BigQuery target models

To configure a target model that will be written to BigQuery, reference the following sections.

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Type & Format

BigQuery supports the following materialization types for target models. The type determines the underlying physical format of your target model.

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Location

Review the location where your model will be written. Any changes you make to the Overwrite location section will be reflected in the Location that Prophecy generates.

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Schema

Define the schema of the dataset and optionally configure additional properties. The schema includes column names, column data types, and optional column metadata. When you expand a row in the Schema table, you can add a column description, apply column tags, and enable/disable quoting for column names.

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Properties

Each property maps to a certain dbt configuration that may be generic to dbt or specific to a platform like BigQuery. If you do not add a property explicitly in the Schema tab, Prophecy uses the dbt default for that property.

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SQL Query

Add a custom SQL query at the end of your target model using the BigQuery SQL dialect. This allows you to apply a final transformation step, which can be useful if you’re importing an existing codebase and need to add conditions or filters to the final output. Custom queries support Jinja, dbt templating, and variable usage for your last-mile data processing. You can reference any column present in the list of input ports beside the SQL query. You can only add additional input ports—the output port cannot be edited.

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Write Options

For a complete guide to defining how to write target tables, visit Write strategies.

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Data Tests

A data test is an assertion you define about a dataset in your project. Data tests are run on target models to ensure the quality and integrity of the final data that gets written to the warehouse. Learn how to build tests in Table tests.