object table

Related topics

• Cloud Storage works with object table From the official documentation: “To analyze images without moving them from Cloud Storage, create an object table.”
• Cloud Storage bucket works with object table From the official documentation: “If you use the remote model to analyze unstructured data from an object table, and the Cloud Storage bucket that you use in the object table is in a different project than your Agent Platform endpoint, you must also have Storage Admin (`roles/storage.admin`) on the Cloud…”
• object table depends on Cloud resource connection From the official documentation: “You must have a Cloud resource connection to connect to the [object table](/bigquery/docs/object-table-introduction) that you create later in this tutorial.”
• object table depends on Cloud Storage From the official documentation: “An [object table](/bigquery/docs/object-table-introduction) is a read-only table over unstructured data objects that reside in Cloud Storage.”
• object table depends on Cloud Storage bucket From the official documentation: “The Cloud Storage bucket used by the input object table must be in the same project where you have created the model and where you are calling the `AI.GENERATE_TEXT` function.”
• object table works with remote model From the official documentation: “By creating a BigQuery ML [remote model](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model-service) that connects to the Cloud Vision API, you can perform these image analysis tasks directly on a BigQuery [object…”
• 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”
• Customer-Managed Encryption Keys secures Cloud Storage From the official documentation: “CMEKs encrypt data at rest in Cloud SQL, Cloud Storage, and within GKE clusters.”
• 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”
• BigQuery ML works with Cloud Storage From the official documentation: “You can export BigQuery ML models to Cloud Storage, and use them for online prediction, or edit them in Python.”
• Cloud Storage works with Cloud Storage FUSE From the official documentation: “Cloud Storage FUSE lets you mount Cloud Storage buckets to your local file system.”
• 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.”
• Cloud Storage works with Customer-Managed Encryption Keys From the official documentation: “Cloud Storage supports CMEK for routing logs.”
• Cloud Storage bucket is part of Cloud Storage From the official documentation: “Create a Google Cloud Storage bucket”
• Customer-Managed Encryption Keys secures BigQuery From the official documentation: “You can either create CMEKs manually to protect your BigQuery resources or use Cloud KMS Autokey.”
• 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.”
• Customer-Managed Encryption Keys secures Gemini Enterprise Agent Platform From the official documentation: “Enable customer-managed encryption keys (CMEK) for the Gemini Enterprise Agent Platform to gain more control over data encryption and key management.”
• 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.”
• remote model is part of BigQuery ML From the official documentation: “- Create a BigQuery ML remote model.”
• 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 ML works with Gemini Enterprise Agent Platform From the official documentation: “You can also access Agent Platform models by using BigQuery ML.”
• 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 Storage bucket works with Cloud Storage FUSE From the official documentation: “When the job runs, the bucket is automatically mounted to the VMs for your job using [Cloud Storage FUSE](/storage/docs/gcs-fuse).”
• Customer-Managed Encryption Keys depends on Cloud Storage From the official documentation: “If used for storing Customer-managed Encryption Keys (CMEK): Cloud Storage (See [Cloud Storage pricing](/storage/pricing).)”
• Customer-Managed Encryption Keys configures Cloud Storage bucket From the official documentation: “If you want your data to remain encrypted with a customer-managed key during an export or import, you must set a customer-managed encryption key on the Cloud Storage bucket before exporting data to it.”
• remote model depends on Gemini Enterprise Agent Platform From the official documentation: “Create a remote model that represents a hosted Agent Platform model:”
• BigQuery works with BigQuery ML From the official documentation: “BigQuery ML lets you build and operationalize machine learning (ML) models over data in BigQuery by using SQL.”
• 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 Cloud Storage bucket From the official documentation: “BigQuery lets you load [Datastore](/datastore) entities from a Cloud Storage bucket into a BigQuery table for analysis.”
• 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 ML is part of BigQuery From the official documentation: “BigQuery ML models are stored in BigQuery datasets, similar to tables and views.”
• BigQuery ML depends on Cloud Storage From the official documentation: “BigQuery ML imports the model from Cloud Storage by using the credentials of the user who runs the `CREATE MODEL` statement.”
• BigQuery ML is an alternative to Gemini Enterprise Agent Platform From the official documentation: “You can use [BigQuery ML](/bigquery/docs/bqml-introduction) or [Agent Platform development tools](/gemini-enterprise-agent-platform/machine-learning/general/developer-tools-overview) to develop, train, and evaluate ML models.”
• 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 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 Storage bucket works with Dataflow From the official documentation: “Begin by creating a Cloud Storage bucket using Google Cloud CLI. This bucket is used as a temporary storage location by the Dataflow pipeline.”
• Cloud Storage FUSE works with Dataflow From the official documentation: “This page describes how to use [Cloud Storage FUSE](/storage/docs/cloud-storage-fuse/overview) with Dataflow to process datasets for machine learning (ML) tasks.”
• Cloud Storage FUSE works with Gemini Enterprise Agent Platform From the official documentation: “Agent Platform uses [Cloud Storage FUSE](/storage/docs/gcs-fuse) to mount the storage buckets.”
• Customer-Managed Encryption Keys is part of BigQuery From the official documentation: “If you require more control over encryption keys, BigQuery supports [customer-managed encryption keys](/bigquery/docs/customer-managed-encryption) in the [Cloud Key Management Service](/kms/docs).”
• Customer-Managed Encryption Keys is part of Cloud Storage From the official documentation: “[Cloud Storage](/storage/docs/introduction) provides features such as customer-managed encryption, lifecycle management, and aging and retention policies.”
• Customer-Managed Encryption Keys works with Dataflow From the official documentation: “This sample shows how to use encryption keys managed by the customer, with a Dataflow pipeline.”
• Customer-Managed Encryption Keys secures Dataflow From the official documentation: “You can either create CMEKs manually to protect your Dataflow resources or use Cloud KMS Autokey.”
• Dataflow depends on Cloud Storage bucket From the official documentation: “Create a Cloud Storage bucket by using the gcloud CLI. This bucket is used as a temporary storage location by the Dataflow pipeline.”
• 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:”