BigQuery Connection API

Related topics

• BigQuery DataFrames depends on BigQuery Connection API From the official documentation: “[BigQuery Connection API (`bigqueryconnection.googleapis.com`)](/bigquery/docs/reference/bigqueryconnection/rest)”
• BigQuery depends on BigQuery Connection API From the official documentation: “To create remote connections in BigQuery for Gemini and embeddings models, enable the BigQuery Connection API in your Google Cloud project.”
• BigQuery Connection API configures BigQuery connection From the official documentation: “The BigQuery connection API lets you manage BigQuery connections to external data sources such as a Spanner database.”
• Cloud resource connection depends on BigQuery Connection API From the official documentation: “These external connections use the BigQuery Connection API.”
• BigQuery is an alternative to Cloud Storage From the official documentation: “The `import_result_sink` must be a Cloud Storage path or a BigQuery table.”
• BigQuery works with Cloud Storage From the official documentation: “Sample to load Avro data from Cloud Storage into a new BigQuery table”
• BigQuery is an alternative to Google Cloud Storage From the official documentation: “The training data can be either a CSV file in Cloud Storage or a table in BigQuery.”
• BigQuery works with Gemini Enterprise Agent Platform From the official documentation: “The BigQuery dataset stores all versions for the associated Gemini Enterprise Agent Platform dataset.”
• BigQuery dataset is part of BigQuery From the official documentation: “Sample demonstrating how to create a dataset in BigQuery.”
• BigQuery works with BigQuery DataFrames From the official documentation: “Uploads a pandas DataFrame to BigQuery using BigQuery DataFrames.”
• BigQuery works with Google Cloud Storage From the official documentation: “Load Cloud Storage data into BigQuery”
• Cloud resource connection works with Cloud Storage From the official documentation: “Cloud resource connection lets BigQuery access Cloud Storage object data and metadata using its own service account.”
• Cloud resource connection works with Gemini Enterprise Agent Platform From the official documentation: “BigQuery uses a [Cloud resource connection](/bigquery/docs/create-cloud-resource-connection) to interact with the Gemini Enterprise Agent Platform endpoint.”
• Cloud Storage works with Gemini Enterprise Agent Platform From the official documentation: “When you perform custom training, Agent Platform usually loads your data from Cloud Storage.”
• BigQuery works with Cloud resource connection From the official documentation: “After you create the training dataset, you establish a connection to link BigQuery and external sources.”
• BigQuery dataset works with Cloud resource connection From the official documentation: “Create the connection in the same [location](/bigquery/docs/locations) as the dataset you created in the previous step.”
• Cloud resource connection is part of BigQuery From the official documentation: “A Cloud resource connection in BigQuery lets you connect BigQuery to other Google Cloud services and external storage by using a dedicated service account with delegated access.”
• Cloud resource connection depends on service account From the official documentation: “A Cloud resource connection in BigQuery lets you connect BigQuery to other Google Cloud services and external storage by using a dedicated service account with delegated access.”
• Cloud resource connection works with service account From the official documentation: “When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.”
• Cloud Storage works with service account From the official documentation: “The service account can manage objects in the Cloud Storage bucket.”
• remote model depends on Gemini Enterprise Agent Platform From the official documentation: “Create a remote model that represents a hosted Agent Platform model:”
• service account is part of Cloud resource connection From the official documentation: “When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.”
• BigQuery depends on Cloud resource connection From the official documentation: “BigQuery uses a [Cloud resource connection](/bigquery/docs/create-cloud-resource-connection) to interact with the Cloud AI service.”
• BigQuery depends on Cloud Storage From the official documentation: “BigQuery relies on Cloud Storage for staging file data and supports reading from and writing to several other sources and Google Cloud services.”
• BigQuery works with remote model From the official documentation: “The function uses a BigQuery ML [remote model](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model-service) to connect BigQuery to the Cloud Natural Language API.”
• BigQuery works with service account From the official documentation: “The service account can be used by services, such as Cloud Run, to read and modify data in the BigQuery dataset.”
• BigQuery connection is part of BigQuery From the official documentation: “In Cloud Shell, create a BigQuery connection to access Cloud Run:”
• BigQuery connection works with BigQuery dataset From the official documentation: “Ensure that the BigQuery connection and the BigQuery dataset exist in the same region, and that they are compatible with the location of the Cloud Storage bucket.”
• BigQuery connection works with Cloud Storage From the official documentation: “Grant the service account for the BigQuery connection an IAM role that lets it read data stored in your Cloud Storage bucket:”
• BigQuery DataFrames depends on BigQuery From the official documentation: “BigQuery DataFrames uses BigQuery, Cloud Run functions, Agent Platform, and other Google Cloud services, which incur their own costs.”
• BigQuery dataset works with service account From the official documentation: “The service account can be used by services, such as Cloud Run, to read and modify data in the BigQuery dataset.”
• Cloud resource connection depends on Cloud Storage From the official documentation: “- `CONNECTION_ID`: A `STRING` value that contains a [Cloud resource connection](/bigquery/docs/create-cloud-resource-connection) that the service can use to access the objects in Cloud Storage, in the format `location.connection_id`.”
• Cloud resource connection secures Google Cloud Storage From the official documentation: “This connection acts as a secure, delegated identity that BigQuery uses to manage and read your Iceberg files. This helps ensure that individual users never have direct access to your Cloud Storage bucket.”
• Cloud resource connection works with remote model From the official documentation: “Create a [Cloud resource connection](/bigquery/docs/create-cloud-resource-connection) for the remote model to use, and get the connection's service account.”
• Gemini Enterprise Agent Platform depends on Cloud Storage From the official documentation: “Certain tasks in Agent Platform require that you use additional Google Cloud products besides Agent Platform. For example, in most cases, you must use Cloud Storage and Artifact Registry when you create a custom training pipeline.”
• Gemini Enterprise Agent Platform works with remote model From the official documentation: “Create a remote model that represents a hosted Agent Platform `gemini-3.5-flash-lite` model:”
• remote model depends on BigQuery connection From the official documentation: “REMOTE WITH CONNECTION {DEFAULT | `PROJECT_ID.REGION.CONNECTION_ID`}”
• service account depends on BigQuery From the official documentation: “Each feature view uses a service account to access the source data in BigQuery during sync.”
• service account depends on Google Cloud Storage From the official documentation: “Google SecOps uses a unique service account to read data from your GCS bucket. You must grant this service account access to your bucket.”