Google Cloud Professional Data Engineer Exam 2026 - Free Practice Questions and Study Guide

The chosen answer, Google Cloud BigQuery, is specifically designed for storing and analyzing both structured and semi-structured data. BigQuery utilizes a columnar storage approach, which allows it to efficiently handle large-scale datasets. It supports SQL queries, making it accessible to users familiar with traditional database management. Additionally, BigQuery is built to handle complex analytical queries over large datasets quickly, which is a significant advantage for businesses and data analysts looking to derive insights from their data.

In the context of structured data, this typically refers to data that adheres to a defined schema, such as tables with rows and columns, while semi-structured data might include formats like JSON or Avro that do not have a fixed structure but still contain some organizational properties. BigQuery's ability to ingest and process such data formats makes it particularly versatile for a wide range of data analytics tasks.

The other options focus on different capabilities that do not center specifically on structured or semi-structured data. For instance, Google Cloud Pub/Sub is primarily a messaging service for event-driven architectures and real-time data streaming, lacking dedicated data storage capabilities. Google Cloud Spanner is a fully managed relational database that excels in transactional workloads and scalability but is not primarily designed for analytical querying and large-scale data processing