VM instances

Related topics

• VM instances is part of Compute Engine From the official documentation: “In addition to virtual machine (VM) instances, Compute Engine also offers bare metal instances.”
• Cloud Storage bucket works with VM instances From the official documentation: “The Compute Engine VMs interacting with a Cloud Storage bucket must be co-located with the bucket.”
• Customer-Managed Encryption Keys secures VM instances From the official documentation: “When you enable CMEK for Gemini Enterprise Agent Platform resources, the key that you designate, rather than a key managed by Google, is used to encrypt data on the boot disks of these VMs.”
• VM instances depends on attached service account From the official documentation: “The Compute Engine VMs interacting with Cloud Storage must have an [attached service account](/compute/docs/instances/change-service-account), even if the service account has no permissions.”
• VM instances depends on service accounts From the official documentation: “To access Google Cloud resources, Compute Engine virtual machine (VM) instances use [service accounts](/iam/docs/service-account-overview).”
• 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.”
• 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.”
• 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”
• Customer-Managed Encryption Keys secures Cloud SQL From the official documentation: “Enable CMEK for Cloud SQL Databases”
• BigQuery works with Cloud Run functions From the official documentation: “Learn how to write an HTTP Cloud Run function that submits a query to BigQuery.”
• Cloud SQL works with Compute Engine From the official documentation: “Cloud SQL uses Compute Engine virtual machines (VMs) with persistent storage disks.”
• Cloud Storage works with Customer-Managed Encryption Keys From the official documentation: “Cloud Storage supports CMEK for routing logs.”
• Cloud Run functions works with Cloud Storage From the official documentation: “(GCF translates GCS notifications to HTTP requests internally)”
• 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.”
• 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”
• Identity and Access Management secures Cloud Storage From the official documentation: “Cloud Storage uses Identity and Access Management (IAM) for authorization.”
• BigQuery works with Identity and Access Management From the official documentation: “BigQuery uses Identity and Access Management (IAM) for authorization.”
• 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 secures Compute Engine From the official documentation: “In IL5, CMEK is enabled by default for Compute Engine, Google Kubernetes Engine (GKE), Cloud Storage, and Cloud SQL.”
• 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.”
• Identity and Access Management secures Compute Engine From the official documentation: “Compute Engine uses Identity and Access Management (IAM) for authorization.”
• 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 Compute Engine From the official documentation: “Compute Engine integrates with Google Cloud technologies such as Cloud Storage, Google Kubernetes Engine, and BigQuery, to extend beyond the basic computational capability to create more complex and sophisticated applications.”
• Cloud Run functions works with Cloud SQL From the official documentation: “[Cloud Run functions](/functions) is an event-driven serverless compute platform that integrates with Cloud SQL.”
• Cloud Run functions depends on Cloud Storage From the official documentation: “Cloud Run functions uses the config in Cloud Storage to determine what actions to take for each stateful MIG.”
• Cloud Run functions is an alternative to Compute Engine From the official documentation: “Cloud Run functions and App Engine don't offer a two-way configurable firewall, so you must either create compensating controls or deploy the tokenization service on Compute Engine or Google Kubernetes Engine.”
• Cloud Run functions is an alternative to Dataflow From the official documentation: “- Perform file validation by using [Cloud Run functions](/functions/docs/concepts/overview) (for example, based on file upload events) or within [Dataflow](/dataflow/docs/overview) pipelines.”
• Cloud Run functions depends on service accounts From the official documentation: “Your Cloud Run function runs as an automatically created service account.”
• Cloud SQL depends on Cloud Storage From the official documentation: “When ingesting data from Cloud SQL, data is first staged to a Cloud Storage bucket.”
• Cloud SQL works with Cloud Storage bucket From the official documentation: “If you exported into a Cloud Storage bucket, you can also check the log file created by Cloud SQL in the bucket for information about export status.”
• Cloud SQL depends on Compute Engine From the official documentation: “Cloud SQL uses Compute Engine virtual machines (VMs) with persistent storage disks.”
• Cloud SQL works with Dataflow From the official documentation: “Use Datastream to replicate changes from your MySQL instance to either BigQuery or Cloud Storage, and then use Dataflow or Managed Service for Apache Spark to bring the data into your Cloud SQL instance.”
• Cloud SQL works with Identity and Access Management From the official documentation: “This page describes how Cloud SQL is integrated with IAM .”
• Cloud Storage works with Identity and Access Management From the official documentation: “To learn how to use IAM with Cloud Storage, see [Using IAM Permissions](/storage/docs/access-control/using-iam-permissions).”
• 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.”
• 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 secures Cloud Run functions From the official documentation: “Cloud Run functions CMEK protection only applies to Google-managed Cloud Run functions resources; you are responsible for protecting data and resources managed by you, such as your source code repositories, [event…”
• Customer-Managed Encryption Keys is part of Cloud SQL From the official documentation: “Cloud SQL also lets you add another layer of encryption to data using [customer-managed encryption keys (CMEK)](/sql/docs/postgres/cmek).”
• 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.”
• 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.”
• Dataflow depends on Identity and Access Management From the official documentation: “Dataflow uses Identity and Access Management (IAM) for authorization.”
• Identity and Access Management secures BigQuery From the official documentation: “Security in BigQuery is intrinsically linked to [Identity and Access Management (IAM)](/iam/docs/overview) in Google Cloud.”
• Identity and Access Management secures Cloud Run functions From the official documentation: “For Cloud Run functions backend services, [Identity and Access Management (IAM)](https://cloud.google.com/iam/docs) is used to control the ability to view, create, update and delete functions.”
• Identity and Access Management configures Cloud Storage bucket From the official documentation: “You can use [Identity and Access Management (IAM)](/storage/docs/access-control/iam) to control access to individual buckets.”
• Identity and Access Management secures Cloud Storage bucket From the official documentation: “These modifications to that file define a second backup repository in a Cloud Storage bucket, accessed through Identity and Access Management (IAM).”
• Identity and Access Management configures Dataflow From the official documentation: “To control access to Dataflow resources for users, groups, and service accounts in a project or organization, use Identity and Access Management (IAM).”
• Identity and Access Management configures service accounts From the official documentation: “To manage this access, you grant specific Identity and Access Management (IAM) roles to the service accounts that receive events and invoke your target workloads.”
• service accounts secures Cloud Run functions From the official documentation: “You can use dedicated service accounts for authentication to invoke Cloud Run functions from the ABAP SDK.”
• service accounts is part of Identity and Access Management From the official documentation: “The Organization Policy Service provides constraints that can be used in [organization policies](/organization-policy/overview) to limit the usage of Identity and Access Management (IAM) [service accounts](/iam/docs/service-accounts).”