True or False: If you use datagroup_trigger for your derived table, you don't need to use the sql_trigger_value or persist_for parameters.

True or False: If you use datagroup_trigger for your derived table, you don't need to use the sql_trigger_value or persist_for parameters.

• A. True
• B. False
• C. Only for Explore parameters
• D. Only in complex queries

Answer: (A)

Using the datagroup_trigger for a derived table handles the triggering logic for when the derived table should be rebuilt automatically when the underlying data changes. When you specify a datagroup_trigger, Looker monitors the related tables for changes, and if a change is detected, it will refresh the derived table accordingly. Because the datagroup mechanism inherently manages the timing for the refresh of the derived table, there is no need to explicitly define the sql_trigger_value or persist_for parameters. These parameters are typically used for more manual control over when the derived table gets refreshed, but incorporating a datagroup_trigger provides an efficient way to ensure that the data remains current without additional configuration. In scenarios where you rely on a datagroup_trigger, the automation it provides eliminates the requirement for those other parameters, making it straightforward to manage the data freshness. This leads to a cleaner LookML definition and reduces room for errors since the derived table will now automatically handle refresh events based on the conditions defined by the datagroup.

Using the datagroup_trigger for a derived table handles the triggering logic for when the derived table should be rebuilt automatically when the underlying data changes. When you specify a datagroup_trigger, Looker monitors the related tables for changes, and if a change is detected, it will refresh the derived table accordingly.

Because the datagroup mechanism inherently manages the timing for the refresh of the derived table, there is no need to explicitly define the sql_trigger_value or persist_for parameters. These parameters are typically used for more manual control over when the derived table gets refreshed, but incorporating a datagroup_trigger provides an efficient way to ensure that the data remains current without additional configuration.

In scenarios where you rely on a datagroup_trigger, the automation it provides eliminates the requirement for those other parameters, making it straightforward to manage the data freshness. This leads to a cleaner LookML definition and reduces room for errors since the derived table will now automatically handle refresh events based on the conditions defined by the datagroup.