Google Cloud Professional Data Engineer Practice Exam 2025 – Complete Preparation

Using star or snowflake schemas is recommended for data modeling in Google BigQuery because these schemas are designed to optimize query performance and facilitate easier data analysis.

Star schemas consist of a central fact table that connects to several dimension tables. This design minimizes the number of joins needed in queries, thereby enhancing performance, particularly for analytical workloads. Dimension tables contain attributes related to the fact data, allowing users to easily slice and dice the data based on various criteria.

Snowflake schemas refine the star schema by normalizing dimension tables into multiple related tables. While this can introduce a slightly higher complexity in query writing, it can lead to more efficient storage and easier maintenance of dimension data. In scenarios involving large datasets or where differing dimensions apply, snowflake schemas can provide a more flexible approach.

These modeling techniques align well with BigQuery's ability to handle large-scale data analytics efficiently, leveraging its powerful query engine to optimize performance on complex analytical queries.

In contrast, while flat tables may seem straightforward, they can lead to data redundancy and performance issues, especially as data volumes grow. Using only unstructured data limits the ability to leverage BigQuery's full capabilities, which shines with structured or semi-structured data that can be effectively queried. Relying solely on one table can