[ Web Proxy ]
URL:
Viewing: https://cloud.google.com/bigquery/docs/data-profile-scan [Back]  [Original]

Profile your data  |  BigQuery  |  Google Cloud Documentation Skip to main content
Google Cloud Documentation [Google Cloud Documentation]
Send feedback Stay organized with collections Save and categorize content based on your preferences.

Profile your data

This document explains how to use data profile scans to better understand your data. BigQuery uses Knowledge Catalog to analyze the statistical characteristics of your data, such as average values, unique values, and maximum values. Knowledge Catalog also uses this information to recommend rules for data quality checks.

For more information about data profiling, see About data profiling.

Tip: The steps in this document show how to manage data profile scans across your project. You can also create and manage data profile scans when working with a specific table. For more information, see the Manage data profile scans for a specific table section of this document.

Before you begin

Enable the Dataplex API.

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

Enable the API

Required roles

This section describes the IAM roles and permissions needed to use Knowledge Catalog data profile scans.

User roles and permissions

To get the permissions that you need to create and manage data profile scans, ask your administrator to grant you the following IAM roles:

For more information about granting roles, see Manage access to projects, folders, and organizations.

These predefined roles contain the permissions required to create and manage data profile scans. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to create and manage data profile scans:

You might also be able to get these permissions with custom roles or other predefined roles.

Knowledge Catalog service account roles and permissions

Note: If you're scanning CMEK-encrypted BigQuery resources, you must also grant the roles/cloudkms.cryptoKeyEncrypterDecrypter role to the relevant service agents. For more information, see DataScans CMEK Permissions.

To ensure that the Knowledge Catalog service account has the necessary permissions to run data profile scans and export results, ask your administrator to grant the following IAM roles to the Knowledge Catalog service account:

Important: You must grant these roles to the Knowledge Catalog service account, not to your user account. Failure to grant the roles to the correct principal might result in permission errors.

For more information about granting roles, see Manage access to projects, folders, and organizations.

These predefined roles contain the permissions required to run data profile scans and export results. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to run data profile scans and export results:

Your administrator might also be able to give the Knowledge Catalog service account these permissions with custom roles or other predefined roles.

If a table uses BigQuery row-level security, then Knowledge Catalog can only scan rows visible to the Knowledge Catalog service account. To let Knowledge Catalog scan all rows, add its service account to a row filter where the predicate is TRUE.

If a table uses BigQuery column-level security, then Knowledge Catalog requires access to scan protected columns. To grant access, give the Knowledge Catalog service account the Data Catalog Fine-Grained Reader (roles/datacatalog.fineGrainedReader) role on all policy tags used in the table. The user creating or updating a data scan also needs permissions on protected columns.

Grant roles to the Knowledge Catalog service account

To run data profile scans, Knowledge Catalog uses a service account that requires permissions to run BigQuery jobs and read BigQuery table data. To grant the required roles, follow these steps:

  1. Get the Knowledge Catalog service account email address. If you haven't created a data profile or data quality scan in this project before, run the following gcloud command to generate the service identity:

    gcloud beta services identity create --service=dataplex.googleapis.com
    

    The command returns the service account email, which has the following format: service-PROJECT_ID@gcp-sa-dataplex.iam.gserviceaccount.com.

    If the service account already exists, you can find its email by viewing principals with the Dataplex name on the IAM page in the Google Cloud console.

  2. Grant the service account the BigQuery Job User (roles/bigquery.jobUser) role on your project. This role lets the service account run BigQuery jobs for the scan.

    gcloud projects add-iam-policy-binding PROJECT_ID \
        --member="serviceAccount:service-PROJECT_NUMBER@gcp-sa-dataplex.iam.gserviceaccount.com" \
        --role="roles/bigquery.jobUser"
    

    Replace the following:

    • PROJECT_ID: your Google Cloud project ID.
    • service-PROJECT_NUMBER@gcp-sa-dataplex.iam.gserviceaccount.com: the email of the Knowledge Catalog service account.
  3. Grant the service account the BigQuery Data Viewer (roles/bigquery.dataViewer) role for each table that you want to profile. This role grants read-only access to the tables.

    gcloud bigquery tables add-iam-policy-binding DATASET_ID.TABLE_ID \
        --member="serviceAccount:service-PROJECT_NUMBER@gcp-sa-dataplex.iam.gserviceaccount.com" \
        --role="roles/bigquery.dataViewer"
    

    Replace the following:

    • DATASET_ID: the ID of the dataset containing the table.
    • TABLE_ID: the ID of the table to profile.
    • service-PROJECT_NUMBER@gcp-sa-dataplex.iam.gserviceaccount.com: the email of the Knowledge Catalog service account.

Create a data profile scan

Console

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Click Create data profile scan.

  3. Optional: Enter a Display name.

  4. Enter an ID. See the Resource naming conventions.

  5. Optional: Enter a Description.

  6. In the Table field, click Browse. Choose the table to scan, and then click Select. Only standard BigQuery and Iceberg REST Catalog tables are supported.

    For tables in multi-region datasets, choose a region where to create the data scan.

    To browse the tables organized within Knowledge Catalog lakes, click Browse within Knowledge Catalog Lakes.

  7. In the Mode section, select one of the following options:

    • Standard: profiles your data with customizable scan settings. This is the default mode.

    • Lightweight: provides quick insights with a low-latency, low-fidelity scan.

  8. If you chose the Standard mode, configure the following options. These options don't appear when you select Lightweight mode.

    1. In the Scope field, choose Incremental or Entire data.

      If you choose Incremental data, in the Timestamp column field, select a column of type DATE or TIMESTAMP from your BigQuery table. Knowledge Catalog uses this column to identify new records as they're added. For tables partitioned on a column of type DATE or TIMESTAMP, it's recommended to use this column as the partition column.

    2. Optional: To filter your data, do any of the following:

      • To filter by rows, select the Filter rows checkbox. Enter a valid SQL expression that can be used in a WHERE clause in GoogleSQL syntax. For example: col1 >= 0.

        The filter can be a combination of SQL conditions over multiple columns. For example: col1 >= 0 AND col2 < 10.

      • To filter by columns, select the Filter columns checkbox.

      • To include columns in the profile scan, in the Include columns field, click Browse. Select the columns to include, and then click Select.

      • To exclude columns from the profile scan, in the Exclude columns field, click Browse. Select the columns to exclude, and then click Select.

      Note: You can use Include columns, Exclude columns, or both. If you use both the fields, then the data profile scan first selects the columns based on your input in the Include columns field and then excludes the columns based on your input in the Exclude columns field.
    3. To apply sampling to your data profile scan, in the Sampling size list, select a sampling percentage. Choose a percentage value that ranges between 0.0% and 100.0% with up to 3 decimal digits.

      • For larger datasets, choose a lower sampling percentage. For example, for a 1 PB table, if you enter a value between 0.1% and 1.0%, the data profile samples between 1-10 TB of data.

      • There must be at least 100 records in the sampled data to return a result.

      • For incremental data scans, the data profile scan applies sampling to the latest increment.

  9. Optional: Publish the data profile scan results in the BigQuery and Knowledge Catalog pages in the Google Cloud console for the source table. Select the Publish results to Knowledge Catalog checkbox.

    You can view the latest scan results in the Data profile tab in the BigQuery and Knowledge Catalog pages for the source table. To let users access the published scan results, see the Grant access to data profile scan results section of this document.

    The publishing option might not be available in the following cases:

    • You don't have the required permissions on the table.
    • Another data profile scan is set to publish results.
  10. In the Schedule section, choose one of the following options:

    • Repeat: Run the data profile scan on a schedule: hourly, daily, weekly, monthly, or custom. Specify how often the scan should run and at what time. If you choose custom, use cron format to specify the schedule.

    • On-demand: Run the data profile scan on demand.

    • One-time run: Run the data profile scan once now, and remove the scan after the auto-deletion time. This feature's in Preview.

      • Set post-scan results auto-deletion: The auto-deletion time defines the duration a data profile scan remains active after execution. A data profile scan without a specified auto-deletion time is automatically removed after 24 hours. The auto-deletion time can range from 0 seconds (immediate deletion) to 365 days.
  11. Click Continue.

  12. Optional: Export the scan results to a BigQuery standard table. In the Export scan results to BigQuery table section, do the following:

    1. In the Select BigQuery dataset field, click Browse. Select a BigQuery dataset to store the data profile scan results.

    2. In the BigQuery table field, specify the table to store the data profile scan results. If you're using an existing table, make sure that it's compatible with the export table schema. If the specified table doesn't exist, Knowledge Catalog creates it for you.

      Note: You can use the same results table for multiple data profile scans.
  13. Optional: Add labels. Labels are key-value pairs that let you group related objects together or with other Google Cloud resources.

  14. To create the scan, click Create.

    If you set the schedule to on-demand, you can also run the scan now by clicking Run scan.

gcloud

To create a data profile scan, use the gcloud dataplex datascans create data-profile command.

If the source data is organized in a Knowledge Catalog lake, include the --data-source-entity flag:

gcloud dataplex datascans create data-profile DATASCAN \
--location=LOCATION \
--data-source-entity=DATA_SOURCE_ENTITY

If the source data isn't organized in a Knowledge Catalog lake, include the --data-source-resource flag:

gcloud dataplex datascans create data-profile DATASCAN \
--location=LOCATION \
--data-source-resource=DATA_SOURCE_RESOURCE

Replace the following variables:

C#

C#

Before trying this sample, follow the C# setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery C# API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dataplex.V1;
using Google.LongRunning;

public sealed partial class GeneratedDataScanServiceClientSnippets
{
    /// <summary>Snippet for CreateDataScan</summary>
    /// <remarks>
    /// This snippet has been automatically generated and should be regarded as a code template only.
    /// It will require modifications to work:
    /// - It may require correct/in-range values for request initialization.
    /// - It may require specifying regional endpoints when creating the service client as shown in
    ///   https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint.
    /// </remarks>
    public void CreateDataScanRequestObject()
    {
        // Create client
        DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create();
        // Initialize request argument(s)
        CreateDataScanRequest request = new CreateDataScanRequest
        {
            ParentAsLocationName = LocationName.FromProjectLocation("[PROJECT]", "[LOCATION]"),
            DataScan = new DataScan(),
            DataScanId = "",
            ValidateOnly = false,
        };
        // Make the request
        Operation<DataScan, OperationMetadata> response = dataScanServiceClient.CreateDataScan(request);

        // Poll until the returned long-running operation is complete
        Operation<DataScan, OperationMetadata> completedResponse = response.PollUntilCompleted();
        // Retrieve the operation result
        DataScan result = completedResponse.Result;

        // Or get the name of the operation
        string operationName = response.Name;
        // This name can be stored, then the long-running operation retrieved later by name
        Operation<DataScan, OperationMetadata> retrievedResponse = dataScanServiceClient.PollOnceCreateDataScan(operationName);
        // Check if the retrieved long-running operation has completed
        if (retrievedResponse.IsCompleted)
        {
            // If it has completed, then access the result
            DataScan retrievedResult = retrievedResponse.Result;
        }
    }
}

Go

Go

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Go API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


//go:build examples

package main

import (
	"context"

	dataplex "cloud.google.com/go/dataplex/apiv1"
	dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb"
)

func main() {
	ctx := context.Background()
	// This snippet has been automatically generated and should be regarded as a code template only.
	// It will require modifications to work:
	// - It may require correct/in-range values for request initialization.
	// - It may require specifying regional endpoints when creating the service client as shown in:
	//   https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options
	c, err := dataplex.NewDataScanClient(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	defer c.Close()

	req := &dataplexpb.CreateDataScanRequest{
		// TODO: Fill request struct fields.
		// See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#CreateDataScanRequest.
	}
	op, err := c.CreateDataScan(ctx, req)
	if err != nil {
		// TODO: Handle error.
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	// TODO: Use resp.
	_ = resp
}

Java

Java

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.dataplex.v1.CreateDataScanRequest;
import com.google.cloud.dataplex.v1.DataScan;
import com.google.cloud.dataplex.v1.DataScanServiceClient;
import com.google.cloud.dataplex.v1.LocationName;

public class SyncCreateDataScan {

  public static void main(String[] args) throws Exception {
    syncCreateDataScan();
  }

  public static void syncCreateDataScan() throws Exception {
    // This snippet has been automatically generated and should be regarded as a code template only.
    // It will require modifications to work:
    // - It may require correct/in-range values for request initialization.
    // - It may require specifying regional endpoints when creating the service client as shown in
    // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
    try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) {
      CreateDataScanRequest request =
          CreateDataScanRequest.newBuilder()
              .setParent(LocationName.of("[PROJECT]", "[LOCATION]").toString())
              .setDataScan(DataScan.newBuilder().build())
              .setDataScanId("dataScanId1260787906")
              .setValidateOnly(true)
              .build();
      DataScan response = dataScanServiceClient.createDataScanAsync(request).get();
    }
  }
}

Python

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import google.api_core.exceptions
from google.cloud import dataplex_v1


def create_data_profile_scan_global(
    project_id: str,
    dataset_id: str,
    table_id: str,
    location: str,
) -> None:
    """Creates a Dataplex Data Profile Scan using global API endpoint routing.

    Args:
        project_id (str): Google Cloud project ID where the scan is created.
        dataset_id (str): Target BigQuery dataset ID.
        table_id (str): Target BigQuery table ID to scan.
        location (str): Google Cloud region where serverless compute runs.
    """
    client = dataplex_v1.DataScanServiceClient()

    parent = client.common_location_path(project=project_id, location=location)

    bigquery_table = (
        f"//bigquery.googleapis.com/projects/{project_id}"
        f"/datasets/{dataset_id}/tables/{table_id}"
    )

    data_profile_spec = dataplex_v1.DataProfileSpec(sampling_percent=100.0)

    data_scan = dataplex_v1.DataScan(
        display_name="Global Data Profile Scan",
        description="Regional data profile scan generating automated table statistics.",
        data=dataplex_v1.DataSource(resource=bigquery_table),
        data_profile_spec=data_profile_spec,
    )

    request = dataplex_v1.CreateDataScanRequest(
        parent=parent,
        data_scan=data_scan,
    )

    try:
        operation = client.create_data_scan(request=request)
        print(operation.result())

    except google.api_core.exceptions.AlreadyExists:
        print("A scan with this ID already exists.")
    except google.api_core.exceptions.InvalidArgument as e:
        print(f"Your scan configuration is invalid: {e}")
    except google.api_core.exceptions.GoogleAPIError as e:
        print(f"Unexpected exception: {e}")


Ruby

Ruby

Before trying this sample, follow the Ruby setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Ruby API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

require "google/cloud/dataplex/v1"

##
# Snippet for the create_data_scan call in the DataScanService service
#
# This snippet has been automatically generated and should be regarded as a code
# template only. It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in https://cloud.google.com/ruby/docs/reference.
#
# This is an auto-generated example demonstrating basic usage of
# Google::Cloud::Dataplex::V1::DataScanService::Client#create_data_scan.
#
def create_data_scan
  # Create a client object. The client can be reused for multiple calls.
  client = Google::Cloud::Dataplex::V1::DataScanService::Client.new

  # Create a request. To set request fields, pass in keyword arguments.
  request = Google::Cloud::Dataplex::V1::CreateDataScanRequest.new

  # Call the create_data_scan method.
  result = client.create_data_scan request

  # The returned object is of type Gapic::Operation. You can use it to
  # check the status of an operation, cancel it, or wait for results.
  # Here is how to wait for a response.
  result.wait_until_done! timeout: 60
  if result.response?
    p result.response
  else
    puts "No response received."
  end
end

REST

To create a data profile scan, use the dataScans.create method.

Note: If your BigQuery table is configured with the Require partition filter setting set to true, use the table's partition column as the data profile scan's row filter or timestamp column.

Create multiple data profile scans

You can configure data profile scans for multiple tables in a BigQuery dataset at the same time by using the Google Cloud console.

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Click Create data profile scan.

  3. Select the Multiple data profile scans option.

  4. Enter an ID prefix. Knowledge Catalog automatically generates scan IDs by using the provided prefix and unique suffixes.

  5. Enter a Description for all of the data profile scans.

  6. In the Dataset field, click Browse. Select a dataset to pick tables from. Click Select.

  7. If the dataset is multi-regional, select a Region in which to create the data profile scans.

  8. In the Mode section, choose one of the following options:

    • Standard: profiles your data with customizable scan settings. This is the default mode.

    • Lightweight: provides quick insights with a low-latency, low-fidelity scan. This feature is in Preview.

  9. If you chose the Standard mode, configure the following settings for the scans. These settings don't appear when Lightweight mode is selected.

    1. In the Scope field, choose Incremental or Entire data.

      If you choose Incremental data, you can select only tables that are partitioned on a column of type DATE or TIMESTAMP.

    2. To apply sampling to the data profile scans, in the Sampling size list, select a sampling percentage.

      Choose a percentage value between 0.0% and 100.0% with up to 3 decimal digits.

  10. Optional: Publish the data profile scan results in the BigQuery and Knowledge Catalog pages in the Google Cloud console for the source table. Select the Publish results to Knowledge Catalog checkbox.

    You can view the latest scan results in the Data profile tab in the BigQuery and Knowledge Catalog pages for the source table. To let users access the published scan results, see the Grant access to data profile scan results section of this document.

    Note: You must choose tables that don't have any existing scans publishing their results.
  11. In the Schedule section, choose one of the following options:

    • Repeat: Run the data profile scans on a schedule: hourly, daily, weekly, monthly, or custom. Specify how often the scans should run and at what time. If you choose custom, use cron format to specify the schedule.

    • On-demand: Run the data profile scans on demand.

      • One-time run: Run the data profile scan once now, and remove the scan after the auto-deletion time. This feature's in Preview.

        • Set post-scan results auto-deletion: The auto-deletion time defines the duration a data profile scan remains active after execution. A data profile scan without a specified auto-deletion time is automatically removed after 24 hours. The auto-deletion time can range from 0 seconds (immediate deletion) to 365 days.
  12. Click Continue.

  13. In the Choose tables field, click Browse. Choose one or more tables to scan, and then click Select.

  14. Click Continue.

  15. Optional: Export the scan results to a BigQuery standard table. In the Export scan results to BigQuery table section, do the following:

    1. In the Select BigQuery dataset field, click Browse. Select a BigQuery dataset to store the data profile scan results.

    2. In the BigQuery table field, specify the table to store the data profile scan results. If you're using an existing table, make sure that it's compatible with the export table schema. If the specified table doesn't exist, Knowledge Catalog creates it for you.

      Knowledge Catalog uses the same results table for all of the data profile scans.

  16. Optional: Add labels. Labels are key-value pairs that let you group related objects together or with other Google Cloud resources.

  17. To create the scans, click Create.

    If you set the schedule to on-demand, you can also run the scans now by clicking Run scan.

Run a data profile scan

Console

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Click the data profile scan to run.
  3. Click Run now.

gcloud

To run a data profile scan, use the gcloud dataplex datascans run command:

gcloud dataplex datascans run DATASCAN \
--location=LOCATION

Replace the following variables:

C#

C#

Before trying this sample, follow the C# setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery C# API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

using Google.Cloud.Dataplex.V1;

public sealed partial class GeneratedDataScanServiceClientSnippets
{
    /// <summary>Snippet for RunDataScan</summary>
    /// <remarks>
    /// This snippet has been automatically generated and should be regarded as a code template only.
    /// It will require modifications to work:
    /// - It may require correct/in-range values for request initialization.
    /// - It may require specifying regional endpoints when creating the service client as shown in
    ///   https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint.
    /// </remarks>
    public void RunDataScanRequestObject()
    {
        // Create client
        DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create();
        // Initialize request argument(s)
        RunDataScanRequest request = new RunDataScanRequest
        {
            DataScanName = DataScanName.FromProjectLocationDataScan("[PROJECT]", "[LOCATION]", "[DATASCAN]"),
        };
        // Make the request
        RunDataScanResponse response = dataScanServiceClient.RunDataScan(request);
    }
}

Go

Go

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Go API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


//go:build examples

package main

import (
	"context"

	dataplex "cloud.google.com/go/dataplex/apiv1"
	dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb"
)

func main() {
	ctx := context.Background()
	// This snippet has been automatically generated and should be regarded as a code template only.
	// It will require modifications to work:
	// - It may require correct/in-range values for request initialization.
	// - It may require specifying regional endpoints when creating the service client as shown in:
	//   https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options
	c, err := dataplex.NewDataScanClient(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	defer c.Close()

	req := &dataplexpb.RunDataScanRequest{
		// TODO: Fill request struct fields.
		// See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#RunDataScanRequest.
	}
	resp, err := c.RunDataScan(ctx, req)
	if err != nil {
		// TODO: Handle error.
	}
	// TODO: Use resp.
	_ = resp
}

Java

Java

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.dataplex.v1.DataScanName;
import com.google.cloud.dataplex.v1.DataScanServiceClient;
import com.google.cloud.dataplex.v1.RunDataScanRequest;
import com.google.cloud.dataplex.v1.RunDataScanResponse;

public class SyncRunDataScan {

  public static void main(String[] args) throws Exception {
    syncRunDataScan();
  }

  public static void syncRunDataScan() throws Exception {
    // This snippet has been automatically generated and should be regarded as a code template only.
    // It will require modifications to work:
    // - It may require correct/in-range values for request initialization.
    // - It may require specifying regional endpoints when creating the service client as shown in
    // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
    try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) {
      RunDataScanRequest request =
          RunDataScanRequest.newBuilder()
              .setName(DataScanName.of("[PROJECT]", "[LOCATION]", "[DATASCAN]").toString())
              .build();
      RunDataScanResponse response = dataScanServiceClient.runDataScan(request);
    }
  }
}

Python

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dataplex_v1


def sample_run_data_scan():
    # Create a client
    client = dataplex_v1.DataScanServiceClient()

    # Initialize request argument(s)
    request = dataplex_v1.RunDataScanRequest(
        name="name_value",
    )

    # Make the request
    response = client.run_data_scan(request=request)

    # Handle the response
    print(response)

Ruby

Ruby

Before trying this sample, follow the Ruby setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Ruby API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

require "google/cloud/dataplex/v1"

##
# Snippet for the run_data_scan call in the DataScanService service
#
# This snippet has been automatically generated and should be regarded as a code
# template only. It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in https://cloud.google.com/ruby/docs/reference.
#
# This is an auto-generated example demonstrating basic usage of
# Google::Cloud::Dataplex::V1::DataScanService::Client#run_data_scan.
#
def run_data_scan
  # Create a client object. The client can be reused for multiple calls.
  client = Google::Cloud::Dataplex::V1::DataScanService::Client.new

  # Create a request. To set request fields, pass in keyword arguments.
  request = Google::Cloud::Dataplex::V1::RunDataScanRequest.new

  # Call the run_data_scan method.
  result = client.run_data_scan request

  # The returned object is of type Google::Cloud::Dataplex::V1::RunDataScanResponse.
  p result
end

REST

To run a data profile scan, use the dataScans.run method.

Airflow

To run a data profile scan using a Directed Acyclic Graph (DAG) in Managed Service for Apache Airflow (Cloud Composer), use the DataplexRunDataProfileScanOperator:

from datetime import datetime
from airflow import DAG
from airflow.providers.google.cloud.operators.dataplex import DataplexRunDataProfileScanOperator

with DAG(
  "dataplex_data_profile_scan",
  start_date=datetime(2026, 1, 1),
  schedule_interval="@daily",
  catchup=False,
) as dag:

  run_profile_scan = DataplexRunDataProfileScanOperator(
      task_id="run_dataplex_profile_scan",
      project_id="PROJECT_ID",
      region="REGION",
      data_scan_id="DATASCAN_ID",
  )

Replace the following variables:

Note: Run isn't supported for data profile scans that are on a one-time schedule.

View data profile scan results

Console

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Click the name of a data profile scan.

    • The Overview section displays information about the most recent jobs, including when the scan was run, the number of table records scanned, and the job status.

    • The Data profile scan configuration section displays details about the scan.

  3. To see detailed information about a job, such as the scanned table's columns, statistics about the columns that were found in the scan, and the job logs, click the Jobs history tab. Then, click a job ID.

Note: If you exported the scan results to a BigQuery table, then you can also access the scan results from the table.

gcloud

To view the results of a data profile scan job, use the gcloud dataplex datascans jobs describe command:

gcloud dataplex datascans jobs describe JOB \
--location=LOCATION \
--datascan=DATASCAN \
--view=FULL

Replace the following variables:

C#

C#

Before trying this sample, follow the C# setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery C# API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

using Google.Cloud.Dataplex.V1;

public sealed partial class GeneratedDataScanServiceClientSnippets
{
    /// <summary>Snippet for GetDataScan</summary>
    /// <remarks>
    /// This snippet has been automatically generated and should be regarded as a code template only.
    /// It will require modifications to work:
    /// - It may require correct/in-range values for request initialization.
    /// - It may require specifying regional endpoints when creating the service client as shown in
    ///   https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint.
    /// </remarks>
    public void GetDataScanRequestObject()
    {
        // Create client
        DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create();
        // Initialize request argument(s)
        GetDataScanRequest request = new GetDataScanRequest
        {
            DataScanName = DataScanName.FromProjectLocationDataScan("[PROJECT]", "[LOCATION]", "[DATASCAN]"),
            View = GetDataScanRequest.Types.DataScanView.Unspecified,
        };
        // Make the request
        DataScan response = dataScanServiceClient.GetDataScan(request);
    }
}

Go

Go

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Go API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


//go:build examples

package main

import (
	"context"

	dataplex "cloud.google.com/go/dataplex/apiv1"
	dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb"
)

func main() {
	ctx := context.Background()
	// This snippet has been automatically generated and should be regarded as a code template only.
	// It will require modifications to work:
	// - It may require correct/in-range values for request initialization.
	// - It may require specifying regional endpoints when creating the service client as shown in:
	//   https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options
	c, err := dataplex.NewDataScanClient(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	defer c.Close()

	req := &dataplexpb.GetDataScanRequest{
		// TODO: Fill request struct fields.
		// See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#GetDataScanRequest.
	}
	resp, err := c.GetDataScan(ctx, req)
	if err != nil {
		// TODO: Handle error.
	}
	// TODO: Use resp.
	_ = resp
}

Java

Java

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.dataplex.v1.DataScan;
import com.google.cloud.dataplex.v1.DataScanName;
import com.google.cloud.dataplex.v1.DataScanServiceClient;
import com.google.cloud.dataplex.v1.GetDataScanRequest;

public class SyncGetDataScan {

  public static void main(String[] args) throws Exception {
    syncGetDataScan();
  }

  public static void syncGetDataScan() throws Exception {
    // This snippet has been automatically generated and should be regarded as a code template only.
    // It will require modifications to work:
    // - It may require correct/in-range values for request initialization.
    // - It may require specifying regional endpoints when creating the service client as shown in
    // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
    try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) {
      GetDataScanRequest request =
          GetDataScanRequest.newBuilder()
              .setName(DataScanName.of("[PROJECT]", "[LOCATION]", "[DATASCAN]").toString())
              .build();
      DataScan response = dataScanServiceClient.getDataScan(request);
    }
  }
}

Python

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dataplex_v1


def sample_get_data_scan():
    # Create a client
    client = dataplex_v1.DataScanServiceClient()

    # Initialize request argument(s)
    request = dataplex_v1.GetDataScanRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_data_scan(request=request)

    # Handle the response
    print(response)

Ruby

Ruby

Before trying this sample, follow the Ruby setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Ruby API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

require "google/cloud/dataplex/v1"

##
# Snippet for the get_data_scan call in the DataScanService service
#
# This snippet has been automatically generated and should be regarded as a code
# template only. It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in https://cloud.google.com/ruby/docs/reference.
#
# This is an auto-generated example demonstrating basic usage of
# Google::Cloud::Dataplex::V1::DataScanService::Client#get_data_scan.
#
def get_data_scan
  # Create a client object. The client can be reused for multiple calls.
  client = Google::Cloud::Dataplex::V1::DataScanService::Client.new

  # Create a request. To set request fields, pass in keyword arguments.
  request = Google::Cloud::Dataplex::V1::GetDataScanRequest.new

  # Call the get_data_scan method.
  result = client.get_data_scan request

  # The returned object is of type Google::Cloud::Dataplex::V1::DataScan.
  p result
end

REST

To view the results of a data profile scan, use the dataScans.get method.

View published results

If the data profile scan results are published to the BigQuery and Knowledge Catalog pages in the Google Cloud console, then you can see the latest scan results on the source table's Data profile tab.

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. In the left pane, click Explorer:

    Highlighted button for the Explorer pane. [Highlighted button for the Explorer pane.]

    If you don't see the left pane, click Expand left pane to open the pane.

  3. In the Explorer pane, click Datasets, and then click your dataset.

  4. Click Overview > Tables, and then select the table whose data profile scan results you want to see.

  5. Click the Data profile tab.

    The latest published results are displayed.

    Note: Published results might not be available if a scan is running for the first time.

View the most recent data profile scan job

Console

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Click the name of a data profile scan.

  3. Click the Latest job results tab.

    The Latest job results tab, when there's at least one successfully completed run, provides information about the most recent job. It lists the scanned table's columns and statistics about the columns that were found in the scan.

gcloud

To view the most recent successful data profile scan, use the gcloud dataplex datascans describe command:

gcloud dataplex datascans describe DATASCAN \
--location=LOCATION \
--view=FULL

Replace the following variables:

REST

To view the most recent scan job, use the dataScans.get method.

View historical scan results

Knowledge Catalog saves the data profile scan history of the last 300 jobs or for the past year, whichever occurs first.

Console

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Click the name of a data profile scan.

  3. Click the Jobs history tab.

    The Jobs history tab provides information about past jobs, such as the number of records scanned in each job, the job status, and the time the job was run.

  4. To view detailed information about a job, click any of the jobs in the Job ID column.

gcloud

To view historical data profile scan jobs, use the gcloud dataplex datascans jobs list command:

gcloud dataplex datascans jobs list \
--location=LOCATION \
--datascan=DATASCAN

Replace the following variables:

C#

C#

Before trying this sample, follow the C# setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery C# API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

using Google.Api.Gax;
using Google.Cloud.Dataplex.V1;
using System;

public sealed partial class GeneratedDataScanServiceClientSnippets
{
    /// <summary>Snippet for ListDataScanJobs</summary>
    /// <remarks>
    /// This snippet has been automatically generated and should be regarded as a code template only.
    /// It will require modifications to work:
    /// - It may require correct/in-range values for request initialization.
    /// - It may require specifying regional endpoints when creating the service client as shown in
    ///   https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint.
    /// </remarks>
    public void ListDataScanJobsRequestObject()
    {
        // Create client
        DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create();
        // Initialize request argument(s)
        ListDataScanJobsRequest request = new ListDataScanJobsRequest
        {
            ParentAsDataScanName = DataScanName.FromProjectLocationDataScan("[PROJECT]", "[LOCATION]", "[DATASCAN]"),
            Filter = "",
        };
        // Make the request
        PagedEnumerable<ListDataScanJobsResponse, DataScanJob> response = dataScanServiceClient.ListDataScanJobs(request);

        // Iterate over all response items, lazily performing RPCs as required
        foreach (DataScanJob item in response)
        {
            // Do something with each item
            Console.WriteLine(item);
        }

        // Or iterate over pages (of server-defined size), performing one RPC per page
        foreach (ListDataScanJobsResponse page in response.AsRawResponses())
        {
            // Do something with each page of items
            Console.WriteLine("A page of results:");
            foreach (DataScanJob item in page)
            {
                // Do something with each item
                Console.WriteLine(item);
            }
        }

        // Or retrieve a single page of known size (unless it's the final page), performing as many RPCs as required
        int pageSize = 10;
        Page<DataScanJob> singlePage = response.ReadPage(pageSize);
        // Do something with the page of items
        Console.WriteLine($"A page of {pageSize} results (unless it's the final page):");
        foreach (DataScanJob item in singlePage)
        {
            // Do something with each item
            Console.WriteLine(item);
        }
        // Store the pageToken, for when the next page is required.
        string nextPageToken = singlePage.NextPageToken;
    }
}

Go

Go

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Go API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


//go:build examples

package main

import (
	"context"

	dataplex "cloud.google.com/go/dataplex/apiv1"
	dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb"
	"google.golang.org/api/iterator"
)

func main() {
	ctx := context.Background()
	// This snippet has been automatically generated and should be regarded as a code template only.
	// It will require modifications to work:
	// - It may require correct/in-range values for request initialization.
	// - It may require specifying regional endpoints when creating the service client as shown in:
	//   https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options
	c, err := dataplex.NewDataScanClient(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	defer c.Close()

	req := &dataplexpb.ListDataScanJobsRequest{
		// TODO: Fill request struct fields.
		// See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#ListDataScanJobsRequest.
	}
	it := c.ListDataScanJobs(ctx, req)
	for {
		resp, err := it.Next()
		if err == iterator.Done {
			break
		}
		if err != nil {
			// TODO: Handle error.
		}
		// TODO: Use resp.
		_ = resp

		// If you need to access the underlying RPC response,
		// you can do so by casting the `Response` as below.
		// Otherwise, remove this line. Only populated after
		// first call to Next(). Not safe for concurrent access.
		_ = it.Response.(*dataplexpb.ListDataScanJobsResponse)
	}
}

Java

Java

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.dataplex.v1.DataScanJob;
import com.google.cloud.dataplex.v1.DataScanName;
import com.google.cloud.dataplex.v1.DataScanServiceClient;
import com.google.cloud.dataplex.v1.ListDataScanJobsRequest;

public class SyncListDataScanJobs {

  public static void main(String[] args) throws Exception {
    syncListDataScanJobs();
  }

  public static void syncListDataScanJobs() throws Exception {
    // This snippet has been automatically generated and should be regarded as a code template only.
    // It will require modifications to work:
    // - It may require correct/in-range values for request initialization.
    // - It may require specifying regional endpoints when creating the service client as shown in
    // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
    try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) {
      ListDataScanJobsRequest request =
          ListDataScanJobsRequest.newBuilder()
              .setParent(DataScanName.of("[PROJECT]", "[LOCATION]", "[DATASCAN]").toString())
              .setPageSize(883849137)
              .setPageToken("pageToken873572522")
              .setFilter("filter-1274492040")
              .build();
      for (DataScanJob element : dataScanServiceClient.listDataScanJobs(request).iterateAll()) {
        // doThingsWith(element);
      }
    }
  }
}

Python

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dataplex_v1


def sample_list_data_scan_jobs():
    # Create a client
    client = dataplex_v1.DataScanServiceClient()

    # Initialize request argument(s)
    request = dataplex_v1.ListDataScanJobsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_data_scan_jobs(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Ruby

Ruby

Before trying this sample, follow the Ruby setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Ruby API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

require "google/cloud/dataplex/v1"

##
# Snippet for the list_data_scan_jobs call in the DataScanService service
#
# This snippet has been automatically generated and should be regarded as a code
# template only. It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in https://cloud.google.com/ruby/docs/reference.
#
# This is an auto-generated example demonstrating basic usage of
# Google::Cloud::Dataplex::V1::DataScanService::Client#list_data_scan_jobs.
#
def list_data_scan_jobs
  # Create a client object. The client can be reused for multiple calls.
  client = Google::Cloud::Dataplex::V1::DataScanService::Client.new

  # Create a request. To set request fields, pass in keyword arguments.
  request = Google::Cloud::Dataplex::V1::ListDataScanJobsRequest.new

  # Call the list_data_scan_jobs method.
  result = client.list_data_scan_jobs request

  # The returned object is of type Gapic::PagedEnumerable. You can iterate
  # over elements, and API calls will be issued to fetch pages as needed.
  result.each do |item|
    # Each element is of type ::Google::Cloud::Dataplex::V1::DataScanJob.
    p item
  end
end

REST

To view historical data profile scan jobs, use the dataScans.jobs.list method.

Job statuses

Data scan jobs can have the following statuses:

Monitoring best practices

When you monitor your data scan jobs, keep the following best practices in mind:

View the data profile scans for a table

To view the data profile scans that apply to a specific table, do the following:

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Filter the list by table name and scan type.

Grant access to data profile scan results

To let users in your organization view the scan results, do the following:

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Click the data profile scan you want to share the results of.

  3. Click the Permissions tab.

  4. Do the following:

    • To grant access to a principal, click Grant access. Grant the Dataplex DataScan DataViewer role to the associated principal.
    • To remove access from a principal, select the principal that you want to remove the Dataplex DataScan DataViewer role from. Click Remove access, and then confirm when prompted.

Manage data profile scans for a specific table

The steps in this document show how to manage data profile scans across your project by using the BigQuery Metadata curation > Data profiling & quality page in the Google Cloud console.

You can also create and manage data profile scans when working with a specific table. In the Google Cloud console, on the BigQuery page for the table, use the Data profile tab. Do the following:

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

    In the Explorer pane, click Datasets, and then click your dataset. Click Overview > Tables, and select the table whose data profile scan results you want to see.

  2. Click the Data profile tab.

  3. Depending on whether the table has a data profile scan whose results are published, you can work with the table's data profile scans in the following ways:

    • Data profile scan results are published: the latest published scan results are displayed on the page.

      To manage the data profile scans for this table, click Data profile scan, and then select from the following options:

      • Create new scan: create a new data profile scan. For more information, see the Create a data profile scan section of this document. When you create a scan from a table's details page, the table is preselected.

      • Run now: run the scan.

      • Edit scan configuration: edit settings including the display name, filters, sampling size, and schedule.

      • Manage scan permissions: control who can access the scan results. For more information, see the Grant access to data profile scan results section of this document.

      • View historical results: view detailed information about previous data profile scan jobs. For more information, see the View data profile scan results and View historical scan results sections of this document.

      • View all scans: view a list of data profile scans that apply to this table.

    • Data profile scan results aren't published: click the menu next to Quick data profile, and then select from the following options:

      • Customize data profiling: create a new data profile scan. For more information, see the Create a data profile scan section of this document. When you create a scan from a table's details page, the table's preselected.

      • View previous profiles: view a list of data profile scans that apply to this table.

Update a data profile scan

Console

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Click the name of a data profile scan.

  3. Click Edit, and then edit the values.

  4. Click Save.

gcloud

To update a data profile scan, use the gcloud dataplex datascans update data-profile command:

gcloud dataplex datascans update data-profile DATASCAN \
--location=LOCATION \
--description=DESCRIPTION

Replace the following variables:

Note: You can update specification fields, such as rowFilter, samplingPercent, or includeFields, in the data profile specification file. See the JSON format.

C#

C#

Before trying this sample, follow the C# setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery C# API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

using Google.Cloud.Dataplex.V1;
using Google.LongRunning;
using Google.Protobuf.WellKnownTypes;

public sealed partial class GeneratedDataScanServiceClientSnippets
{
    /// <summary>Snippet for UpdateDataScan</summary>
    /// <remarks>
    /// This snippet has been automatically generated and should be regarded as a code template only.
    /// It will require modifications to work:
    /// - It may require correct/in-range values for request initialization.
    /// - It may require specifying regional endpoints when creating the service client as shown in
    ///   https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint.
    /// </remarks>
    public void UpdateDataScanRequestObject()
    {
        // Create client
        DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create();
        // Initialize request argument(s)
        UpdateDataScanRequest request = new UpdateDataScanRequest
        {
            DataScan = new DataScan(),
            UpdateMask = new FieldMask(),
            ValidateOnly = false,
        };
        // Make the request
        Operation<DataScan, OperationMetadata> response = dataScanServiceClient.UpdateDataScan(request);

        // Poll until the returned long-running operation is complete
        Operation<DataScan, OperationMetadata> completedResponse = response.PollUntilCompleted();
        // Retrieve the operation result
        DataScan result = completedResponse.Result;

        // Or get the name of the operation
        string operationName = response.Name;
        // This name can be stored, then the long-running operation retrieved later by name
        Operation<DataScan, OperationMetadata> retrievedResponse = dataScanServiceClient.PollOnceUpdateDataScan(operationName);
        // Check if the retrieved long-running operation has completed
        if (retrievedResponse.IsCompleted)
        {
            // If it has completed, then access the result
            DataScan retrievedResult = retrievedResponse.Result;
        }
    }
}

Go

Go

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Go API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


//go:build examples

package main

import (
	"context"

	dataplex "cloud.google.com/go/dataplex/apiv1"
	dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb"
)

func main() {
	ctx := context.Background()
	// This snippet has been automatically generated and should be regarded as a code template only.
	// It will require modifications to work:
	// - It may require correct/in-range values for request initialization.
	// - It may require specifying regional endpoints when creating the service client as shown in:
	//   https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options
	c, err := dataplex.NewDataScanClient(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	defer c.Close()

	req := &dataplexpb.UpdateDataScanRequest{
		// TODO: Fill request struct fields.
		// See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#UpdateDataScanRequest.
	}
	op, err := c.UpdateDataScan(ctx, req)
	if err != nil {
		// TODO: Handle error.
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	// TODO: Use resp.
	_ = resp
}

Java

Java

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.dataplex.v1.DataScan;
import com.google.cloud.dataplex.v1.DataScanServiceClient;
import com.google.cloud.dataplex.v1.UpdateDataScanRequest;
import com.google.protobuf.FieldMask;

public class SyncUpdateDataScan {

  public static void main(String[] args) throws Exception {
    syncUpdateDataScan();
  }

  public static void syncUpdateDataScan() throws Exception {
    // This snippet has been automatically generated and should be regarded as a code template only.
    // It will require modifications to work:
    // - It may require correct/in-range values for request initialization.
    // - It may require specifying regional endpoints when creating the service client as shown in
    // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
    try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) {
      UpdateDataScanRequest request =
          UpdateDataScanRequest.newBuilder()
              .setDataScan(DataScan.newBuilder().build())
              .setUpdateMask(FieldMask.newBuilder().build())
              .setValidateOnly(true)
              .build();
      DataScan response = dataScanServiceClient.updateDataScanAsync(request).get();
    }
  }
}

Python

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dataplex_v1


def sample_update_data_scan():
    # Create a client
    client = dataplex_v1.DataScanServiceClient()

    # Initialize request argument(s)
    data_scan = dataplex_v1.DataScan()
    data_scan.data.entity = "entity_value"

    request = dataplex_v1.UpdateDataScanRequest(
        data_scan=data_scan,
    )

    # Make the request
    operation = client.update_data_scan(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Ruby

Ruby

Before trying this sample, follow the Ruby setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Ruby API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

require "google/cloud/dataplex/v1"

##
# Snippet for the update_data_scan call in the DataScanService service
#
# This snippet has been automatically generated and should be regarded as a code
# template only. It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in https://cloud.google.com/ruby/docs/reference.
#
# This is an auto-generated example demonstrating basic usage of
# Google::Cloud::Dataplex::V1::DataScanService::Client#update_data_scan.
#
def update_data_scan
  # Create a client object. The client can be reused for multiple calls.
  client = Google::Cloud::Dataplex::V1::DataScanService::Client.new

  # Create a request. To set request fields, pass in keyword arguments.
  request = Google::Cloud::Dataplex::V1::UpdateDataScanRequest.new

  # Call the update_data_scan method.
  result = client.update_data_scan request

  # The returned object is of type Gapic::Operation. You can use it to
  # check the status of an operation, cancel it, or wait for results.
  # Here is how to wait for a response.
  result.wait_until_done! timeout: 60
  if result.response?
    p result.response
  else
    puts "No response received."
  end
end

REST

To edit a data profile scan, use the dataScans.patch method.

Note: Update isn't supported for data profile scans that are on a one-time schedule.

Delete a data profile scan

Console

  1. In the Google Cloud console, on the BigQuery Metadata curation page, go to the Data profiling & quality tab.

    Go to Data profiling & quality

  2. Click the scan you want to delete.

  3. Click Delete, and then confirm when prompted.

gcloud

To delete a data profile scan, use the gcloud dataplex datascans delete command:

gcloud dataplex datascans delete DATASCAN \
--location=LOCATION --async

Replace the following variables:

REST

To delete a data profile scan, use the dataScans.delete method.

Note: Delete isn't supported for data profile scans that are on a one-time schedule.

What's next

Send feedback

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2026-08-12 UTC.

Need to tell us more? [[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-08-12 UTC."],[],[]]

Web Proxy Viewer  |  New URL  |  Original Page