All BigQuery code samples

Client: Create credentials with scopes

Create credentials with Drive and BigQuery API scopes.

Java Python

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• Querying Google Drive data

Client: Create with application default credentials

Create a BigQuery client using application default credentials.

Java Node.js PHP Python

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• Getting started with authentication

Client: Create with service account key file

Create a BigQuery client using a service account key file.

C# Java Node.js Python

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• Authenticating with a service account key file

Customer-managed Encryption Keys: Copy table

Copy a table with customer-managed encryption keys.

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• Protecting data with Cloud KMS keys

Customer-managed Encryption Keys: Create table

Create a table with customer-managed encryption keys.

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• Protecting data with Cloud KMS keys

Customer-managed Encryption Keys: Load table

Load a table with customer managed encryption keys to Google Cloud Storage in JSON format.

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• Protecting data with Cloud KMS keys

Customer-managed Encryption Keys: Query table

Query a table with customer-managed encryption keys.

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• Protecting data with Cloud KMS keys

Customer-managed Encryption Keys: Update table

Update a table with customer-managed encryption keys.

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• Protecting data with Cloud KMS keys

Datasets: Check dataset existence

A function to check whether a dataset exists.

Java Python

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• Managing datasets
• Migrating from Python Client Library v0.27

Datasets: Create dataset

Create a dataset within a project.

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• Creating datasets
• Getting started with authentication
• Migrating from Python Client Library v0.27

Datasets: Delete dataset

Delete a dataset from a project.

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• Managing datasets
• Migrating from Python Client Library v0.27

Datasets: Delete dataset and contents

Delete a dataset and its contents from a project.

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Datasets: Delete label

Remove a label from a dataset.

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• Deleting labels

Datasets: Get dataset labels

Retrieve the labels of a dataset for a given dataset ID.

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• Viewing labels

Datasets: Get dataset properties

Retrieve the properties of a dataset.

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• Getting information about datasets
• Migrating from Python Client Library v0.27

Datasets: List by label

List datasets, filtering by labels.

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• Filtering resources using labels

Datasets: List datasets

Lists all existing datasets in a project.

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• Listing datasets
• Migrating from Python Client Library v0.27

Datasets: Update access controls

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• Controlling access to datasets
• Updating dataset properties

Datasets: Update default partition expiration

Java Python

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Datasets: Update default table expiration times

Updates dataset's default table expiration times.

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• Updating dataset properties

Datasets: Update description

Update the description of an existing dataset resource.

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• Migrating from Python Client Library v0.27
• Updating dataset properties

Datasets: Update label

Update an existing label on a dataset.

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• Adding labels to resources
• Updating labels

Jobs: Cancel job

Attempt to cancel a job.

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• Managing jobs

Jobs: Create job

Run a BigQuery (query, load, extract, copy) job in a specified location with additional configuration.

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• Running jobs programmatically

Jobs: Get job properties

Retrieve the properties of a job for a given job ID.

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• Managing jobs

Jobs: List jobs

List all jobs in a project.

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• Managing jobs

Load: Avro

Load Avro file from Google Cloud Storage into a new table.

Java Node.js Python

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• Loading Avro data from Cloud Storage

Load: Avro, replace table

Load Avro file from Google Cloud Storage, replacing existing table data.

Java Node.js Python

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• Loading Avro data from Cloud Storage

Load: CSV

Load CSV from Google Cloud Storage, using an explicit schema.

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• Loading CSV data from Cloud Storage
• Migrating from Python Client Library v0.27

Load: CSV, autodetect schema

Load CSV file from Google Cloud Storage, using autodetected schema.

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Load: CSV, replace table

Load CSV file from Google Cloud Storage, replacing a table.

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• Loading CSV data from Cloud Storage

Load: Clustered table

Load data from a CSV file on Google Cloud Storage to a clustered table.

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• Creating and using clustered tables

Load: Column-based time partitioning

Load data into a table that uses column-based time partitioning.

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• Loading CSV data from Cloud Storage
• Managing partitioned table data

Load: JSON

Loads JSON file from Google Cloud Storage, using an explicit schema.

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• Loading JSON data from Cloud Storage
• Specifying a schema

Load: JSON, autodetect schema

Load JSON file from Google Cloud Storage, using autodetect schema.

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• Using schema auto-detection

Load: JSON, replace table

Load JSON file from Google Cloud Storage, replacing a table.

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• Loading JSON data from Cloud Storage

Load: Local file

Import a local file into a table.

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• Loading data from a local data source
• Migrating from Python Client Library v0.27

Load: ORC

Load ORC file from Google Cloud Storage into a new table.

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• Loading ORC data from Cloud Storage

Load: ORC, replace table

Load ORC file from Google Cloud Storage, replacing a table.

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• Loading ORC data from Cloud Storage

Load: Parquet

Load Parquet file from Google Cloud Storage into a new table.

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• Loading Parquet data from Cloud Storage

Load: Parquet, replace

Load Parquet file from Google Cloud Storage, replacing a table.

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• Loading Parquet data from Cloud Storage

Load: Relax column, append

Change column from required to nullable in load append job.

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• Modifying table schemas

Models: Create model

Create a model within an existing dataset.

Java Node.js

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Models: Delete model

Delete a model from a dataset.

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• Deleting models

Models: Get model

Get a model resource for a given model ID.

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• Getting model metadata

Models: List models

Lists all existing models in a dataset.

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• Listing models

Models: List models, streaming

Lists all existing models in the dataset using streaming method.

Node.js

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Models: Update description

Update a model's description property for a given model ID.

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• Updating model metadata

Pandas: Download public table data to DataFrame

Use the BigQuery Storage API to speed-up downloads of large tables to DataFrame.

Python

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Pandas: Download public table data to DataFrame from sandbox

Use the BigQuery Storage API to download query results to DataFrame.

Python

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Pandas: Download query results to DataFrame

Get query results as a Pandas DataFrame.

Python

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• Migrating from Python Client Library v0.27

Pandas: Download table data to DataFrame

Python

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Pandas: Load data from DataFrame

Load contents of a pandas DataFrame to a table.

Python

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• Migrating from Python Client Library v0.27

Query Parameters: ARRAY

Run a query with array parameters.

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• Running parameterized queries

Query Parameters: Named

Run a query with named parameters.

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• Running parameterized queries

Query Parameters: Named, provided types

Run a query with named parameters and provided parameter types.

Java Node.js

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Query Parameters: Positional

Run a query with positional parameters.

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• Running parameterized queries

Query Parameters: Positional, provided types

Run a query with positional parameters and provided parameter types.

Java Node.js

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Query Parameters: STRUCT

Run a query with struct parameters.

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• Running parameterized queries

Query Parameters: TIMESTAMP

Run a query with timestamp parameters.

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• Running parameterized queries

Query: Batch priority

Query runs at batch priority.

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• Running interactive and batch query jobs

Query: Clustered table

Query a clustered table.

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• Querying clustered tables

Query: Column-based time partitioning

Query a table that uses column-based time partitioning.

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Query: Disable query cache

Query disables the use of the cache.

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• Using cached query results

Query: Download results to Arrow

Run a query and get Arrow table.

Python

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Query: Dry run

Run a dry run query.

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• Controlling costs in BigQuery
• Estimating storage and query costs
• Issuing a query dry run

Query: Enable large results

Query enables large result sets using legacy SQL.

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• Writing query results

Query: External Cloud Storage, permanent table

Query data from a file on Google Cloud Storage by creating a permanent table.

Java Node.js Python

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• Querying Cloud Storage data

Query: External Cloud Storage, temporary table

Query data from a file on Google Cloud Storage by creating a temporary table.

Java Python

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• Querying Cloud Storage data

Query: External Sheets, permanent table

Query data from a Google Sheets file by creating a permanent table.

Java Python

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• Querying Google Drive data

Query: External Sheets, temporary table

Query data from a Google Sheets file by creating a temporary table.

Java Python

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• Querying Google Drive data

Query: Legacy SQL

Runs a query with legacy SQL.

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• Switching SQL dialects

Query: Pagination

Java Python

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• Paging through table data

Query: Relax column, append

Change column from required to nullable in query append job.

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• Modifying table schemas

Query: Save query results

Query saves results to permanent table.

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• Creating and using tables
• Writing query results

Query: Script

Run query script.

Java Python

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Query: Standard SQL

Run a query with standard SQL.

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• BigQuery API Client Libraries
• Migrating from Python Client Library v0.27
• Running interactive and batch query jobs
• Switching SQL dialects

Query: Total rows

Run a query and get total rows.

Java Python

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Query: Write to destination table

Tutorial for running a query on the natality public dataset and writing the results to a destination table.

Python

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• Use Dataproc, BigQuery, and Apache Spark ML for Machine Learning

Quickstart: Create a dataset with the BigQuery API

Creates a dataset named "my_new_dataset".

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• Getting started with authentication
• Setting Up a Java Development Environment

Quickstart: Query with the BigQuery API

Create a simple app with BigQuery.

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• Getting started with authentication
• Quickstart: Using client libraries

Quickstart: Read from the BigQuery Storage API

Read data from a table via read stream.

Java Node.js

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Routines: Create routine

Create a routine within an existing dataset.

Java Node.js Python

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Routines: Create routine with DDL

Java Node.js Python

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Routines: Delete routine

Delete a routine from a dataset.

Java Node.js Python

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Routines: Get routine

Get a routine resource for a given routine ID.

Java Node.js Python

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Routines: List routines

Lists all existing routines in a dataset.

Java Node.js Python

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Routines: Update routine

Update an existing routine resource.

Java Node.js Python

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Set user agent

Set a custom user agent on a BigQuery client

Node.js

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Tables: Add column via load job

Add a new column to a BigQuery table while appending rows via a load job.

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• Modifying table schemas

Tables: Add column via query job

Add a new column to a BigQuery table while appending rows via a query job with an explicit destination table.

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• Modifying table schemas

Tables: Add empty column

Manually add an empty column.

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• Modifying table schemas

Tables: Add label

Add a label to a table.

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• Adding labels to resources
• Updating labels

Tables: Column-based time partitioning

Create a table that uses column-based time partitioning.

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• Creating and using time-unit column-partitioned tables

Tables: Copy multiple tables

Copy multiple source tables to a given destination.

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• Managing tables

Tables: Copy table

Copy a single source table to a given destination.

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• Managing tables
• Migrating from Python Client Library v0.27

Tables: Create clustered table

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• Creating and using clustered tables

Tables: Create integer range partitioned table

Create a new integer range partitioned table in an existing dataset.

Java Node.js Python

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• Creating and using integer range partitioned tables

Tables: Create table with schema

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• Creating and using tables
• Migrating from Python Client Library v0.27
• Specifying a schema

Tables: Delete label

Remove a label from a table.

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• Deleting labels

Tables: Delete table

Delete a table from a dataset.

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• Managing tables
• Managing views
• Migrating from Python Client Library v0.27

Tables: Export table to Cloud Storage, CSV

Exports a table to CSV file in Google Cloud Storage bucket.

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• Exporting table data
• Migrating from Python Client Library v0.27

Tables: Export table to Cloud Storage, JSON

Exports a table to newline delimited JSON file to Google Cloud Storage bucket.

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Tables: Export table to Cloud Storage, compressed

Exports a table to a compressed file in Google Cloud Storage bucket.

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• Exporting table data

Tables: Get table labels

Retrieve the labels of a table for a given table ID.

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• Viewing labels

Tables: Get table properties

Retrieve the properties of a table for a given table ID.

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• Creating and using tables
• Migrating from Python Client Library v0.27

Tables: Insert, no IDs

Insert rows without row IDs in a table.

Java Python

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• Streaming data into BigQuery

Tables: List tables

List tables in a given dataset

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• Creating and using tables
• Listing views
• Migrating from Python Client Library v0.27

Tables: Nested repeated schema

Specifying nested and repeated columns in schema.

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• Specifying nested and repeated columns

Tables: Preview table data

Retrieve selected row data from a table.

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• Managing table data
• Paging through table data

Tables: Relax column

Change columns from required to nullable.

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• Modifying table schemas

Tables: Streaming insert

Inserts simple rows into a table via the streaming API (insertAll).

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• Migrating from Python Client Library v0.27
• Streaming data into BigQuery

Tables: Streaming insert with complex data types

Insert data of various BigQuery-supported types into a table.

Java Node.js

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Tables: Table exists

A function to check whether a table exists.

Java Python

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• Migrating from Python Client Library v0.27

Tables: Undelete

Undelete a table.

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• Managing tables

Tables: Update description

Update a table's description.

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• Managing tables
• Migrating from Python Client Library v0.27
• Updating view properties

Tables: Update expiration

Update a table's expiration time.

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• Managing tables
• Updating view properties

Tables: Update require partition filter

Update require partition filter on a table.

Java Python

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Tables: Update with DML

Java

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• Updating table data using the Data Manipulation Language

Tutorial: Create an Authorized View

Create an authorized view using GitHub public data.

Java Node.js Python

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• Creating an authorized view

Views: Create view

Create a view within a dataset.

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• Creating views

Views: Create view with DDL

Create a view via a DDL query.

Java Node.js Python

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• Using data definition language statements

Views: Get view properties

Retrieve the properties of a view for a given view ID.

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• Getting information about views

Views: Grant view access

Authorize and grant access to a view.

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• Creating authorized views

Views: Update query

Update a view's query.

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• Updating view properties