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

The multi-tier architecture using Cloud Storage and BigQuery is the most suitable approach for building data lakes on Google Cloud due to several reasons.

Firstly, Cloud Storage serves as an ideal foundational layer for a data lake because it allows the storage of vast amounts of unstructured and semi-structured data at an economical cost. It is built to handle a variety of data types, such as log files, images, videos, and more, which aligns perfectly with the needs of a data lake.

Secondly, BigQuery acts as a powerful analytics layer for processing and querying that data stored in Cloud Storage. It provides scalable, pay-as-you-go analytics capabilities, allowing data engineers and analysts to run complex queries and generate insights without worrying about the underlying infrastructure. This separation of storage and compute resources enhances flexibility and efficiency, enabling the organization to optimize costs based on usage.

In summary, leveraging a multi-tier architecture that combines Cloud Storage for data lake storage and BigQuery for analytics streamlines the process of storing, managing, and analyzing large datasets, which is essential for effective data operations in a cloud environment. This approach not only aligns with best practices in data architecture but also maximizes the capabilities offered by Google Cloud services.