processing) marketing analytics and reporting tool that requires a relational database capable of handling hundreds of terabytes of data. BigQuery (option D) is the optimal choice because it is specifically designed for large-scale analytical processing (OLAP), supports SQL for relational queries, and can efficiently handle petabytes of data. Community discussion strongly supports D (94% consensus), with key points including: BigQuery is ideal for OLAP workloads, while Cloud SQL (B) and Cloud Spanner (A) are better suited for OLTP (Online Transaction Processing) and have lower storage limits (e.g., Cloud SQL max ~64 TB). Cloud Firestore (C) is a NoSQL database for real-time apps, not analytical processing. Although some comments debate BigQuery's relational nature, it supports SQL and joins, making it suitable for analytical relational queries in this context.
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You are building an online analytical processing (OLAP) marketing analytics and reporting tool that requires a relational database capable of operating on hundreds of terabytes of data. What is the Google-recommended solution for this use case?
A
Cloud Spanner, because it is globally distributed
B
Cloud SQL, because it is a fully managed relational database
C
Cloud Firestore, because it offers real-time synchronization across devices
D
BigQuery, because it is designed for large-scale processing of tabular data