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

Enhancing query performance in BigQuery can be effectively achieved by using partitioning and clustering on tables. This approach organizes the data in a way that minimizes the amount of data scanned during query execution, leading to faster response times and reduced costs.

Partitioning involves dividing a table into smaller, more manageable segments, based on a specific column (often a date). When a query is executed, BigQuery can skip entire partitions that do not match the query criteria. This significantly reduces the volume of data processed and speeds up the performance of queries that filter on the partitioned column.

Clustering adds another layer of optimization by storing related data together, which means that when a query is run, BigQuery can more quickly locate the relevant rows. For example, if a table is clustered on a user ID, queries that filter by this ID will run more efficiently because the rows are stored sequentially.

This data organization results in a performance improvement, especially when querying large datasets, as it minimizes the amount of data that needs to be read and processed during query execution. Thus, partitioning and clustering is a powerful strategy for enhancing query performance in BigQuery.