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Answer: Construct a table that combines book and author information, nesting author details within an author column.
Google recommends using nested and repeated fields to denormalize data storage, enhancing query performance. This approach is particularly effective for hierarchical relationships that are often queried together, such as parent-child relationships. Denormalization is a key strategy for improving read performance in previously normalized relational datasets. For more details, refer to [BigQuery best practices for nested data](https://cloud.google.com/bigquery/docs/best-practices-performance-nested).
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When migrating an application that tracks library books and their details (like author or publication year) from an on-premises data warehouse to BigQuery, how should you structure the data to optimize query speed about the authors of borrowed books, adhering to Google's schema design best practices?
A
Maintain the current schema with separate tables for books and their attributes, continuing to query as before.
B
Design a wide table with columns for each attribute, such as the author's first name, last name, and date of birth.
C
Construct a table that combines book and author information, nesting author details within an author column.
D
Retain the existing schema but create a view that joins all tables, and consistently query this view.
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