Google Cloud Professional Data Engineer Exam 2025 - Free Practice Questions and Study Guide

Using BigQuery is more appropriate for handling large volumes of analytical queries due to its architecture designed for massive data analysis. BigQuery utilizes a columnar storage layout and distributed computing, enabling it to efficiently execute complex SQL queries over large datasets. This design allows for quick performance on analytical workloads, which often involve aggregations and filtering across vast amounts of data.

In contrast, options like transactional queries are better suited for a relational database like Cloud SQL, which is optimized for handling ACID transactions and row-based storage. Similarly, small datasets do not benefit from BigQuery's ability to scale and perform efficiently, as smaller datasets can be handled easily by Cloud SQL without incurring the overhead of using BigQuery. Real-time data streaming also typically aligns better with specialized services like Cloud Pub/Sub for ingestion and processes like Cloud Dataflow for real-time analytics, rather than bulk analytical queries which BigQuery excels at.

In essence, the primary strength of BigQuery lies in its capacity to process and analyze large datasets swiftly, making it the ideal choice when facing high volumes of analytical queries.