An organization wants to analyze data in a data warehouse. How should they proceed?

Importing data into a semi-structured time-series database is optimized for time-ordered metrics and monitoring data, not for general-purpose data warehouse analytics.

A data warehouse like BigQuery is specifically designed to store structured and semi-structured data at scale and supports ad-hoc analytical queries using SQL. This architecture enables organizations to run exploratory and complex analytical workloads efficiently without predefined query patterns.

Copying unstructured data into an object store such as Cloud Storage creates a data lake, not a data warehouse, and does not support direct SQL-based ad-hoc analysis.

Storing data in structured tables for transactional queries describes a relational OLTP database such as Cloud SQL, which is optimized for high-frequency transactional operations rather than large-scale analytical queries.