Update: My understanding of SQL and NoSQL in the context of BigQuery
In Google BigQuery, the distinction between SQL and NoSQL does not apply in the same way it does for traditional database systems. BigQuery is fundamentally a fully managed, serverless, and highly scalable data warehouse with an SQL interface for querying data. All tables in BigQuery are accessed using SQL queries. However, the structure of the data within these tables can vary, allowing you to work with data that is more typical of NoSQL databases (like nested and repeated fields, and semi-structured data).
For example related_topics and embedding are of mode REPEATED which indicates that we can work with the data in a NoSQL way.
Update: My understanding of SQL and NoSQL in the context of BigQuery
In Google BigQuery, the distinction between SQL and NoSQL does not apply in the same way it does for traditional database systems. BigQuery is fundamentally a fully managed, serverless, and highly scalable data warehouse with an SQL interface for querying data. All tables in BigQuery are accessed using SQL queries. However, the structure of the data within these tables can vary, allowing you to work with data that is more typical of NoSQL databases (like nested and repeated fields, and semi-structured data).
For example
related_topicsandembeddingare of mode REPEATED which indicates that we can work with the data in a NoSQL way.