From the course: Data Warehousing on Google Cloud Platform

Overview of BigQuery SQL - Google Cloud Platform Tutorial

From the course: Data Warehousing on Google Cloud Platform

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Overview of BigQuery SQL

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- [Instructor] Let's discuss the SQL within BigQuery and how it compares to what we typically see in other relational database systems. BigQuery stores data by columns instead of by rows. The reason why is because it's designed to take large, complex queries that may process large amounts of data. The column storage helps BigQuery process live or streaming data. It also optimizes queries by caching previous query results and prevents reprocessing queries. This happens if the data or query hasn't changed. There are also two different SQL dialects. Google SQL is ANSI-compliant SQL that supports query statements, procedural, data definition language, data manipulation language, data control language, and transactional control language statements. Google SQL is the default dialect and supports the newest functionality available. Legacy SQL is the traditional SQL most RDBMS platforms use and is still available within BigQuery for backwards compatibility. In the Query settings, you can go to Advanced settings to change the dialect. However, you'll have to change the dialect every time you create a new query. As of June, 2024, the following data types are available in Google SQL.

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