How can one optimize queries in BigQuery for better performance?

To optimize queries in BigQuery for better performance, using partitioned tables and efficient filtering is essential.

Partitioned tables allow data to be divided into segments based on a specified key, such as date or region. This segmentation helps by enabling the query engine to read only the relevant partitions instead of scanning the entire dataset. For example, if a query filters based on a timestamp, and the table is partitioned by date, BigQuery can skip irrelevant partitions, significantly reducing the amount of data processed and enhancing performance.

Efficient filtering complements this by ensuring that queries only retrieve the necessary rows. By using conditions that target specific subsets of data, such as dates or categorical variables, you can further reduce the overhead associated with data scanning. This practice not only speeds up query execution but also reduces costs associated with querying larger datasets.

In contrast, using more complex SQL syntax may complicate queries without necessarily improving performance. Maximizing the amount of data scanned typically leads to slower queries and increased costs, rather than optimization. Limiting data access to certain users can enhance security and governance but does not inherently affect the performance of the queries themselves.

Therefore, focusing on partitioned tables and efficient filtering is the most effective strategy for optimizing query performance in BigQuery.