What is the difference between sql_trigger and sql_trigger_value?

What is the difference between sql_trigger and sql_trigger_value?

• A. sql_trigger is for datagroups, sql_trigger_value is for PDTs
• B. sql_trigger is for calculations, sql_trigger_value is for aggregations
• C. sql_trigger is used in PDTs, sql_trigger_value is used in a datagroup
• D. They serve identical functions in different contexts

Answer: (C)

The option highlighting the distinction that sql_trigger is used in Persistent Derived Tables (PDTs), while sql_trigger_value is employed within a datagroup, is accurate. In Looker, sql_trigger serves a crucial role in defining when a PDT should be refreshed based on the result of a certain SQL query, which allows for dynamic updating of the table contents based on the underlying data. On the other hand, sql_trigger_value is utilized to manage the timing and logic behind the data refresh mechanisms in datagroups. It allows for the specification of a SQL query that indicates the last time data was updated. This functionality is essential for ensuring that the data integrity and freshness are maintained across your Looker models. Understanding the separate roles these commands play enhances a developer's ability to manage data effectively in Looker's ecosystem and ensures proper implementation of data refresh logic in both PDTs and datagroups.

The option highlighting the distinction that sql_trigger is used in Persistent Derived Tables (PDTs), while sql_trigger_value is employed within a datagroup, is accurate. In Looker, sql_trigger serves a crucial role in defining when a PDT should be refreshed based on the result of a certain SQL query, which allows for dynamic updating of the table contents based on the underlying data.

On the other hand, sql_trigger_value is utilized to manage the timing and logic behind the data refresh mechanisms in datagroups. It allows for the specification of a SQL query that indicates the last time data was updated. This functionality is essential for ensuring that the data integrity and freshness are maintained across your Looker models.

Understanding the separate roles these commands play enhances a developer's ability to manage data effectively in Looker's ecosystem and ensures proper implementation of data refresh logic in both PDTs and datagroups.