Google BigQuery Execute Custom Query activity¶
Introduction¶
A Google BigQuery Execute Custom Query activity, using its Google BigQuery connection, executes custom queries against tables in datasets in Google BigQuery and is intended to be used as a source in an operation.
Create a Google BigQuery Execute Custom Query activity¶
An instance of a Google BigQuery Execute Custom Query activity is created from a Google BigQuery connection using its Execute Custom Query activity type.
To create an instance of an activity, drag the activity type to the design canvas or copy the activity type and paste it on the design canvas. For details, see Create an activity instance in Component reuse.
An existing Google BigQuery Execute Custom Query activity can be edited from these locations:
- The design canvas (see Component actions menu in Design canvas).
- The project pane's Components tab (see Component actions menu in Project pane Components tab).
Configure a Google BigQuery Execute Custom Query activity¶
Follow these steps to configure a Google BigQuery Execute Custom Query activity:
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Step 1: Enter a name and SQL query statement
Provide a name for the activity and specify the SQL query statement. -
Step 2: Review the data schemas
Any request or response schemas are displayed.
Step 1: Enter a name and SQL query statement¶
In this step, provide a name for the activity and specify the SQL query statement. Each user interface element of this step is described below.
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Name: Enter a name to identify the activity. The name must be unique for each Google BigQuery Execute Custom Query activity and must not contain forward slashes
/
or colons:
. -
SQL Query: Enter a SQL query statement. Query statements require valid GoogleSQL syntax.
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Save & Exit: If enabled, click to save the configuration for this step and close the activity configuration.
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Next: Click to temporarily store the configuration for this step and continue to the next step. The configuration will not be saved until you click the Finished button on the last step.
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Discard Changes: After making changes, click to close the configuration without saving changes made to any step. A message asks you to confirm that you want to discard changes.
Step 2: Review the data schemas¶
Any request or response schemas are displayed. Each user interface element of this step is described below.
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Data Schemas: These data schemas are inherited by adjacent transformations and are displayed again during transformation mapping.
The Google BigQuery connector uses the Google SDK version 25.4.0. Refer to the API documentation for information on the schema nodes and fields.
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Refresh: Click the refresh icon or the word Refresh to regenerate schemas from the Google BigQuery endpoint. This action also regenerates a schema in other locations throughout the project where the same schema is referenced, such as in an adjacent transformation.
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Back: Click to temporarily store the configuration for this step and return to the previous step.
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Finished: Click to save the configuration for all steps and close the activity configuration.
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Discard Changes: After making changes, click to close the configuration without saving changes made to any step. A message asks you to confirm that you want to discard changes.
Next steps¶
After configuring a Google BigQuery Execute Custom Query activity, complete the configuration of the operation by adding and configuring other activities, transformations, or scripts as operation steps. You can also configure the operation settings, which include the ability to chain operations together that are in the same or different workflows.
Menu actions for an activity are accessible from the project pane and the design canvas. For details, see Activity actions menu in Connector basics.
Google BigQuery Execute Custom Query activities can be used as a source with these operation patterns:
- Transformation pattern
- Two-target archive pattern (as the first source only)
- Two-target HTTP archive pattern (as the first source only)
- Two-transformation pattern (as the first source only)
To use the activity with scripting functions, write the data to a temporary location and then use that temporary location in the scripting function.
When ready, deploy and run the operation and validate behavior by checking the operation logs.