Google BigQuery
This page describes how to use BigQuery external Source and Target gems to read from or write to tables. Only use an external Source and Target gem when BigQuery is not the configured SQL warehouse connection. Otherwise, use the Table gem.
Source configuration
Use these settings to configure a BigQuery Source gem for reading data.
Source location
| Parameter | Description |
|---|---|
| Format type | Table format for the source. For BigQuery tables, set to bigquery. |
| Select or create connection | Select or create a new BigQuery connection in the Prophecy fabric you will use. |
| Dataset | Dataset containing the table you want to read from. |
| Name | Exact name of the BigQuery table to read data from. |
Source properties
Infer or manually configure the schema of your Source gem. Optionally, add a description for your table. Additional properties are not supported at this time.
Target configuration
Use these settings to configure a BigQuery Target gem for writing data.
Target location
| Parameter | Description |
|---|---|
| Format type | Table format for the target. For BigQuery tables, set to bigquery. |
| Select or create connection | Choose or create a BigQuery connection in the Prophecy fabric you will use. |
| Dataset | Dataset where the target table will be created or updated. |
| Name | Name of the BigQuery table to write data to. If the table doesn’t exist, it will be created automatically. |
Target properties
| Property | Description | Default |
|---|---|---|
| Description | Description of the table. | None |
| Write Mode | Whether to overwrite the table completely, append new data to the table, or throw an error if the table exists. | None |
Cross-workspace access
If your fabric uses BigQuery as the SQL warehouse, you can’t select BigQuery in an external Source or Target gem. Instead, you must use Table gems, which are limited to the BigQuery warehouse defined in the SQL warehouse connection.
To work with tables from a different BigQuery workspace, use BigQuery sharing. This lets you access shared resources without creating additional BigQuery connections.
Prophecy implements this guardrail to avoid using external connections when the data can be made available in your warehouse. External connections introduce an extra data transfer step, which slows down pipeline execution and adds unnecessary complexity. For best performance, Prophecy always prefers reading and writing directly within the warehouse.