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You can write datasets to BigQuery as part of your job definition.
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NOTE: In some environments, users may not be permitted the bigquery.datasets.create permission, and jobs on BigQuery sources fail. As a workaround, an administrator can define a BigQuery dataset for use for these jobs. This dataset is used by for storing temporary tables of intermediate query results when running jobs on BigQuery sources, which eliminates the need for the bigquery.datasets.create permission in the service account. For more information on defining this BigQuery query dataset, see Dataprep Project Settings Page. |
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NOTE: Object and Array data types are written back to BigQuery as string values. |
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NOTE: BigQuery does not support destinations with a dot (.) in the name. For more information, see https://cloud.google.com/bigquery/docs/tables#table_naming. |
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Tip: Optionally, you can choose to merge your results to an existing BigQuery table. Based on a specified set of matching columns, you can define the rows to update from your source. |
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NOTE: By default, data is published to BigQuery using strict data type matching rules. When there are mismatches between and BigQuery data types, the publication fails. This setting is defined on a per-output basis. For more information, see BigQuery Table Settings. |
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Tip: Optionally, you can choose to merge your results to an existing BigQuery table. Based on a specified set of matching columns, you can define the rows to update from your source. |
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Tip: During import from , you can choose to show hidden files. These files include profiler files generated during a job run. These files can be re-imported into the product and then published to BigQuery for additional analysis. Please note that BigQuery does not support names with a dot in them. For more information, see Import Data Page. |
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Create New Connection: n/a
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| inCQL | true |
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| label | ((label = "bigquery") OR (label = "connection")) |
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