Connections

Related topics

• Connections is part of BigQuery From the official documentation: “A connection is a BigQuery resource that stores credentials for accessing external data.”
• Identity and Access Management secures Connections From the official documentation: “Get the current Identity and Access Management (IAM) policy for a BigQuery connection. The policy defines which principals have what permissions on the connection resource.”
• BigQuery works with Datastream From the official documentation: “Datastream supports three types of partitioning for BigQuery tables:”
• BigQuery works with Sensitive Data Protection From the official documentation: “Uses the Data Loss Prevention API to analyze BigQuery data.”
• Cloud Storage works with Datastream From the official documentation: “When you stream to Cloud Storage, Datastream organizes data by object name and timestamp.”
• 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 Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• BigQuery works with Cloud Storage From the official documentation: “External BigQuery data source backed by Cloud Storage”
• BigQuery works with Cloud SQL From the official documentation: “Add credentials to connect BigQuery to Cloud SQL.”
• BigQuery works with Dataflow From the official documentation: “Read from BigQuery to Dataflow”
• 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.”
• Cloud Logging works with Cloud SQL From the official documentation: “Cloud SQL uses Cloud Logging.”
• Cloud Storage works with Dataflow From the official documentation: “Write from Dataflow to Cloud Storage”
• Cloud Storage works with Sensitive Data Protection From the official documentation: “Uses the Data Loss Prevention API to analyze a file on GCS.”
• Cloud SQL works with Identity and Access Management From the official documentation: “This page describes how Cloud SQL is integrated with IAM .”
• Identity and Access Management secures BigQuery From the official documentation: “Access to BigQuery data from AlloyDB is managed through IAM.”
• Cloud SQL works with Datastream From the official documentation: “This page describes an example NAT VM configuration that lets Datastream privately connect to a Cloud SQL instance.”
• 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).”
• Identity and Access Management secures Cloud Storage From the official documentation: “Cloud Storage uses Identity and Access Management (IAM) for authorization.”
• 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 SQL works with Sensitive Data Protection From the official documentation: “Sensitive Data Protection starts identifying your Cloud SQL instances and creating a default connection for each instance.”
• 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.”
• 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.”
• 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 Dataflow From the official documentation: “Information stored in [Cloud Logging](/logging/docs/region-support) is primarily generated by the code in your Dataflow program.”
• 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 works with Datastream From the official documentation: “In addition, Datastream supports writing the change event stream into Cloud Storage, and offers streamlined integration with Dataflow templates to build custom workflows for loading data into a wide range of destinations, such as Cloud SQL and Spanner.”
• Dataflow works with Sensitive Data Protection From the official documentation: “This proof-of-concept uses Dataflow and Sensitive Data Protection to securely tokenize and import data from a relational database to BigQuery.”
• Identity and Access Management secures 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).”
• BigQuery is an alternative to Dataflow From the official documentation: “To automate the EDA process, use BigQuery or a managed data processing service like Dataflow.”
• Cloud Logging works with Identity and Access Management From the official documentation: “This document helps you understand how Cloud Logging uses [Identity and Access Management (IAM)](/iam/docs/overview) roles and permissions to control access to Logging resources.”
• 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.”
• 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 Datastream From the official documentation: “You can get and set permissions for Datastream using the Google Cloud Console, the IAM methods of the API, or the Google Cloud SDK.”
• Sensitive Data Protection secures BigQuery From the official documentation: “To help meet compliance requirements, use Sensitive Data Protection to inspect your BigQuery tables and to help protect your sensitive data.”
• Sensitive Data Protection depends on Identity and Access Management From the official documentation: “Sensitive Data Protection uses Identity and Access Management (IAM) to control access to resources.”