Build and operate the data processing systems behind analytics and ML on Google Cloud, including BigQuery, Dataflow, and Pub/Sub, and track your readiness for the Professional Data Engineer exam.
Advanced
Prepare and test your skills
Advanced
Prepare and test your skills
Google Cloud feature
authorized views
Topic in 6 exam domains
Also called: authorized logical view, authorized view
A view configuration used to share query results or control access to underlying data without giving direct table access.
Where this appears on the exam
4 objectives in 3 of 5 domains
Related topics
FoundationsBuilding blocksBuilding blocksThis topicWorks alongsideAppliedIdentity and Access ManagementServiceVPC Service ControlsServiceBigtableServiceCloud StorageServiceDatastreamServiceSensitive Data ProtectionServiceCloud SQLServiceBigQueryServiceDataflowServicerow-level securityFeature
authorized viewsFeature
Column-level access controlsFeaturesubset tablesConceptlogical viewFeaturePoints to what builds on itWorks with or alternative toHover or tap a line to see what connects the two
- authorized views is part of BigQuery From the official documentation: “In this tutorial, you create an authorized view in BigQuery that is used by your data analysts.”
- authorized views is an alternative to Column-level access controls From the official documentation: “An alternative approach to using an authorized view would be to set up column-level access controls on the source data and then give your users access to a view that queries the access-controlled data.”
- authorized views depends on Identity and Access Management From the official documentation: “To use authorized views, you need the following [IAM permissions](/resource-manager/docs/access-control-proj#permissions_and_roles):”
- authorized views secures logical view From the official documentation: “To control access to views in BigQuery, see [Authorized views](/bigquery/docs/authorized-views).”
- authorized views is an alternative to row-level security From the official documentation: “Although authorized views are flexible and scalable, one of the following methods might better apply to your use case: - Set row-level policies on a table.”
- authorized views is an alternative to subset tables From the official documentation: “To manage tenant access to subsets of large fact tables, you can use tenant-specific authorized views or create tenant-specific subset tables.”
- authorized views works with VPC Service Controls From the official documentation: “When using authorized views in a VPC Service Controls perimeter, ingress and egress rules must allow principals access to the project that contains the view and any other projects that contain the source data the view is accessing data from.”
- BigQuery works with Sensitive Data Protection From the official documentation: “Uses the Data Loss Prevention API to analyze BigQuery data.”
- 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: “External BigQuery data source backed by Cloud Storage”
- BigQuery works with Datastream From the official documentation: “Permissions needed for datastream to write to BigQuery.”
- BigQuery works with Dataflow From the official documentation: “Read from BigQuery to Dataflow”
- BigQuery works with Cloud SQL From the official documentation: “Add credentials to connect BigQuery to Cloud SQL.”
- Cloud Storage works with Datastream From the official documentation: “When you stream to Cloud Storage, Datastream organizes data by object name and timestamp.”
- VPC Service Controls secures BigQuery From the official documentation: “BigQuery data is protected with VPC Service Controls”
- Cloud SQL works with Cloud Storage From the official documentation: “When ingesting data from Cloud SQL, data is first staged to a Cloud Storage bucket.”
- BigQuery works with Bigtable From the official documentation: “External BigQuery data source backed by Bigtable”
- 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 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 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).”
- logical view is part of BigQuery From the official documentation: “The default type of view for BigQuery is a *logical view*.”
- row-level security is part of BigQuery From the official documentation: “This document describes how to use row-level access security with other BigQuery features.”
- VPC Service Controls secures Cloud Storage From the official documentation: “Storage Transfer Service supports transfers to Cloud Storage buckets protected by VPC Service Controls.”
- 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 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.”
- Identity and Access Management secures BigQuery From the official documentation: “Access to BigQuery data from AlloyDB is managed through IAM.”
- 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.”
- Identity and Access Management secures Bigtable From the official documentation: “Bigtable uses Identity and Access Management (IAM) for authorization.”
- VPC Service Controls secures Cloud SQL From the official documentation: “VPC Service Controls perimeters protect the Cloud SQL Admin API.”
- Bigtable is an alternative to Cloud Storage From the official documentation: “If the joining data is in the gigabyte or terabyte range, use either Bigtable or Cloud Storage to merge the joining data to the driving data, depending on the nature of the data.”
- Bigtable works with Dataflow From the official documentation: “The Dataflow job then writes the transformed data to your Bigtable table.”
- Bigtable works with Identity and Access Management From the official documentation: “Bigtable uses [Identity and Access Management (IAM)](/iam) for access control.”
- 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: “Use Dataflow and Sensitive Data Protection to securely tokenize and import data from a relational database to BigQuery.”
- Identity and Access Management works with VPC Service Controls From the official documentation: “VPC Service Controls perimeters and Identity and Access Management (IAM) roles work together.”
- Sensitive Data Protection works with VPC Service Controls From the official documentation: “For information about VPC Service Controls support for Sensitive Data Protection, see the [Supported products](/vpc-service-controls/docs/supported-products#table_dlp) table in the VPC Service Controls documentation.”
- 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 is an alternative to Dataflow From the official documentation: “To automate the EDA process, use BigQuery or a managed data processing service like Dataflow.”
- BigQuery works with VPC Service Controls From the official documentation: “BigQuery integrates with VPC Service Controls to restrict access to your BigQuery resources from specific networks.”
- Bigtable is an alternative to Cloud SQL From the official documentation: “If you're interested in evaluating how the performance of your on-premises databases compares to [Cloud SQL](/sql), [Spanner](/spanner), [Firestore](/firestore), or [Bigtable](/bigtable), you could implement a PoC where the same business logic uses different databases.”
- Bigtable works with Cloud Storage From the official documentation: “Another way you can back up Bigtable is to use a managed data export to Cloud Storage.”
- 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 VPC Service Controls From the official documentation: “Make sure that you configure VPC Service Controls so that the project that hosts the CA Service CA pool and CA<br>and the project that hosts Cloud SQL belong to the same service perimeter.”
- Dataflow depends on Identity and Access Management From the official documentation: “Dataflow uses Identity and Access Management (IAM) for authorization.”
- Dataflow works with VPC Service Controls From the official documentation: “To customize DNS resolution when using Dataflow with VPC Service Controls, use Cloud DNS [private zones](/dns/docs/overview#dns-private-zones) instead of using custom BIND servers.”
- 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).”
- 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.”
- logical view is part of Bigtable From the official documentation: “You can create a parameterized view from a logical view in Bigtable and then perform operations on parameterized views.”
- row-level security is part of Cloud SQL From the official documentation: “In Cloud SQL, tables can have row-level security policies that restrict, on a per-user basis, which rows users can view by creating queries, or the rows that users can insert, update, or delete by running data modification commands.”
- 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.”
- VPC Service Controls secures Bigtable From the official documentation: “You can use [VPC Service Controls](/vpc-service-controls/docs/overview) to control access to Bigtable instances from outside of approved networks.”
- VPC Service Controls secures Dataflow From the official documentation: “Place your Dataflow pipelines inside a security perimeter using VPC Service Controls.”
- VPC Service Controls secures Datastream From the official documentation: “The API for Datastream can be protected by VPC Service Controls”
- VPC Service Controls secures Sensitive Data Protection From the official documentation: “VPC Service Controls enables you to define a security perimeter around your projects and resources, including Sensitive Data Protection resources.”
How learners score on this topic
Last updated