Cloud resource connection

Related topics

• 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.”
• 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 resource connection works with Spanner From the official documentation: “A Cloud resource connection associated with a service account is used to connect to the Spanner.”
• 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.”
• Cloud resource connection depends on BigQuery Connection API From the official documentation: “These external connections use the BigQuery Connection API.”
• 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.”
• Cloud resource connection works with remote models From the official documentation: “You can create and share a Cloud resource connection so that data analysts can query external data, run remote functions, and use remote models without managing credentials directly.”
• Cloud resource connection works with Vertex AI From the official documentation: “`CONNECTION`: a [Cloud resource connection](/bigquery/docs/create-cloud-resource-connection) in the `[PROJECT_ID.]LOCATION.CONNECTION_ID` format. This connection is used to communicate with Vertex AI and Cloud Storage.”
• 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 works with Cloud SQL From the official documentation: “Add credentials to connect BigQuery to Cloud SQL.”
• BigQuery works with Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• Cloud SQL works with Cloud Storage From the official documentation: “Exports data from a Cloud SQL instance to a Cloud Storage bucket as a SQL dump or CSV file.”
• BigQuery works with Dataflow From the official documentation: “Read from BigQuery to Dataflow”
• Cloud Storage works with Dataflow From the official documentation: “Write from Dataflow to Cloud Storage”
• Dataflow works with Spanner From the official documentation: “Use Dataflow to load data into Spanner.”
• Compute Engine works with service account From the official documentation: “A common method is to attach a service account to a resource like a Compute Engine instance.”
• 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 Spanner From the official documentation: “Federated analytics seamlessly combine data from Spanner and other sources in BigQuery.”
• Cloud Run works with Cloud SQL From the official documentation: “Connecting from Cloud Run (fully managed) to Cloud SQL”
• Cloud SQL works with Compute Engine From the official documentation: “Cloud SQL uses Compute Engine virtual machines (VMs) with persistent storage disks.”
• service account works with Vertex AI From the official documentation: “The service account can interact with Vertex AI services.”
• 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.”
• Cloud Run depends on service account From the official documentation: “The Cloud Run function needs a service account with permissions to write to GCS bucket and be invoked by Pub/Sub.”
• Cloud Logging works with Cloud SQL From the official documentation: “Database Migration Service and Cloud SQL use Cloud Logging.”
• Cloud Logging works with Cloud Storage From the official documentation: “Cloud Storage writes various log entries to Cloud Logging in the Google Cloud console.”
• Cloud Run works with Google Cloud Storage From the official documentation: “The Cloud Run function needs a service account with permissions to write to GCS bucket.”
• Cloud SQL works with Vertex AI From the official documentation: “This page describes how to integrate Cloud SQL with [Vertex AI](/vertex-ai/docs).”
• Cloud Storage works with Compute Engine From the official documentation: “For example, you can create a service account that your Compute Engine instances use to access objects stored in Cloud Storage buckets.”
• BigQuery works with Google Cloud Storage From the official documentation: “Load Cloud Storage data into BigQuery”
• BigQuery works with Vertex AI From the official documentation: “The page provides guidance for getting predictions from Vertex AI, based on retail [data exported into BigQuery](/retail/docs/export-data-into-bq).”
• Cloud Logging works with Compute Engine From the official documentation: “Compute Engine sends you Cloud Logging events for scheduled, started, or completed maintenance.”
• Cloud Logging works with Gemini Enterprise Agent Platform From the official documentation: “When you run a training job, Gemini Enterprise Agent Platform training saves logs to [Logging](/logging/docs).”
• Cloud Run works with Cloud Storage From the official documentation: “Receive events from Cloud Storage in a Cloud Run service”
• Cloud Run works with Identity and Access Management From the official documentation: “For example, a remote backend deployed on Cloud Run can use Identity and Access Management (IAM) to:”
• 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.”
• Identity and Access Management secures Cloud Storage From the official documentation: “Cloud Storage uses Identity and Access Management (IAM) for authorization.”
• Identity and Access Management secures Spanner From the official documentation: “Identity and Access Management (IAM) lets you control user and group access to Spanner resources at the project, Spanner instance, and Spanner database levels.”
• service account is part of Identity and Access Management From the official documentation: “Service accounts function as both principals and resources within IAM.”
• Vertex AI depends on service account From the official documentation: “To use the Google Vertex AI model endpoints, you must add Vertex AI permissions to the service account that you used while installing AlloyDB Omni.”
• BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• BigQuery works with Identity and Access Management From the official documentation: “BigQuery uses Identity and Access Management (IAM) for authorization.”
• Cloud Logging is an alternative to Cloud Storage From the official documentation: “Instead, they are routed to either Cloud Storage (recommended) or Cloud Logging, depending on your organization's configuration.”
• Cloud Logging works with Google Cloud Storage From the official documentation: “Cloud Logging can [route logs](/logging/docs/export/configure_export_v2) to Cloud Storage by Google SecOps on a scheduled basis.”
• Cloud Logging is an alternative to Google Cloud Storage From the official documentation: “When you specify your own service account for builds, you must store your build logs in a Cloud Logging bucket, in a user-created Cloud Storage bucket, or in a Google Cloud-created and user-owned Cloud Storage bucket.”
• Cloud SQL is an alternative to Spanner From the official documentation: “If a workload requires transaction processing with strong relational consistencies, [Spanner](/spanner/docs/overview) or [Cloud SQL](/sql/docs) can be better alternatives.”
• Cloud Storage works with service account From the official documentation: “The service account can manage objects in the Cloud Storage bucket.”
• Compute Engine works with Google Cloud Storage From the official documentation: “The usage export bucket is a Google Cloud Storage bucket where Compute Engine usage reports are stored.”
• Dataflow depends on Cloud Storage From the official documentation: “Begin by creating a Cloud Storage bucket using the Google Cloud console or Google Cloud CLI. The Dataflow pipeline uses this bucket as a temporary storage location.”
• Dataflow depends on Compute Engine From the official documentation: “When Dataflow runs a pipeline, the processing is distributed across multiple Compute Engine virtual machines (VMs), also known as workers.”
• Dataflow works with Google Cloud Storage From the official documentation: “When you run a pipeline using Dataflow, your results are stored in a Cloud Storage bucket.”