storage class

Related topics

• Cloud Storage depends on storage class From the official documentation: “- The monthly availability of data stored in Cloud Storage depends on the storage class of the data and the location type of the bucket.”
• storage class configures Cloud Storage bucket From the official documentation: “Create a Cloud Storage bucket with storage class and location parameters.”
• 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”
• 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.”
• Cloud Storage works with Customer-Managed Encryption Keys From the official documentation: “Cloud Storage supports CMEK for routing logs.”
• Cloud Storage works with Managed Service for Apache Spark From the official documentation: “Managed Service for Apache Spark accesses these files on Cloud Storage.”
• Cloud Storage works with object table From the official documentation: “To analyze images without moving them from Cloud Storage, create an object table.”
• 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 Storage bucket is part of Cloud Storage From the official documentation: “Create a Google Cloud Storage bucket”
• Managed Service for Apache Spark depends on Cloud Storage From the official documentation: “Managed Service for Apache Spark uses Cloud Storage as its default distributed file system.”
• BigQuery works with Managed Service for Apache Spark From the official documentation: “This means you can quickly read and write BigQuery data to and from Managed Service for Apache Spark.”
• 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).)”
• 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 is an alternative to Managed Service for Apache Spark From the official documentation: “For more complex data loads into your Cloud SQL for PostgreSQL database, consider using Dataflow or Managed Service for Apache Spark, which involves creating an ETL process.”
• 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 is an alternative to Managed Service for Apache Spark From the official documentation: “An Analytics Workload refers to the migration of new Data Lake and Data Warehouse environments from on-premises (e.g., Teradata, Cloudera) and cloud platforms (e.g., Snowflake, Redshift, Databricks) to BigQuery or Managed Service for Apache Spark on Google Cloud.”
• 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 bucket works with Managed Service for Apache Spark From the official documentation: “You can add this flag to specify a Cloud Storage bucket where Managed Service for Apache Spark will upload workload dependencies.”
• 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…”
• 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.”
• 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 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.”
• 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.”
• Customer-Managed Encryption Keys works with Managed Service for Apache Spark From the official documentation: “If you want to control your encryption keys, then you can use customer-managed encryption keys (CMEKs) in [Cloud KMS](/kms/docs) with CMEK-integrated services including Managed Service for Apache Spark.”
• Customer-Managed Encryption Keys secures Managed Service for Apache Spark From the official documentation: “You can use customer-managed encryption keys (CMEK) to encrypt the following cluster data: - Data on persistent disks attached to Managed Service for Apache Spark cluster VMs - Job argument data submitted to your cluster, such as a query string submitted with a Spark SQL job -…”
• 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.”
• 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.”