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

BigQuery’s columnar storage significantly enhances performance by enabling the system to read only the specific columns required for a given query. This approach contrasts with traditional row-based storage systems that must scan entire rows, regardless of whether all the data contained in those rows is needed for the query. By focusing only on the necessary columns, BigQuery reduces the amount of data processed and speeds up query execution. This optimization minimizes I/O operations, resulting in faster response times and more efficient use of resources.

In addition to this primary benefit, the columnar storage architecture supports effective data compression, which helps to save storage space, although this is not the main aspect of performance improvement being highlighted in this context. The simultaneous storage of multiple formats does not directly correlate with performance efficiency in terms of query execution either. Additionally, while indexing can improve query performance, BigQuery utilizes a different mechanism to optimize performance rather than relying on traditional automatic indexes as might be expected in other database systems. Thus, the emphasis on reading only necessary columns is a key factor that leads to the enhanced performance experienced with BigQuery.